Automated Visual Inspection: A Survey - IEEE Xplore

IEE1E T'RANSACTIONS ON PATTE1RN ANALYSIS ANI) MACHINE INTELLI(;ENCE, VOL. PAMI-4, NO. 6, NOVEMBE:R 1982

Automated Visual Inspection: A ROLAND T. CHIN, MEM13ER, 111EE, ANI) CHARLES A. HARLOW,

Abstract-This paper surveys publications, reports, and articles dealing with automated visual inspection for industry. The references are organized according to their contents: overview and discussions, rationales, components and design considerations, commercially available systems, applications. A number of applications and their inspection methodologies are discussed in detail: the inspection of printed circuit boards, photomasks, integrated circuit chips. Other inspection applications are listed as a bibliography. A list of selectively annotated references in commercially available visual inspection tools is also included. Index Terns-Automated visual inspection, defects detection, image processing, industrial automation, pattern recognition, quality assurance.

r

557

Survey

MEIMBER,

IEEE:

PROCBSO

SCANNE R tsJ

A

A

COMPONE NTS

A

A

CGOQO ~~~~~~~~~~~~I 'A

A

A

TRANSPORT SORTER

o

A

Fig. 1. Simple pictorial diagram of an automatic visual inspection system.

I. INTROI)UCTION IN industrial manufacturing, product inspection is an important step in the production process. Because product reliability is of utmost importance in most mass-production facilities, 100 percent inspection of all parts, subassemblies, and finished products is often being attempted. As a result, the inspection process is normially the largest single cost in

manufacturing. The most difficult task for inspection is that of inspecting for visual appearance. Visual inspection seeks to identify both functional and cosmetic defects. The visual inspection in most manufacturing processes depends mainly on humiian inspectors whose performance is generally inadequate and variable. The human visual system is adapted to perform in a world of variety and change; the visual inspection process, on the other hand, requires observing the same type of imiiage repeatedly to detect anomalies [49]. Some studies [51]-[54], [58] show that the accuracy of human visual inspection declines with dull, endlessly routine jobs. Slow, expensive, erratic inspection is the result. Automated visual inspection is obviously the alternative to the human inspector. A number of studies have addressed problems in advanced automation of mianufacturing especially in automation research needs [591, [65]. A significant number of conferences and workshops have been held, and a nunmber of studies hiave been reported [61]-[63], on issues involved in identifying and improving productivity. These studies justify the need for industrial automation and show the general acceptance among manufacturers that automated systems will increase productivity and improve product quality [60], [64]. Manuscript received April 2, 1982. R. T. (Chin is with the Department ot Ellectrical and (Computer lngineering, University of Wisconsin, Madison, WI 53706. C. A. Hlarlow is with the Department of Flectrical I'ngineering, College of Frngineering, Louisiana State University, Baton Rouge, LA 70803.

On the automation of visual inspection, potential advantages have been justified [50], [55] -[57] One obvious advantage is the elimination of human labor, which is increasingly expensive. Human inspectors are slow compared to modern production rates, and they make many errors. Other advantages of automatic operation are speed and diagnostic capabilities. Several practical reasons for automated inspection include:

freeing humiians from the dull and routine; saving humlan labor costs; performing inspection in unfavorable environments; reducing demand for highly skilled human inspectors; analyzing statistics on test information and keeping records for nmanagement decisions; and * matching high-speed production with high-speed inspection. * * * * *

Advances in technology have resulted in better, cheaper image-analysis equipment. These advances-combined withl those in computer technology, pattern recognition, image processing, and artificial intelligence--permit inmage-analysis systems in production environments. Fig. 1 shows a simple pictorial diagram of an automated visual inspection system. This installation can replace and improve present quality control operations involving manual sampling or visual measurements. The image scanner and the processor replace the hunman inspectors in an ordinary production-line inspection system. The transport moves the objects to be inspected into the scanning station. The scanner collects visual data describing the object and sends them to the processor to be analyzed. After analysis, decisions are made, and the processor directs the sorter to reject the defective item. Obviously, this is a greatly sinmplified description of an automated visual inspection system.

0 162-8828/82/1 100-0557500.75 © 1982 IEEE

558

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. PAMI-4, NO. 6, NOVEMBER 1982

II. A SURVEY OF AUTOMATED VISUAL INSPECTION In recent years, a growing number of studies have discussed the merits of various approaches to machine recognition of images. Although this technology can be variously applied, only optical character recognition has fully reached commercial reality. Two other primary applications are the processing of satellite images and medical images. Automated visual inspection for manufacturing is on the threshold of broad commercial use. The electronics industry is investigating a wide variety of computerized visual inspection methods because significant technological advances in design and production of electronic assemblies have increased the speed of production while greatly reducing physical size. Such production advances have greatly complicated present inspection processes. Other industries-automobile, lumber, bottling, textiles, and metal processing-use similar procedures to attempt the automation of visual inspection. This paper attempts to survey the current state-of-the-art of automated visual inspection and to provide some fresh insights and up-todate information for those interested in this new technology. Examples quoted here are by no means exhaustive and have been chosen because of their general interest and availability of information. The body of literature generated from this newly developed field is both vast and scattered. There are a number of survey articles and bibliographies published recently on automated visual inspection studies of various industrial parts [1 ] - [14]. A significant number of papers have been written [15] -[48] to provide general information and basic concepts for newcomers in this technology. A number of studies have been reported [66] -[961 on issues involved in industrial vision system design considerations and requirements as well as the role of software, hardware, optics, and imaging devices. Other studies include the development of prototype systems for certain applications and the demonstration of inspection methodologies. The following sections will further discuss these applications. All cited publications have been grouped into categories. The grouping of the reported studies is designed to make it easier for the reader to locate the references dealing with a particular application of topic. The following lists the categories: 0

0

0

0

0

0

0

0

the inspection of printed circuit patterns the inspection of microcircuit photomasks chip inspection and alignment for bonding the inspection of other electrical and electronic assemblies the inspection of automobile parts visual inspection for metal processing industry visual inspection of fabric visual inspection by radiographic imaging.

III.

COMMERCIALLY AVAILABLE GAUGING EQUIPMENT AND VISUAL INSPECTION SYSTEMS

Despite significant advances in automation manufacturing visual product inspection has not generally been part of this automation trend. One reason for the slow progress visual inspection is that it is difficult to design off-the-shelf visual inspection systems for broadbased industrial applications. Many factors must be considered in designing such

processes,

systems: hardware, software, system throughput, versatility, and reliability. Versatility is essential to designing general purpose inspection tools. Versatility refers to the number of different inspections the system can perform. For example, on a circuit board one might want to check tolerance on paths, possible missing components, improper solder connectors, and the condition of metal surfaces. If at all achievable, the number of different inspections desired can be quite high. Often one also desires to change the type of product being inspected; for example, from circuit board to metal surface inspections. For applications where the inspection sequences are simple, the number of points to be inspected is limited, and the inspection requires only simple GO/NO-GO indication, some simple off-the-shelf electrooptical gauging equipment are available. A typical setup usually consists of an imaging camera, a monitor, and a control/process console. Such a system is able to take linear measurements by recognizing the object's high-contrast boundaries. This is usually done by simple edge detection logic circuitries, measuring controls and digital readout displays. In most cases, the control unit of such equipment is composed of hardwired, plug-in units. Programming is carried out by electronic units, push buttons, or simple instructions to address peripheral units and to control the process. System responses are usually limited to display measurements. In recent years, microcomputers are used to perform the controlling functions. It performs simple data analysis to obtain necessary parameters such as dimension, size, and object position. System responses, usually based on these parameters, signal the operator for appropriate action. References [97]-[1 19] describe a number of the commercially available electrooptical gauging equipment and their applications. A brief annotation is added to each cited reference to briefly describe its contents and to identify the manufacturer. A number of off-the-shelf inspection equipment manufacturers that are not associated with the cited references are listed in Section E of the Reference listing. The latest addition to the off-the-shelf visual inspection system field is several general-purpose software programmable systems [102], [103], [108]. They are software-intensive, with programs for calibration, programs for scanning, programs for analyzing images, and programs for decision making. The capital cost of these general-purpose, off-the-shelf visual inspection systems are relatively high, but they can be justified by their inspection utilization rate. Their versatility and the ease with which their inspection roles can be extended makes them economical for many industrial applications. The operation of a general-purpose inspection system typically consists of two phases-the training phase and the inspection phase. In the training process, a human operator uses a model part to teach the system the features to be examined and their acceptable tolerances. Interaction between the system and the operator is required and is usually carried out by some interactive devices such as keyboard with joystick and a video monitor. The training results, table of tolerances, and the inspection sequences are required to be stored in easily accessible devices. Then the operation is virtually automatic after the training procedure. During inspection, the system guides the scanning of the part to be

CHIN AND HARLOW: AUTOMATED VISUAL INSPECTION

559

inspected, measures the desired features, and compares the measurements with tolerance data previously stored. The results of the inspection phase is a determination of whether the part is acceptable. IV. VISUAL INSPECTION APPLICATIONS Very few industrial firms have experimented with the development of computer visual systems for their own production lines. For simple optical gauging problems, off-theshelf visual inspection tools are usually used. But in some cases, this simple gauging equipment cannot be used to solve complicated inspection problems. In these circumstances, a specialized inspection system has to be justified. After the carefully planned visual inspection system shows considerable promise, both technically and economically, the development of such a unique system is carried out in-plant from conception to implementation. Such installation then becomes an integrated part of the production process that performed visual inspection on one type of products. A number of laboratories, research institutions and universities have also studied automated visual inspection problems. These studies are feasiblity demonstrations. Some of these research projects are concerned with providing pragmatic solutions to current problems in industrial inspection. The primary result of the research is to develop prototype systems that show the adequacy of picture processing techniques and the availability of technology needs for practical inspection systems. Others are concerned with the technological progress needed for the future and the impact such technologies will have on the economy, environment, and future trends in automated inspection. A Product System Productivity Research [65] sponsored by National Science Foundation in cooperation with General Electric, the C. S. Draper Laboratory, Inc., and the M.I.T. Center for Policy Alternatives has been conducted to analyze national productivity problems and to organize research programs. It has shown many problems are common to a number of industries, and these problems are not likely to be resolved by industry alone. They require the knowledge and resources of universities, research institutions, industry and government to be brought together in partnership for cooperative pursuit of more productive industry. The study formulated an estimate of typical technology trends for the next ten years and for most needed research projects. In the area of inspection, research and development on advanced visual systems and real-time systems are recommended. The study also points out that research on a few representative visual systems would serve the needs of many industries, and the technological transfer of the results would benefit the designing and building of practical advanced visual inspection tools. Both production-line inspection systems and prototype research systems are surveyed in the following sections. These unique, one-of-a-kind systems are grouped into categories according to their applications. Their unique inspection methodologies and unique features are discussed.

Fig. 2. A PCB with defects. Master

Image

Test

mage

Fig. 3. Image subtraction process involves a simple Exclusive-Or operation between the master image and the image of the part to be

inspected.

under-etching, and other cosmetic defects. (Fig. 2 shows a defective PCB). Traditionally, visual inspection is performed by highly trained workers to detect large flaws, to align the board and to control tolerance. It is the small scale flaws, often with dimensions below 2 mils, that require tedious inspection and for these defects that an automated system would be most valuable. All PCB inspection algorithms, although seemingly diverse, utilize a priori knowledge. This a priori knowledge provides strategies and standards for the inspection process. The stratification of the various inspection model depends on this knowledge of the PCB such as the geometric structure and dimensions as well as the various engineering tolerance standards. Image Subtraction: Image subtraction is the most obvious approach to the visual inspection of PCB. In this approach, the PCB to be inspected is scanned and its image is compared against some representation of a perfect PCB image. The subtracted image, showing defects, can subsequently be displayed and analyzed. Fig. 3 shows the subtraction process. Peterson [123] has experimented a variation of the subtraction technique by using two cameras scanning the PCB to be inspected and the coded model in synchronism. The model A. The Inspection of Printed Circuit Patterns that is used as the ideal standard circuit is stored in analog Printed circuit boards (PCB's), the heart of all electronic form as a coded gray-scale image and not in digital form so devices, are inspected extensively to isolate errors such as inspection is performed at high speed. Arlan et al. [140], shorts, opens, missing holes, incorrect markings, over-etching, [144] have developed the automatic in-process microcircuit

560

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND

evaluation (AIME) system to perform 100 percent inspection of thick film circuits in real time. The image subtraction method is implemented in a video processor by comparing video frames with that from a perfect image stored on a video disk. Lee [134] has used the image subtraction method for the inspection of printed thick film pattems on ceramic substrate. He also extended the method to include a searching scheme through the subtracted image for potential defects. Olsen [1261 approached the image subtraction method somewhat differently. He initially prepared a mask with a light blocking pattern corresponding to the desired PCB. By illuminating the PCB through the mask with the light of a color complimentary to the color of the PCB and with the mask in registry with the PCB, defects are readily apparent as bright spots on a dark surface. A few other PCB inspection systems have also been developed and reported [127], [120]. They use the direct subtraction method in which defects appear as differences found through comparison. This method suffers from several problems: large reference data storage; precise alignment; and sensitivity to illumination and scanner conditions. One other drawback of image subtraction is the fact that many and perhaps most acceptable PCB do not match point-by-point identically because of shrinking or swelling of the board. In this situation, the use of correction schemes to compensate for such problems is necessary and may result in systems which become impractical. Feature Matching: Feature matching is an extended approach of the image subtraction method. Typically, the PCB to be inspected is scanned and the required features are extracted. Then, instead of comparing the PCB and the perfect pattern with each other directly, the features extracted from the PCB and those defined for the perfect pattern are compared: if they are the same, the PCB is satisfactory. In image processing terminologies, this subsequent processing is called local feature extraction and template matching. This strategy greatly compresses the image data for storage and at the same time reduces the sensitivity of the input intensity data. Fig. 4 shows the extracted features-edges-of a PCB and the templates of four possible configurations of a nick with a width of one pixel point along the extracted edges. Jarvis [142] has devised a two-stage strategy for the inspection of PCB. The method consists of the preselection of a list of local 5 X 5 binary patterns which describe the normal conductor-substrate boundary derived from a perfect PCB. In the first stage, each border pattern from the PCB being inspected is matched with patterns from the prepared list. Those patterns not found in this list are subjected to the second stage process which performs various tests to determine if the pattern indicates a flaw. These supplemental tests include the computation of conductor area, the length between boundaries, the ratio of area to length, and others. It was found in the reported example that 99 percent of the possible normal 5 X 5 patterns could be enumerated by preparing less than a few hundred binary templates and only a few percent of the patterns were subjected to the second stage process. It should be noted that this method eliminates the need for precise alignment of the test board but requires careful generation of the binary templates.

MAqHINE

INTELLIGENCE, VOL. PAMI-4, NO. 6, NOVEMBER 1982

[ I 1[1 1-1

1

[

1

1[1 IN]

Fig. 4. Extracted edges of a PCB and the four possible binary templates of nicks along the edges.

Pavlidis and Krakaner [145] have modified this technique of binary template matching to include a rapid table look-up procedure and a rapid paging algorithm for sequential processing. Also incorporated into the procedure are the geometric properties and the dimension of conductors. This information reduces the required number of templates. Chin and Harlow [137], [138] used an operator-interactive model-building graph procedure to train the PCB inspection system. Interaction is used when necessary to extract features, reduced noise, define the graph model, modify feature models, and encode models in compact data structures. Fig. 5 shows a simple example of the interactive training process. In this step, the operator uses a joystick and a display to select windows and to remove unwanted regions. In the later inspection phase, instead of searching for similar features through the PCB under test, the inspection routine accesses the stored models for expected feature types and feature locations and then directs the scanner to look for the expected features in the test image. If the feature is missing or measurably different, the graph model indicates an error. Unlike most of the other feature comparison methods, this procedure does not require the scanning/processing of the entire PCB image. Ejiri et al. [122] have developed three types of local processing to find defects on color TV receiver circuit boards. Defects are defined as "small portions" having a width less than the PCB conductor pattern. One of the methods, the expansioncontraction method, which is somewhat unique, does not involve any perfect master image (Fig. 6). Starting with an input test pattern, areas identified as conductor are first enlarged uniformly in all directions. This expanded pattern is then contracted by the same amount to eliminate small portions within the substrate. Finally, the pattern derived from the expansion-contraction procedure is compared with the original input test pattern, and the small defects are extracted. Another feature matching experiment was implemented as digital hardware by Baxter and Shipway [131]. In this


561

PCB INSPECTION BY DIMENSIONAL VERIFICATION

BX, ........

~~~~~~~~~~~~.... N

.

............................. ..........

~~~~~~~~.....

....... .

Fig. 7. The basic idea of dimensional verification: measurement A is acceptable; measurments B and C are rejected.

Fig.

5.

An example of the interactive training process.

input

pattern

expansion

contraction controction

expansion

Fig. 6. Principle of the expansion-contraction method to detect small defects.

method, features from successive areas of a high-resolution PCB image are compared with features from corresponding areas of a low-resolution master pattern. Takaki et al. [136] also use local feature extraction for detecting small defects on the boundary of circuit patterns. Using the assumption that normal patterns have regular geometric features, local features not matching the defined features are judged to be defective. Dimensional Verification: In many applications, the feature extraction and template matching method is simplified to dimensional verification. The task is to make a determination for each measurement as to whether it falls within the previously established standards. Criteria of this method might include lengths and widths of conductors, hole and pad diameters, and distances between adjacent conductors. The measurements are obtained by measuring the distance between two conductor-substrate boundaries. In other words, the

distance between edges is the primary feature of this inspection method. Fig. 7 shows the simple idea. Thissen [121], [132] has developed a system for the automatic inspection of connecting-lead patterns on tape using the center-line method. In this method the model with which the inspected pattern is compared-four models for the conductor pattern consists of the center-line of all the tracks and insulation strips of the pattern. Each of these center-lines is assigned a test criterion that indicates the minimum required width of the track. During the inspection, the PCB is aligned and a determination for each center-line point is made as to whether there is sufficient clearance around it. Nakashima et al. [146] have developed a feature extraction method which uses a laser diffraction technique to inspect PCB photomasks. The location and direction of the pattern edge are obtained by transmitting a laser beam through the PCB mask and focusing the diffracted beam onto a spatially divided photodetector. Once the locations and directions of pattern edges are known, pattern widths are measured and compared to predefined standards. Bentley [139], [143] has developed the Inspectron for the inspection of circuit patterns on the individual layers of a multilayer PCB. The concept of this instrument is centered around a special detector array configuration. The detector array, contains a total of 38 detectors, so arranged that it can detect flaws by minimum conductor width and minimum conductor spacing criteria. The detector signals are processed by high-speed logic circuitry and the inspection procedure does not require alignment. Sterling [141] has devised a PCB inspection scheme that utilizes run length image encoding. Such encoding determines the positions of the edges of the conductor on each scan and provides convenient means of linking the information on a scan line to the previous scan lines. After the encoding, the inspection process involves the tracking of regions from scan line to scan line, the extraction of topological features and the detection of anomalies by imposing localized constraints such as minimum or maximum width. This approach eliminates the need for precise alignment and enables the process to be implemented in hardware. The proposed system uses a CCD array and video preprocessor. Restrick [133] has shown the feasibility of using linear solid state arrays to provide high resolution for the inspection of PCB. The inspection scheme is also based on minimum

562


a

b

c

(a)

d

text in an input text stream. Initially, a set of common PCB defects is expressed in Regular Expressions. Later in the inspection, conductor boundaries are extracted from the PCB image and expressed in Regular Expressions via chain coding. Specified defects are then detected by searching the text stream for the defective pattern expressions.

B. The Inspection of Microcircuit Photomasks One of the major sources of yield loss in the manufacture of integrated circuits (IC's) is defects in the photomasks. The function of the photomask is to transfer the desired pattern to the wafer for the semiconductor lithographic process. To achieve high percentage yield in the fabrication of the circuits, the photomask must contain high fidelity imagery. The most common photomask inspection technique used to this date is manual inspection. The well-trained operator looks at each and every portion of the mask through a microabadadadabca scope looking for defective points such as registration errors, (b) dimension variations, and random visual defects. The sensitivFig. 8. An illustration of image description by using a string grammar to track the boundary. (a) Primitives of a PCB grammar. (b) Boundary ity to these defects increases both as the circuit elements of a PCB pattern and its string representation. decrease in size and as the die size grows. As integrated circuits get smaller and the number of gates per die gets higher, line width and line clearance criteria. Cheng [124], [135] the manual inspection becomes less reliable and expensive. developed a system using laser scanner as an inspection device Automated inspection of photomasks can in principle provide by measuring the conductor width. The detector signals, in 100 percent inspection and perform the inspection with a forms of waveforms, are analyzed by hardware error recogni- high reliability and efficiency. A study was conducted by Ciarlo et al. [156] to determine tion unit. Syntactic Approach: Another approach of this inspection the requirements for automated IC photomask inspection application is the syntactic analysis and recognition. The systems. From this study it was concluded that a viable syntactic approach to inspection offers a possibility for automated visual inspection system should be able to detect describing a large set of complex objects using small sets of random defects that are 2 ,m or larger in dimensions, and simple pattern primitive and structural rules. Most images do for pattern registration and dimensional verification should be not consist of character strings; however, certain pictorial measured to a few tenths of a micrometer accuracy. patterns such as PCB images can be made to fit the string Differential Scanning: This method scans and compares model. Local features can be decomposed into a relatively adjacent chip patterns of the photomask. This conceptually small number of unique elements called primitives, such as simple approach uses the fact that IC masks contain a periodic corners or lines. A structural description of the primitives and array of nominally identical chip images, so that defects can the relationships between themselves can be determined, be detected by comparing a neighboring image. A simplified forming a string grammar. Fig. 8 illustrates an example. Given system is shown in Fig. 9. This method of inspection does a set of primitives describing common defects, one can use an not detect repeated defects and one must rely on additional automatic procedure to locate them by searching the string inspection to ensure that there are no repeated defects. Optidescribing the PCB under test for all occurrences of the cal and mechanical alignment is also a difficult requirement to defective pattern primitives. meet when using this method. Various syntactic techniques for shape analysis and boundary An automatic mask inspection system, the AMIS, has been tracing have been discussed by Pavlidis [147]. Some of these in routine operation for measuring the defect count on various techniques have been applied to the problem of PCB inspec- types of IC masks at Bell Laboratories, Murray Hill, NJ, since tion. In one experiment [128], [129], a PCB image was 1975 [151], [155]. A differential laser scanning system scans encoded into finite alphabets. The procedure involves a two adjacent and identical patterns and a processor compares boundary tracer which produces ordered lists of boundary their outputs; any deviations found are defects. The system is points, a polygonal approximation program which attains comprised of a flying spot laser with two acousto-optically dedata compaction and noise removal, and a syntactic shape flected laser beams, photodetectors to measure the light analyzer which produces descriptions of the shape. The detec- transmitted through the mask, a mechanical XY-scan table tion of defects then involves the detection of local defective driven by stepping motors, a signal processor to measure the features expressed in finite expressions. analog difference between outputs of the two photodetectors Jarvis [125] has developed an algorithm to recognize fea- and to threshold the signal for recognizing defects, a minitures in line drawings using Regular Expression Language. A computer to automate operation sequences and to classify Regular Expression is a specification for finite state automaton defects, and TV monitors to display both the actual image and which recognizes the occurrence of the specified pattern of the defect map. The scanning and defect detection reportedly

CHIN AND HARLOW: AUTOMATED VISUAL INSPECTION Scanner X

Scanner Y

Detectors

Fig. 9. Schematic of a differential scanning system.

take about three minutes for a 50 X 50 mm mask area with a detection limit down to 2 gm [ 162] . Another differential laser scanning system has been developed at RCA Laboratories, Princeton, NJ [166] . Its system components, operational procedures, and detection method are very similar to the system described above. In 1976, KLA Instruments began development of a fully automatic photomask inspection system [ 163], [171], [174] . The KLA-100 system has recently been indroduced to the semiconductor industry. The optical information which comes from two adjacent dice is first converted to digital data. These data are then stored and processed for alignment corrections. Random defects are detected by simple comparison. Color Separation: Ito [ 160] utilized color information in an inspection system for IC mask patterns. Three masks are illuminated side by side and the transmitted light is filtered to give complementary colors. The three colored images are then superimposed precisely. Assuming that defects could rarely occur at the same place in the three masks, the locations of defects in each of the three mask patterns can be detected by identifying the superimposed color. Tanabe of Hitachi Ltd. [158] has also used similar color separation scheme to inspect IC masks. Both of these systems require precise optical alignment. Furthermore, since the masks are separated widely in space, the mechanical registration requirement is another major problem. Feature Matching: Feature extraction and template matching have been used to detect defective portions of the IC mask. It involves the extraction of features and the comparison with a set of predefined criteria or known templates. The funda-

563

mental concepts of approach used in this application are very similar to those used in PCB inspection. The following describes examples using this approach. A microdensitometer produced by Perkin-Elmer has been described [157] for the automatic inspection of mask registration and critical dimensions. In this simple case, extracted dimensions from the optical images on the mask are compared to criteria representing a perfect mask. Numerous studies have been conducted by Nyyssonen [168] to inspect mask pattern by line-width measurements. The accuracy of such measurement is dependent upon the location accuracy of the line or feature edge. A microdensitometer system designed and built for the accurate measurement of line widths less than 10 ,um has been described [164]. Other similar studies reported recently include Kryger [177] and Figler [178]. Bourdelais et al. [152] have constructed an apparatus using a microscope, a vidicon camera, a video processor, and logic decision circuitry to automatically inspect IC masks for pinholes and spots. The feature used in this system is the relative number of pinholes and spots with respect to the overall mask. This feature measurement provides an indication of the relative degree of mask perfection, and that this is the number which determines whether the mask is good or bad. Hara et al. [179] described a defect detection method by extracting local features from the two die patterns and comparing them. The local features include width, corner, curvature, orientation, and density. Implementation of the method in hard-wired circuits has been realized and such systems are being used in Hitachi's production lines. A unique feature extraction method for the inspection of mask containing a few-micrometer patterns has been proposed by Goto et al. [172]. It utilizes a scanning electron microscope to image the mask pattern and the contour direction counting method to detect defects. This method counts the number of contour lines of each direction within a window, and the number determines whether there is a defective pattern in the window or not. This method has the advantages that defects can be extracted without any stored reference and precise alignment requirements. In an experiment using an actual mask pattern, defects of 0.5 ,um in size reportedly have been extracted from a pattern with line widths of a few micrometers. A hierarchical inspection algorithm, which utilizes local features, has been proposed and tested for IC mask inspection at Toshiba Research and Development Center, Japan [173]. Local features are defined by 3 X 3 digital patterns, from which normal local features and defective features are selected. Using multilevels of inspection such as inspection in horizontal and vertical directions; inspection in diagonal directions; and measuring widths and gaps, the system can inspect larger areas with less information in parallel with small defect detection with full information. The IC mask inspection system was hardware-implemented and was reported to have a detection capability of 1 ,um and an inspection time of less than 1 h for 4 in mask. Diffraction and Spatial Filtering: This approach to defect detection is based upon the diffraction of light by the IC photomask itself. It can be shown that if a photomask placed

564

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. PAMI-4, NO. 6, NOVEMBER 1982 Photomask

Defect Map

Spatial

Transform Lens

Fi ter

q

q

..........I............ ......................... ....................... Fig. 10. Spatial filtering for photomask inspection.

............

at the front focal plane of a lens is illuminated by a collimated laser beam, then the light on the back focal plane of the lens is proportional to the Fourier transform of that photomask pattern. A second lens may be used to produce a diffraction pattern of the Fourier transform and thereby reconstructing the original photomask image. The Fourier transform of the periodic die arrays of the photomask consists of a two-dimensional array of bright spots. The spacing of these spots depends only on the spacing of the photomask, and the amplitude of these spots depends upon the individual device pattern. If any defects are present on the mask, a second transformed pattern will appear which is not confined to the array of bright spots generated by the perfect mask. The simplest way of using these properties to inspect photomask is to design a spatial filter which is placed at the Fourier transform plane to block out the diffraction pattern produced by the perfect-mask array, leaving only the transformed pattern of defects. Fig. 10 shows the approach. This method is noncomparative, thereby eliminating the need for a reference pattern, but it has several drawbacks [175]. It is both difficult and time-consuming to make adequate spatial filters, and in addition, there are problems with respect to focusing and aligning the filter with the transformed image. Also, this technique is very sensitive to surface and thickness irregularities in the photomask causing unwanted interference effects. A number of inspection studies has been performed using this spatial filtering technique [148]-[150], [153], [159], [165], [167], [170], [180]. A Fourier-optics photomask inspection system, which has a defect resolution of 10 pm and a field of view of 2 in in diameter, was developed at RCA Laboratories, Princeton, NJ [165] . Watkins [148] -[150] extended the simple filtering technique by using two apertures to select two portions from the illuminated periodic mask surface and a grating at the spatial frequency plane to filter the diffraction pattern. By choosing the correct aperture separation, the diffraction patterns from two mask portions are subtracted from each other, resulting in an image consisting only of random defects. Almi and Shamir [167] simplified the method by comparing patterns of one-dimensional optical Fourier transforms. This

q ...........

method avoids the inconvenient process of producing a spatial filter for each photomask. An inspection system based on the boundary diffraction wave theory is now in use at Western Electric, Allentown, PA [153]. This system, developed at Bell Laboratories is operated on the principle that the regular smooth edge of a valid pattern will diffract light differently than will the irregular edge of a defect. It uses a single focused laser spot scanning and a special photodetector array. The pattern of light diffracted from edges is detected by the photodetector and analyzed to distinguish between typical defects and valid mask features. Minami et al. [159], [170] have developed the directional filtering scheme to inspect photomask. In this method, the directional filter masking the particular angular components, such as 00, 450, 900, and 1350, is used as the spatial filter in the optical system. The filtered output shows the pattern element distribution, which contains directional components other than those particular directions. Random defects such as pinholes, intrusions, and extrusions are not of particular direction and therefore displayed in the output plane. C Chip Inspection and Alignment for Bonding In semiconductor chip assembly, each die is being visually inspected followed by the bonding of the die onto the package substrate and the bonding of wires from the die pads to the physically larger package leads. The process involves the detection of defects, the recognition of the chip boundary, the determination of the chip position and orientation, and the recognition of bonding pads. Conventionally, human operators have to perform all of these functions. Until recently, there exists a few automatic die-bonding and wire-bonding machines either at experimental stage or operating in production lines. Kashioka et al. [182] have developed a multiple-station transistor wire-bonding system using a multiple local template matching method for finding the transistor's position and orientation. A series of 12 X 12 binary templates describing corners of the chip are defined initially by an operator using an interactive facility. In the matching process, the system


searches for local areas that best match the templates. The matching process is carried out in a sequential manner. The search terminates when a pair of templates match successfully with two local areas and the distance and angle requirements between them are met. Then coordinates of the bonding pads are calculated and the wire bonding is performed. The system, now operating in the assembly line, can support up to 50 bonding machines in a timesharing mode and has a bonding accuracy of 99 percent and an average recognition speed of 0.2 s per chip. Baird [186]-[189] has developed a system to inspect and align IC chips. The system, installed in 1977 on the assembly line at General Motors, is inspecting the IC chips used in GM's ignition systems and aligning them for test probing. The basic idea is very similar to [182]. The approximate orientation of the chip is first determined by identifying the highest relative frequency of occurrence of all the directions of the edge points. At the same time, all possible corners of the chip are located by small local template matching. Then the actual four corners are determined by applying a global relational mask at the known orientation. Finally, the chip's structural integrity is inspected by examining the contrast about the chip's boundaries. After that, the system directs the alignment of test probes to desired locations for functional tests. Mese et al. [185] described an automatic position recognition technique for LSI assembly. A threshold is determined by successive approximations and is used to generate a binary image. Then, a set of four templates are used in parallel to roughly locate the four corners of a bonding pad. Finally, a set of small boundary templates and a direction finding process are employed to do minor adjustments. In another IC wire-bonding system, Kawata and Hirata [191] used two TV cameras to view two different portions of a chip and determine the position and orientation of the chips from the positions of the bonding pads detected in the two images. Coordinates of all the pads are then calculated. Igarashi et al. [190] used a photodiode linear sensor to determine IC bonding pads by fitting a line to the centers of the detected pads while the chip is scanned mechanically. Inoue et al. [183], [184] have developed a projection method for locating chips in an automated die-bonding system. Projection data is a one-dimensional distribution obtained by counting horizontally or vertically the number of one's in a binary picture. The position of the chip is located by analyzing the projection data distributions. Defective and misoriented chips are also detected. Die-bonding systems using this method are now working in the production line for transistors and IC's. Horn [181] proposed to use only four diagonal lines that intersect the chip boundaries for the determination of the IC position. Hsieh and Fu [192] proposed an integrated system for alignment, inspection, and wire-bonding of IC chips. The first stage determines the orientation of the chip by locating the mask frame. The second stage inspects the chip. The third stage locates the actual position of two pads residing at opposite corners and calculates the locations of all other pads. The proposed approach uses only sparse scanning to

565

achieve high data compression and suggests the possibility of parallel processing. D. Inspection of Other Electrical and Electronic Assemblies Miniaturized solid state components and other electrical devices are increasingly complex and beyond human inspection capability. A variety of inspection tasks must be automated to relieve the human inspector in tedious search for defects. Quite a few studies report visual inspection of electronic assemblies and electrical devices. The following shows the diversity of applications. A prototype system to inspect a water-resistant wire connector has been developed by Jarvis [2011 . The visual inspection requirement is to determine the correct location of the metal contact and the presence of an adequate amount of the sealant compound. Jarvis [2021 also developed a sealed contact switch inspection system to detect shape asymmetries and cracks in the region of the glass-metal seals. Van Daele et al. [205], [206] described another system for performing an extensive inspection on devices of this kind. The inspection is based on the cylindrical symmetry and reflective surfaces of the switch. A system of programs has been implemented by Chien and Snyder [199] to inspect several hybrid circuit components. These components include resistors, capacitors and flexible wires and the inspection is done by the procedural model approach. Mountjoy [200] has developed an inspection for detecting missing components on PCB's. The inspection employs a selective point comparison technique. Schroeder and Hines [198] used the same idea as [200] in the component verification system to inspect hybrid microcircuits. Wirick and Diede [191] have developed a prototype system to demonstrate the applicability of thermal scanning for the inspection of hybrid circuits. The method is based on the fact that most circuit components dissipate power in a normal heat pattern and deviations from the normal temperature profile are therefore indicative of a faulty circuit. Pau [197] has derived algorithms for IC inspection by analyzing the visible and infrared imagery during electrical testing. In other words, infrared thermography analysis, visual inspection, and electrical testing are performed simultaneously. Automatic assembly and inspection of incandescent light bulb filaments has been studied by Lin and Chan [193]. The objective is to automate threading of the filament and to inspect for defects after assembly. To determine the locations of the filament supporting wire tips in the three-dimensional space, the method analyzes two or more two-dimensional images taken at different angles. Klein and Breeding [204] have investigated the visual inspection of bubble memory overlay patterns. In their method, portions of the overlay patterns are characterized by vertical and horizontal histograms which are then compared to histograms extracted from the corresponding areas of a perfect device.

E. Inspection for Other Industries-A Bibliography Automated visual inspection to improve productivity are needed in many nonelectronic industrial applications. Often, the necessary installations or proposed systems are complex

566


and of high technical content. They have been realized with intense research and development. Many of these reported research studies are worth mentioning, but it is almost impossible to discuss all of them in detail. Publication on nonelectronic industrial inspection have been grouped into categories. The grouping of the publications is designed to make it easier for the reader to locate the reference dealing with a particular application of topic. No effort has been made to include non-English references, and no attempt is made to critically evaluate these listed references. The purpose of this section is primarily to provide a bibliography of nonelectronic applications, arranged according to subject matter. The vast variety of applications are grouped into the following groups. Visual Inspection of Automobile Parts[207]-[21S]: Applications include the inspection of brake cylinder, hydraulics cylinder, catalytic-converter substrate for emissions control, engine cylinder bore, camshaft, automobile frames, and a variety of other automobile parts. Visual Inspection in Metal Processing Industry [216] -[226]: Brook et al. [221 ] from the Sira Institute have studied extensively problems concerning defect detection on strip product surfaces. Systems resulting from these studies are now being used in European metal processing industry. Also, Mundy et al. [226] from General Electric Company have worked on metal surface inspection using the scattering theory of light for computer modeling of the metal surface. Visual Inspection of Fabric [227] -[235]: Fabric inspection and quality evaluation has a significant role in the overall fabric production process. Research on this problem is being done in the United States, Japan, Britain and West Germany. Koshimizu [231 ] briefly surveyed the fabric inspection activities and listed a few automated fabric inspection systems. Visual Inspection by Radiographic Imaging /236]-[246]: Radiographic analysis is one of the major methods of machine part inspection. The principle application is for the detection of flaws within castings of other machined parts. Radiography has also been used for inspection in continuous industrial processes. -Applications include the automated inspection of electronic parts, fuel pin, battery, artillery shell, turbine engine, and many others. Other Applications [24 7/ -[288]: Other applications include the inspection for the glass and ceramic industry, the inspection for the food and packaging industry, the inspection of military equipment, the inspection of railroad tracks, the inspection for the paper manufacturing, and the inspection of pharmaceutical products. Related topics that are largely or entirely omitted from this survey are: 1) robotics involving visual feedback for part manipulation, recognition of objects in a bin of industrial parts, and related problems concerning industrial robots; 2) visual inspection applications and research activities in private industries that do not appear in publications; 3) the integration of ultrasonic inspection with the manufacturing of products; and 4) topics dealing with related subjects but too far removed from the main subject. Of course, a survey in an active field of research is incomplete; however, omissions are unintentional and do not reflect any judgment on the authors.

Mention should also be made here that information concerning practical applications of computer vision to industrial inspection could be found in publications of the Society of Photo-Optical Instrumentation Engineers (SPIE) and the Society of Manufacturing Engineers (SME). There is also a wealth of information available in the proceedings of conferences such as the International Symposiums of Industrial Robots, International Joint Conferences on Artificial Intelligence, International Joint Conferences on Pattern Recognition, and the IEEE Computer Society Conferences on Pattern Recognition and Image Processing. Information on fundamental techniques is available in a number of IEEE journals. They include the IEEE COMPUTER, PROCEEDINGS OF THE IEEE, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACH INE INTELLIGENCE, TRANSACTIONS ON SYSTEMS,

MAN, AND CYBERNETICS, and TRANSACTIONS ON COMPUTERS. Also, there have been papers published in other journals including the Computer Graphics and Image Processing and Pattem Recognition. REFERENCES A. Overview: Related Surveys and Bibliographies A number of survey articles and related bibliographies on automated visual inspection studies of various industrial parts are listed. A few review references on image processing and pattern recognition techniques are also included. [11 J. E. Harry, Industrial Lasers and Their Applications. London: McGraw-Hill, 1974, pp. 145-147. [2] D. W. Franz, "CCTV systems aid accurate inspection," Functional Photography, pp. 30-34, Sept. 1975. [31 D. E. Hegland, "Closed-circuit TV peeks into manufacturing," Automation, pp. 74-77, June 1976. [4] M. E. Merchant, Survey of Current Status and Current Research and Development of Computer Automation of Manufacturing, June 1976. [5] J. F. Jarvis, "On automating visual inspection," in Proc. 7th EIA Annu. Symp. Automat. Imagery Pattern Recognition, College Park, MD, Apr. 1977, pp. 138-146. [6] C. J. D. M. Verhagen, "General survey of image processing applications, past and future," in Proc. SPIE, A utomat. Inspection Appl. Image Processing Techniques, vol. 130, Sira, London, Sept. 1977, pp. 8-17. [7] G. J. VanderBurg, "Image pattern recognition applications in industrial inspection," in Proc. 8th Annu. Automat. Imagery Pattern Recognition Talk, Gaithersburg, MD, Apr. 1978. [8] S. Tsuji, "Survey on vision systems for advanced automation in Japan," in Proc. SPIE, Imaging Applications for Automated Industrial Inspection & Assembly, vol. 182, Washington, DC, Apr. 1979, pp. 2-5. [9] R. A. Brook, "Development of techniques for automated industrial inspection in the United Kingdom in the age of microprocessors," in Proc. SPIE, Imaging Applications for A utomated Industrial Inspection & Assembly, vol. 182, Washington, DC, Apr. 1979, pp. 79-82. [10] W. E. Reed, Nondestructive Testing: X-Ray Photography and Radiography. Springfield, VA: Nat. Tech. Inform. Service, Dec. 1979. [11] D. H. Janney and R. P. Kruger, "Digital image analysis applied to industrial non-destructive evaluation and automated parts assembly," Int. Advances in Nondestructive Testing, vol. 6, pp. 39-93, 1979. [12] M. Yachida and S. Tsuji, "Industrial computer vision in Japan," Computer, pp. 50-63, May 1980. [13] W. Myers, "Industry begins to use visual pattern recognition," Computer, pp. 21-31, May 1980. [14] J. F. Jarvis, "Visual inspection automation," Computer, pp. 32-38, May 1980. [15] A. Rosenfeld, "Progress in picture processing: 1969-71," Comput. Surveys, no. 5, pp. 81-108, 1973.


[16] -, "Picture Processing, vol. [17] , "Picture Processing, vol.

[18]

[191] [20] [21] [22]

[231] [24]

[25] [26]

[27] [28]

processing: 1972," Comput. Graphics Image 1, pp. 394-416, 1972. processing: 1973," Comput. Graphics Image 3, pp. 178-194, 1974. , "Picture processing: 1974," Comput. Graphics Image Processing, vol. 4, pp. 133-155, 1975. , "Picture processing: 1975," Comput. Graphics Image Processing, vol. 5, pp. 215-237, 1976. , "Picture processing: 1976," Comput. Graphics Image Processing, vol. 6, pp. 157-18 3, 1977. , "Picture processing: 1977," Comput. Graphics Image Processing, vol. 7, pp. 211-242, 1978. , "Picture processing: 1978," Comput. Graphics Image Processing, vol. 9, pp. 354-393, 1979. , "Picture processing: 1979," Comput. Graphics Image Processing, vol. 13, pp. 46-79, 1980. L. Kanal, "Patterns in pattern recognition: 1968-1974," IEEE Trans. Inform. Theory, vol. IT-20, pp. 697-722, Nov. 1974. K. S. Fu and A. Rosenfeld, "Pattern recognition and image processing," IEEE Trans. Comput., vol. C-25, pp. 1336-1346, Dec. 1976. A. K. Agarwala, Ed., Machine Recognition of Patterns. New York: IEEE Press, 1977. J. K. Aggarwal, R. 0. Duda, and A. Rosenfeld, Eds., Computer Methods in Image Analysis. New York: IEEE Press, 1977. J. R. Ullmann, "A review of optical pattern recognition techniques," in Pattern Recognition Ideas in Practice, B. G. Batchelor, Ed. New York: Plenum, 1978, pp. 17-38.

B. General Discussions This section attempts to provide references with basic concepts and newcomers in this technology. Discussions on future directions of industrial visual automation are included. [29] J. E. Sparks, "Television that nobody watches," Machine Design, Feb. 1972. [30] G. R. Southward, "The TV camera as a computer input," EE/Syst. Eng. Today, July 1973. [31] N. Jensen, "Practical jobs for optical computers," Machine Design, pp. 94-100, Feb. 1973. [32] "Controls that learn to make their own decision," Business Week, Apr. 6, 1974. [33] J. Tucker, "Optical scanners ... Inspectors with 20/20 vision," Quality, pp. 10-11, Aug. 1976. [34] C. A. Harlow, S. J. Dwyer, III, D. Mountjoy, and W. McFarland, "Visual inspection techniques employing computers," Electron. Packaging and Production, pp. 111-113, Sept. 1976. [35] D. Nitzan and C. A. Rosen, "Programmable industrial automation," IEEE Trans. Comput., vol. C-25, pp. 1259-1270, Dec. 1976. [36] J. R. Parks, "Image processing-A new tool for quality control," in Proc. SPIE, Automat. Inspection Applications of Image Processing Techniques, vol. 130, Sira, London, Sept. 1977, pp. 2-7. [37] M. F. A. Fadl, "A look at inspection equipment of the future," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 2-3. [38] S. Tsuji, "Future directions of industrial applications," in Proc. 4th Int. Joint Conf Pattern Recognition, Kyoto, Japan, Nov. 1978, pp. 1144-1145. [39] C. A. Harlow, "Image analysis and graphs," Comput. Graphics Image Processing, voL 2, pp. 60-82, 1973. [40] M. L. Baird, "Future directions of industrial applications of pattern recognition," in Proc. 4th Int. Joint Conf. Pattern Recognition, Kyoto, Japan, Nov. 1978, p. 1146. [41] T. Uno, "Future directions of industrial applications," in Proc. 4th Int. Joint Conf on Pattern Recognition, Kyoto, Japan, Nov. 1978, p. 1147. [42] J. A. Weaver, "Some thoughts on future directions of industrial applications," in Proc. 4th Int. Joint Conf: Pattern Recognition, Kyoto, Japan, Nov. 1978, pp. 1148-1149. [43] R. P. Kruger, M. Yachida, R. A. Brook, and G. Agin, "Panel discussion on industrial visual automation," in Proc. SPIE, Imaging Applications for Automated Industrial Inspection and Assembly, vol. 182, Washington, DC, pp. 150-155, Apr. 1979. [44] W. B. Thompson, Guest Ed., "Machine perception for industrial applications," Computer, May 1980. [45] E. L. Hall, "Computer image processing and recognition," in

general information for

567

Proc. SPIE, Optics in Metrology and Quality Assurance, vol. 220, Feb. 1980. [46] R. Stewart, "Autoplace's opto-sense vision: Applications in industry," in Proc. SPIE, Optics in Metrology and Quality Assurance, vol. 220, Feb. 1980. [47] R. A. Morris, "Image processing applications in nondestructive examination," in Proc. SPIE, Optics in Metrology and Quality Assurance, vol. 220, Feb. 1980. [48] M. T. Jackson, "On-line optical gaging," in Proc. SPIE, Optics in Metrology and Quality Assurance, vol. 220, Feb. 1980. C. Rationales for Automating Visual Inspection A significant number of studies have addressed issues involving the need for advanced automation of manufacturing. The reported studies listed in this section show the general acceptance among manufacturers that automated systems will increase productivity and improve product quality. In addition, a number of studies examine the fact that the accuracy of human visual inspection declines with dull, endlessly routine jobs. These studies justify the potential advantages of automated visual inspection. [49] D. H. Harris, "The nature of industrial inspection," Human Factors, vol. 11, no. 2, pp. 139-148, 1969. [50] H. Berger and K. J. Reimann, "An overview-Advantages of imaging techniques for nondestructive testing," in Proc. SPIE, Imaging Techniques for Testing and Inspection, vol. 29, Los Angeles, CA, Feb. 1972. [51] R. H. Day, "Visual spatial illusions: A general explanation," Science, vol. 175, pp. 1335-1340, Mar. 1972. [52] C. R. R. Snyder, "Selection, inspection, and naming in visual search," J. Experimental Psychol., vol. 92, no. 3, pp. 428431, 1972. [53] S. Coren and J. S. Girgus, "Visual spatial illusions: Many explanations," Science, vol. 179, pp. 503-504, Feb. 1973. [54] J. W. Schoonard and J. D. Gould, "Field of view and target uncertainty in visual search and inspection," Human Factors, Feb. 1973. [55] R. C. McMaster, "Potentials of automated nondestructive examination," in Proc., Automat. Inspection and Product Contr. Conf., Oct. 1974, pp. 319-332, Oct. 1974. [56] R. L. Davies, "The computer can't replace the inspector," in Proc. Automat. Inspection and Product Contr. Conf., Oct. 1974, pp. 223-228. [57] J. Anderson, "When do you need an automatic gage?," in Proc. Automat. Inspection and Product Cont. Conf., Oct. 1974, pp. 125-130. [58] S. C. Wang, "Human reliability in visual inspection," Quality,

Sept. 1974. [59] S. J. Dwyer, III and T. J. Williams, Eds., "Research needs of the automation field," Automat. Res. Council, Rep. 8, Aug. 1977.

[60] J. R. Parks, "Intelligent machines-Commercial potential," Radio Electron. Eng., vol. 47, no. 8/9, pp. 355-367, 1977. [61] G. Boothroyd and C. Ho, "Performance and economics of programmable assembly systems," presented at the SME Assemblex IV Conf. and Exposition, AD77-720, Detroit, Ml, Nov. 1977.

[62] R. Allan, "Electronics to boost productivity," IEEE Spectrum, pp. 45-48, Jan. 1978. [63] D. Christiansen, Ed., "Special issue on productivity," IEEE Spectrum, Oct. 1978. [64] T. Nicholson, R. Thomas, and W. D. Marbach, "Blue-collar robots," Newsweek, Apr. 23, 1979. [65] Product System Productivity Research, NSF Grants APR7516221, and APR75-15334, sponsored by Nat. Sci. Foundation in cooperation with General Electric, the C. S. Draper Laboratory, and the M.I.T. Cen. for Policy Alternatives. D. System Components and Design Considerations

Publications on design considerations and requirements of industrial vision systems are listed in this section. They discuss the role of software and hardware in automated inspection as well as optics and imaging devices. [66] D. H. Jirauch, "Software design techniques for automatic checkout," presented at the IEEE Automat. Support Syst. Symp., Nov. 1966. [67] J. J. Amodei, "Optics in automated inspection," in Proc

568

[68]

[69]

[70]

[71] [72] [73]

[74] [75]

[76] [77]

[78] [79]

[80]

[81] [82] [83]

[84]

[85] [86] [87]

[88]

[89] [90] [91]

[92]

[93]


SPIE, Solving Quality Contr. and Reliability Problems with Optics, vol. 60, San Diego, CA, May 1975, pp. 3-5. G. P. Weckler, "Image detection for industrial gaging and inspection," in Proc. SPIE, Solving Quality Contr. and Reliability Problems with Optics, vol. 60, San Diego, CA, May 1975, pp. 14-19. T. S. Huang, "Electronic processing in flaw detection," in Proc, SPIE, Solving Quality Contr. and Reliability Problems with Optics, vol. 60, San Diego, CA, May 1975, pp. 20-21. R. T. Chien and W. E. Synder, "Hardware for visual image processing," IEEE Trans. Circuits Syst., vol. CAS-22, pp. 541551, June 1975. C. A. Harlow, S. E. Henderson, D. A. Rayfield, R. J. Johnston, and S. J. Dwyer, III, "Automated inspection of electronic assemblies," Computer, pp. 36-45, Apr. 1975. W. D. McFarland, "An integrated digital image processing system (IDIPS)," Ph.D. dissertation, College Eng., Univ. Missouri, Columbia, 1973. G. R. Southworth, "High speed video inspection techniques," presented at the Electro-Optics Syst. Design Conf., Nov. 1975. L. R. Baker and P. West, "The role of optics in inspection," Opt. Spectra, pp. 30-31, July 1975. T. 0. Binford, "Vision systems for industrial applications," presented at the Workshop on Advanced Automation, MD, Oct. 1976. R. Dahlberg, "Digital image processor links TV signal sources to computer," Comput. Design, Oct. 1977. P. Mengers, "A digital boost for low-light-level TV," Opt. Spectra, Aug. 1977. P. Mengers and K. A. Wickersheim, "High-speed digital image processing," Res. Develop., pp. 42-52, Oct. 1977. R. R. A. Morton, "Use of software for pattern classification," in Proc. SPIE, Automat. Inspection Applications of Image Processing Techniques, vol. 130, Sira, London, Sept. 1977, pp. 61-65. A. Pugh and K. Waddon, "The prospects for sensory arrays and microprocessing computers in manufactuiing industry," Radio Electron. Eng., vol. 47, pp. 377-384, Aug./Sept. 1977. C. A. Rosen and D. Nitzan, "Use of sensors in programmable automation," Computer, pp. 12-23, Dec. 1977. U. Rembold, M. K. Seth, and J. S. Weinstein, Computers in Manufacturing. New York: Marcel Dekker, 1977. J. R. Parks, "Industrial sensory devices," in Pattern Recognition Ideas in Practices, B. G. Batchelor, Ed. New York: Plenum, 1978. R. Nevatia, "Characterization and requirements of computer vision systems," in Computer Vision Systems, A. R. Hanson and E. M. Riseman, Eds. New York: Academic, 1978, pp. 81-88. A. Puch, K. Waddon, and W. B. Heginbotham, "Orientation and inspection of component parts," in Proc. SPIE, Industrial Applications of Solid-State Image Scanners, vol. 145, Sira, London, pp. 66-76, 1978. D. J. Purll, "Survey of present state of the art in applications of solid-state image scanners," in Proc. SPIE, Industrial Applications of Solid-State Image Scanners, vol. 145, 1978, pp. 9-17. G. M. Clarke and J. Bedford, "Laser scanning for defects, dimensions and surface finish," in Proc. 3rd Int. Confi Automat. Inspection and Product Contr., Nottingham, England, Apr. 1977, pp. 119-124. B. G. Batchelor, "SUSIE: A prototyping system for automatic visual inspection," in Proc. 4th Int. Conf Automat. Inspection and Product Contr., Chicago, IL, Nov. 1978, p. 49-80. H. R. Sellner, "Using microprocessors for image processing," in Proc. SPIE, Image Understanding Syst. and Industrial Applications, vol. 155, Aug. 1978, pp. 241-247. J. Meyn, "Digitized video processing in realtime," Comput. Design, Dec. 1978. H. Asada, M. Tabata, M. Kidode, and S. Watanabe, "New image processing hardwares and their applications to industrial automation," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Washington, DC, Apr. 1979, pp. 14-19. P. H. Smith, B. J. McCartin, and D. L. Davies, "Software concepts useful for industrial image processing," in Proc. IEEE Coput. Soc. Conf- Pattern Recognition and Image Processing, Chicago, IL, Aug. 1979, pp. 653-659. D. T. Lee, "Considerations in the design specifications of an

automatic system," in Proc. SPIE, Opt. in Metrology and Quality Assurance, vol. 220, Feb. 1980. [94] P. West, "Precision optical gauging with image scanning camera and programmable microprocessor controller," in Proc. SPIE, Minicomput. and Microprocessors in Opt. Syst., vol. 230, Apr.

1980.

[95] G. B. Porter, III and J. L. Mundy, "Visual inspection system design," Computer, pp. 40-48, May 1980. [96] G. J. Agin, "Computer vision systems for industrial inspection and assembly," Computer, pp. 11-20, May 1980. E. Commercially Available Gauging Equipment and Visual Inspection Systems This section lists a number of commercially available visual inspection tools. There are basically three types of available equipment: simple electro-optical gauging tools, general purpose programmable image analysis systems, and custom-design systems for unique applications. A brief annotation is added to each cited reference to briefly describe its contents and to identify the manufacturer. For those papers whose titles are sufficiently suggestive of their contents, the descriptions are omitted. A number of off-the-shelf inspection equipment manufacturers that are not associated with the cited publications are also included in this list. They are arranged alphabetically.

[97] R. E. Bible, "A high-precision production-oriented gage utilizing a solid state image sensor and microprocessor," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 24-27. Automat. Eng. San Diego, CA: A semiautomatic gauge used to measure size and location

of features of

a

diesel

engine fuel

injector nozzle is described. [98] "How to spot bad fasteners," Business Week, Nov. 16, 1974. Automat. Syst. Inc., Brookfield, CT: Manufacturer of laser inspection and process control systems; applications presented include the inspection of nuts, the inspection of bearings, and

[99] [100]

[101]

[102]

[103]

[104]

[105]

[106]

the detection of surface flaws. M. G. Dreyfus, "Space age

production by automatic image alignment," Manufacturing Eng. Management, pp. 28-31, Mar. 1971. , "Automatic image alignment and measurement," in Proc. Electro-Opt. Syst. Design Conf., Anaheim, CA, Nov. 1975, pp. 791-793. BAI Corp., Stamford, CT: The Metri-Eye System, a noncontact measurement instrument and industrial alignment system, is described. N. Wood, "Zooming in on IC's," Quality Management Eng., Feb. 1975. Circon Corp., Santa Barbara, CA: Manufacturer of interchangeable industrial visual gauging subsystems for simple dimension measurement by recognizing the part's edges. B. J. Tucker, "The comp-gage: A computerized electro-optical gage," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 90-93. A software programmable optical comparator-the CompGage System- for industrial dimensional inspection is described. Manufacturer: EMR Photoelectric, Princeton, NJ. J. Wilder, "Applications of a flexible pattern recognition system in industrial inspection," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 94-101. EMR Photoelectric: A flexible software programmable pattern recognition system for industrial applications such as recognizing codes and labels on parts is described. -, "A pattern recognition system for reading stamped characters," in Proc. 8th EIA Annu. Automat. Imagery Pattern Recognition Symp., Apr. 1978, pp. 91-101. L. A. Branaman, "Recent applications of electronic vision to noncontact automatic inspection," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 102-107. General Electric Co., Syracuse, NY: The optomation instrument system is described. It inspects parts by measuring extracted features and comparing those measurements with predetermined criteria. "Business notes," Laser Weekly, vol. 13, p. 5, July 30, 1979. Intec Corp., Norwalk, CT: Manufacturer of laser inspection


[107]

[1081]

[109]

[110]

[111]

[112]

[113]

systems for detecting flaws in materials in the form of continuously moving webs. R. H. Searle, "Computerized inspection using advanced optical systems," SME Tech. Paper AD77-731, Assemblex IV Conf. and Exposition, Dearborn, MI, Nov. 1977. Jones and Lamson, a Division of Textron Inc., Springfield, VT: The evolution of optical gauging is presented. , "Evolution of an electro-optical automatic gaging system," SME Tech. Paper EE78-368, 2nd Minnesota Electron. Manufacturing and Assembly Conf. and Exposition, May 1978. A general purpose and software programmable image analyzerthe Metric Eye-developed by Jones and Lamson for noncontact dimensional inspection is described. A. L. Wertheimer, H. N. Forck, and E. C. Muly, "Light scattering instrumentation for particulate measurements in processes," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 49-58. Leeds and Northrup Company, North Wales, PA: A line of instruments, the MICROTRAC, using light scattering to measure various parameters of particulate distribution is described. Examples include measurements of metal powder, quality control for ceramics manufacturing, and quality control of abrasives. H. L. Kasdan, "Recent developments in automatic visual inspection," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 28-36. Recognition Syst., Inc., Van Nuys, CA: Manufacturer of a variety of industrial inspection machines using image analysis, laser scanning, and coherent optical processing; The Needle Point Insp. Syst., the Fabric Insp. Syst., the Small Part Color Verification System, and the Cotton Trash Measurement System are described. D. C. Mead, H. L. Kasdan, and T. J. Middleton, "Quality control by diffraction pattern analysis," in Proc. SPIE, Solving Quality Contr. and Reliability Problems with Optics, vol. 60, May 1975, pp. 57-65. R. W. Pfoutz, "Digital optics for dimensional gauging of fabricated parts and continuous webs," in Proc. SPIE, Solving Quality Contr. Reliability Problems with Optics, vol. 60, San Diego, CA, May 1975, pp. 110-1 17. Reticon Corp., Sunnyvale, CA: Manufactures a variety of solid state image sensors and electronic gauging controllers. The inspection of metal strip, glass slide, bolt threads, small industrial diamond, plastic bottle top, and the Button Inspection System are described. J. P. Skurla, "Solid state image sensors offer reliability advantages in electrical terminal inspection system," presented at the ISA Industry Oriented Conf. and Exhibit, Milwaukee, WI, Oct. 1975. Describes the Reticon Corp. Electrical Terminal Inspection

System.

[114] "Reticon camera and systems product summary," Reticon

[115]

[116]

[1171

[118]

Corp., 1976. Described inspection systems include the Roller Insp. Syst., the Reed Switch Insp. Syst., the Hot Trip Insp. Syst., the Label Insp. Syst., and Plastic Molding Insp. Syst. "Production processes monitored by microprocessor controlled non-contact inspection systems," Comput. Design, pp. 55-66, Sept. 1978. Describes the inspection of tablet packaging and the detection of opaque defects on glass lenses. Both systems are developed by Reticon Corp. "Industrial measuring bottleneck bypassed," Opt. Spectra, pp. 28-29, Aug. 1978. Selective Electronic Company AB: A Swedish film; manufacturer of noncontact electrooptic instruments for measuring position, dimensions, and thickness. The Selspot system for displacement measuring and the Optocator system for dimension measurement are described. "Widespread industrial use of a new ligh-precision, non-contacting measurement system in process control applications is foreseen by its developer, Selective Electronic, Inc., for the optocator system," Laser Weekly, vol. 13, pp. 3-4, July 13, 1979. D. M. Swing, "Dimensional gauging using a scanning laser," in Proc. SPIE, Solving Quality Contr. and Reliability Problems with Optics, vol. 60, May 1975, pp. 118-123. Techmet Co., Dayton, OH: Developed the LaserMike System

569

for Autometrix Co. to inspect dimensions of catalytic converter ceremic substrate. [119] R. Moore, "Laser-based non-contact gauge for small parts inspection," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 18-23. Zygo Corp., Middlefield, CT: Custom manufacturer of industrial gauging instruments such as the Laser Telemetric System; Rod diameter measurement, die swell measurement, and nuclear fuel rod profilometry systems are described. A List of Visual Inspection Equipment Manufacturers Anter Laboratories, Inc., Pittsburgh, PA: Custom design and development of profile and dimensional measuring instruments. Autech Corp., Columbus, OH: Manufacturer of measuring systems utilizing laser/optic techniques. Colorado Video, Inc., Boulder, CO: Manufacturer of video instruments including video micrometers, digital processors, analyzers, and quantizers. Comptal Corp., Pasadena, CA: Manufacturer of general purpose programmable image processing systems. DeAnza Systems, Inc., Santa Clara, CA: Manufacturer of image analysis systems, image array processors, and other picture digitizing equipment. Diffrato Ltd., Detroit, MI: Custom manufacturer of laser and electrooptical systems for industrial gauging and inspection. Ford Aerospace & Communication Corp., Sub. of Ford Motor Co., Detroit, MI: Custom manufacturer and designer of automatic laser inspection and control systems. Hamamatsu Corp., Middlesex, NJ: Manufacturer of the Measuring Video Systems that have been used for industrial inspection such as the inspection of soft drink bottles. ILC Data Device Corp., Bohemia, NY: The Opto-Video System has been designed for hybrid microelectronic circuit inspection. Integrated Photomatrix, Inc.: Marketing noncontact measuring systems using linear photodetector arrays. Instrumentation Marketing Corp., Burbank, CA: Manufacturer of the Computer Automated Photo Digitizing System for the inspection of photomasks. International Imaging Systems, a Division of Stanford Technology Corp., Sunnyvale, CA: Manufacturer of video and digital image processors and systems. Joyce-Leobl, a Division of Vickers Ltd., Gateshead, England: Manufacturer of software based automatic image analysis systems such as the Magiscan System for industrial piece part inspection. E. Leitz, Inc., Rockleigh, NJ: Manufacturer of production and inspection equipment for electronic industry. Lindly & Co., Inc., Mineola, NY: Manufacturer of photoelectric inspection and quality control equipment. Object Recognition System, Inc.: Manufacturer of automatic visual inspection systems for both R&D and industry. Quantex Corp., Sunnyvale, CA: Manufacturer of high speed image processing equipment and digital image memory systems. Spatial Data Systems, Inc., Goleta, CA: Manufacturer of the EyeCom automated parts measurement system; A programmable digital television inspection system for use in noncontact measurements. Spectron Instrument Corp., Denver, CO: Custom manufacturer of noncontact automated inspection and quality control systems. Stahl Research Laboratories, Inc., Port Chester, NY: Manufacturer of image analysis optical scanning systems, X-Y state controllers, and custom electrooptical instrumentation. Stocker & Yale Inc., Beverly, MA: Manufacturer of optics-illumination and automatic gauging systems such as the AutoGauge Model VIMS-the Video Inspection Measurement System. F. Visual Inspection of Printed Circuit Patterns [120] D. L. Fehrs, "A gated optical comparitor for automated inspection of microcircuit patterns," presented at the Annu. Meet. Opt. Soc. Amer., San Francisco, CA, Oct. 1972. [121] F. L. A. M. Thissen, "Visual inspection of printed circuit boards using a heartline method," Philips Res. Tech. Note 228/7 3,

1973. [122] M. Ejiri, T. Uno, M. Mese, and S. Ikeda, "A process for detecting defects in complicated patterns," Comput. Graphics Image Processing, vol. 2, pp. 326-339, 1973. [123] C. Peterson, "Automated visual inspection," in Proc. 2nd Int. Joint Conf Pattern Recognition, Copenhagen, Denmark, 1974.

570


[124] C. K. Cheng, "A laser scanning system for computer circuit board inspection," presented at the Electro-Optics Syst. Design Conf., Anaheim, CA, Nov. 1975. [125] J. F. Jarvis, "Feature recognition in line drawing using regular expression," in Proc. 3rd Int. Joint Conf. Pattern Recognition, Coronado, CA, Nov. 1976, pp. 189-192. [126] 0. A. Olsen, "Visual method of locating faults in printed circuit boards," U.S. Patent 3976383, Aug. 24, 1976. [127] R. D. Taylor, "Rapid visual scanning," Bendix Corp., Kansas City, MO, Final Rep. BDX-613-1187, Aug. 1976. [128] T. Pavlidis, "A minimum storage boundary tracing algorithm and its application to automatic inspection," Princeton Univ., Tech. Rep. 222, Dec. 1976. [129] C. M. Bjorklund and T. Pavlidis, "On the automatic inspection and description of printed wiring boards," in Proc. Int. Conf. Cybern. Soc., Princeton, NJ, 1977, pp. 690-693. [130] "The present status of automated defect inspection for printed circuit boards," Nikkei Electron., pp. 80-92, Aug. 1977. [131] D. W. Baxter and R. E. Shipway, "Defect inspection of objects such as electronic circuits," U.S. Patent 4056716, Nov. 1, 1977. [132] F. L. A. M. Thissen, "An equipment for automatic optical inspection of connecting-lead patterns for integrated circuits," Philips Tech. Rev., vol. 37, no. 2, pp. 77-88, 1977. [133] R. C. Restrick, IIl, "An automatic optical printed circuit inspection system," in Proc. SPIE, Solid State Imaging Devices, vol. 1 16, 1977, pp. 76-8 1. [1341 D. T. Lee, "A computerized automatic inspection system for complex printed thick film patterns," in Proc. SPIE, Application of Electron. Imaging Syst., vol. 143, 1978, pp. 172-177. [135] C. C. K. Cheng, "Automated recognition system for industrial quality assurance," in Proc. SPIE, Image Understanding Syst. and IndustrialApplications, vol. 155, Aug. 1978, pp. 78-82. [136] A. Takaki, S. Ishihara, M. Naruse, and T. Yamada, "Automated inspection system for various defects in screen-printed patterns," in Proc. 13th Int. Congr. High Speed Photography and Photonics, Tokyo, 1978, pp. 594-597. [137] R. T. Chin, C. A. Harlow, and S. J. Dwyer, III, "Automatic visual inspection of printed circuit boards," in Proc. SPIE, Image Understanding Syst. and Industrial Applications, vol. 155, Aug. 1978, pp. 199-213. [138] R. T. Chin, "Automated visual inspection," College Eng., Univ. Missouri, Columbia, Aug. 1979. [139] W. A. Bentley, "The inspectron: An automatic optical printed circuit board (PCB) inspector," in Proc. SPIE, Opt. Pattern Recognition, vol. 201, Aug. 1979, pp. 37-47. [1401 L. Arlan, M. J. Cantella, T. J. Dudziak, and M. F. Krayewsky, "High-resolution computer-controlled television system for hybrid circuit inspection," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, July 1979, pp. 130-139. [141] W. M. Sterling, "Automatic non-reference inspection of printed wiring boards," in Proc. IEEE Comput. Soc. Conf. Pattern Recognition and Image Processing, Aug. 1979, pp. 93-100. [142] J. F. Jarvis, "A method for automating the visual inspection of printed wiring boards," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-2, pp. 77-82, Jan. 1980. [143] W. A. Bentley, "Automated optical inspection of multilayer printed circuit boards," in Proc. SPIE, Opt. in Metrology and Quality Assurance, voL 220, Feb. 1980. [144] L. Arlan, "Thick-film hybrid inspection with a computercontrolled high-resolution imaging and storage system," in Proc. SPIE, Opt. in Metrology and Quality Assurance, vol. 220, Feb. 1980. [145] L. Krakaner and T. Pavlidis, "Visual printed wiring board fault detection by a geometrical method," Comput. Sci. Lab., Princeton, Univ., Tech. Rep. 248, 1978. [146] M. Nakashima, K. Fujihara, and T. Inagaki, "Automatic mask pattern inspection for printed circuits based on pattern width measurement," in Proc. SPIE, Image Applications for Automat. Inspection and Assembly, vol. 182, Apr. 1979, pp. 38-48. [147] T. Pavlidis, "Syntactic pattern recognition of shape," in Proc. IEEE Comput. Soc. Pattern Recognition and Image Processing Conf., 1977, pp. 98-107. G. Visual Inspection of Microcircuit Photomasks

[ 148] L. S. Watkins, "Inspection of IC photomasks with intensity spatial filters," Proc. IEEE, vol. 57, pp. 1634-1639, 1969.

[149] L. S. Watkins, R. A. Heinz, R. L. Odenweller, Jr., and R. C. Oehrle, "Tool and production inspection by optical spatial filtering of periodic images," Western Elec. Eng., vol. 27, pp. 39-56, 1973. [150] L. S. Watkins, "Application of spatial filtering subtraction to thin film and integrated circuit mask inspection," Appl. Opt., vol. 12, pp. 1880-1884, Aug. 1973. [1511 E. K. Sittig and M. Feldman, "An automated inspection system for IC masks with replicated patterns," in Proc. Kodak Microelectron. Seminar, Atlanta, GA, Oct. 1973. [152] R. J. Bourdelais, D. Colangelo, R. J. McFadyen, and J. F. Elliott, "Instrument for automatically inspecting integrated circuit masks for pinholes and spots," U.S. Patent 3795452, Mar. 5, 1974. [153] J. D. Cuthbert, D. F. Munro, and D. L. Fehrs, "A microelectronic mask inspection system based on single spot laser scan techniques," in Proc. ICO Conf Opt. Methods in Sci and Measurements, Tokyo, 1974, pp. 481-486. [1541 "Automatic mask inspection: A new problem for the laser," Opt. Spectra, July 1975. [155] J. H. Bruning, M. Feldman, T. S. Kinsel, E. K. Sittig, and R. L. Townsend, "An automated mask inspection system-AMIS," IEEE Trans. Electron Devices, vol. ED-22, July 1975. [1561 D. R. Ciarlo, P. A. Schultz, and D. B. Novotny, "Automated inspection of IC photomasks," in Proc. SPIE, Photofabrication Imagery, vol. 55, 1975, pp. 84-89. [157] F. G. O'Caliaghan, H. C. Clarke, and R. W. Poindexter, "Automated mask inspection for registration and dimensions," presented at the SPSE Winter Symp. Micro-Photo Fabrication, 1975. [158] I. Tanabe, "Production of high quality photomasks for Mos LSI's," Interface 75, Kodak Pub. G45, pp. 91-99, 1975. [159] M. Minami, H. Sekizawa, H. Masuda, and T. Watanabe, "A new mask inspection device," in Proc. Micro-Electronic Seminar Interface 76, pp. 67-80, Oct. 1976. [160] T. Ito, "Pattern classification by color effect method," in Proc. 3rd Int. Joint Conf Pattern Recognition, Nov. 1976, pp. 2630. [1611 D. R. Ciarlo, "IC inspection test masks-Experimental results," Lawrence Livermore Lab. Rep. UCID 17391, Feb. 1977. [162] J. G. Skinner, "The use of an automatic mask inspection system (AMIS) in photomask fabrication," in Proc. SPIE, Semiconductor Microlithography II, vol. 100, 1977, pp. 20-25. [163] P. Sandland, "Automatic inspection of mask defects," in Proc. SPIE, Semiconductor Microlithography II, voL 100, 1977, pp. 26-35. [164] D. Nyyssonen, "Linewidth measurement with an optical microscope: The effect of operating conditions on the image profile," Appl. Opt., vol. 16, pp. 2223-2230, 1977. [165] A. H. Firester, "Laser optical inspection systems," RCA Eng., vol. 22, pp. 10-13, Apr./May 1977. [166] 1. D. Knox, P. V. Goedertier, D. W. Fairbanks, and F. Caprari, "Inspecting IC masks with a differential laser scanning system," RCA Eng., vol. 22, Apr./May 1977. [167] L. U. Almi and J. Shamir, "One-dimensional Fourier transform for the inspection of photomasks," in Applications of Holography and Optical Data Processings, Marom, Friesem, and Wiener-Avnear, Eds. Pergamon, 1977, pp. 541-548. [168] D. Nyyssonen, "Optical linewidth measurements on silicon and iron-oxide photomasks," in Proc. SPIE, Semiconductor Microlithography II, vol. 100, 1977, pp. 127-134. [169] Y. Nakagawa, et al., "Study on automatic visual inspection of shadow-mask patterns," in Information Control Problems in Manufacturing Technology, IFAC. Pergamon, 1977, pp. 63-70. [170] M. Minami, "IC pattern inspection with coherent optics," in Proc. Conf: Laser and Electroopt. Syst., San Diego, CA, Feb. 1978, p. 66. [171] K. Levy, "Automated equipment for 100% inspection of photomasks," Solid State Technol., pp. 60-66, May 1978. [172] Y. Goto, Y. Furukawa, and T. Inagaki, "Inspection for defects of a mask containing one to submicrometer patterns using a scanning electron microscope and feature extraction method," J. Vaccuum Sci. Technol., vol. 15, pp. 953-956, May/June 1978. [173] N. Goto, T. Kondo, K. Ichikawa, and M. Kanemoto, "An automatic inspection system for mask patterns," in Proc. 4th Int. Joint Conf. Pattern Recognition, Japan, Nov. 1978, pp. 970974.

571


[174] K. A. Snow, "Optical problems of small geometry automatic mask inspection," in Proc. SPIE, Develop. Semiconductor Microlithography III, vol. 135, 1978, pp. 96-103. [175] D. B. Novotny and D. R. Ciarlo, "Automated photomask inspection part I," Solid State Technol., pp. 51-59, May 1978; "Automated photomask inspection part 2," Solid State Technol., pp. 59-67, June 1978. [176] R. P. Speck, "The practical aspects of automatic photomask inspection," in Proc. SPIE, Develop. in Semiconductor Microlithography V, vol. 221, San Jose, CA, Mar. 1980. [177] D. L. Kryger, "Microdensitometer measurements of photomask quality," in Proc. SPIE, Opt. Metrology and Quality Assurance, vol. 220, Feb. 1980. [178] B. D. Figler, "Photomask mensuration with the linear microdensitometer," in Proc. SPIE, Opt. Metrology and Quality Assurance, vol. 220, Feb. 1980. [179] Y. Hara, T. Hamade, K. Nakagawa, S. Torisawa, S. Nakashima, Y. Yabuhara, and N. Akiyama, "Automatic inspection of LSI photomasks," in Proc. 5th Int. Conf Pattern Recognition, Miami Beach, FL, Nov. 1980. [180] N. N. Axelrod, "Intensity spatial filtering applied to defect detection in integrated circuit photomasks," Proc. IEEE, vol. 60, pp. 447-448, 1972. H. Chip Inspection and Alignment for Wire Bonding [181] B. K. P. Horn, "A problem in computer vision: Orienting silicon integrated circuit chips for lead bonding," Comput. Graphics Image Processing, vol. 4, pp. 294-303, 1975. [1821 S. Kashioka, M. Ejiri, and Y. Sakamoto, "A transistor wirebonding system utilizing multiple local pattern matching techniques," IEEE Trans. Syst., Man, Cybern., vol. SMC-6, pp. 562569, Aug. 1976. [183] T. Inoue, N. Sakai, E. Tsuda, and T. Inari, "Automatic transistor die-bonding system with TV cameras," in Proc. 7th Int. Symp. Industrial Robots, Oct. 1977, pp. 515-522. [184] T. Inoue, "Automatic die-bonding system for semiconductors with TV cameras," Assemblex IV Conf. and Exposition, SME Tech. Paper AD77-719, Detroit, MI, Nov. 1977. [185] M. Mese, I. Yamazaki, and T. Hamada, "An automatic position recognition technique for LSI assembly," in Proc. 5th Int. Joint Conf Artificial Intell., Aug. 1977, pp. 685-693. [186] M. L. Baird, "An application of computer vision to automated IC chip manufacture," presented at the 3rd Int. Joint Conf. Pattern Recognition, Coronado, CA, Nov. 1976. [187] "Vision system may allow computers to handle inspection tasks," Comput. Design, p. 42, Dec. 1977. [188] M. L. Baird, "SIGHT-I: A computer vision system for automated IC chip manufacture," IEEE Trans. Syst., Man, Cybern., vol. SMC-8, pp. 133-139, Feb. 1978. [189] C. Arnst, "System at GM sees, inspects ignition chips," ComputerWorld, p. 10, Dec. 26, 1977/Jan. 2, 1978. [1901 K. Igarashi, M. Naruse, S. Miyazaki, and T. Yamada, "Fully automated integrated circuit wire bonding system," in Proc. 9th Int. Symp. Exposition on Industrial Robots, Washington, DC, Mar. 1979, pp. 87-97. [191] S. Kawata and Y. Hirata, "Automatic IC wire bonding system with TV cameras," presented at the SME Assembly VI Conf., 1979. [192] Y. Y. Hsieh and K. S. Fu, "A method for automatic IC chip alignment and wire bonding," in Proc. IEEE Comput. Soc. Conf Pattern Recognition and Image Processing, Aug. 1979, pp. 101-108. I. Inspection of Other Electrical and Electronic Assemblies

[193] W. C. Lin and C. F. Chan, "Feasibility study of automatic

assembly and inspection of light bulb filaments," Proc. IEEE, vol. 63, pp. 1437-1445, Oct. 1975. [194] J. A. G. Hale and P. Saraga, "Control of a PCB drilling by visual feedback," in Proc. 4th Int. Joint Conf; Artificial Intell., U.S.S.R., 1975, pp. 775-781. [195] K. Edamatsu et al., "An image processing system for defect detection," Fuji Elec. J., vol. 49, pp. 10-15, Nov. 1976. [1961 M. P. Wirick et al., "Infrared testing of printed circuit boards and hybrids," in Proc. SPIE, Optics in Metrology and Quality Assurance, vol. 220, Feb. 1980. [197] L. F. Pau, "Integrated testing and algorithms for visual inspec-

[198] [199]

[200] [201] [202]

[203]

[204]

[205]

[206]

tion of semiconductor IC's," in Proc. 5th Int. Conf. Pattern Recognition, Miami Beach, FL, Nov. 1980. D. D. Schroeder and R. E. Hines, "Component verification system," IEEE Trans. Components, Hybrids, Manuf. Technol., vol. CHMT-3, pp. 367-369, Sept. 1980. R. T. Chien and W. E. Snyder, "Visual understanding of hybrid circuits via procedural models," in Proc. 4th Int. Joint Conf: Artificial Intell., U.S.S.R., 1975, pp. 742-748. D. R. Mountjoy, "An automatic system for the visual inspection of printed circuit boards," Ph.D. dissertation, College Eng., Univ. Missouri, Columbia, Dec. 1976. J. F. Jarvis, "Automatic visual inspection of Western Electric series 700 connectors," in Proc. IEEE Comput. Soc. Conf. Pattern Recognition Image Processing, 1977. , "Automatic visual inspection of glass-metal seals," in Proc. 4th Int. Joint Confi Pattern Recognition, Japan, Nov. 1978, pp. 961-965. E. S. McVey and A. Van Tol, "An experimental printed circuit board drilling system automated by pattern recognition," Pattern Recognition, vol. 1 1, pp. 271-276, 1979. C. A. Klein and K. J. Breeding, "Automatic optical identification of faults in bubble memory overlay patterns," in Proc. IEEE Comput. Soc. Conf. Pattern Recognition and Image Processing, Aug. 1979, pp. 87-92. J. Van Daele, A. Oosterlinck, and H. Van den Berghe, "The Leuven automated visual inspection machine (LAVIM)," in Proc. SPIE, Imaging Applications for Automat. Inspection and Assembly, vol. 182, Apr. 1979, pp. 58-64. , "Automatic visual inspection of Reed switches," Opt. Eng., vol. 19, pp. 240-244, Mar./Apr. 1980.

J. Inspection of Automobile Parts [207] R. M. Center, "Brake cylinder inspection combines laser, minicomputer," Qual. Management Eng., Sept. 1972. [208] R. A. Wey and J. K. Bowker, "Optical sensing of celi-structure defects in catalytic converter substrates," in Proc. SPIE, Solving Qual. Contr. and Reliability Problems with Optics, vol. 60, May 1975, pp. 68-76. [209] P. A. McKeown, P. Cook, and W. P. N. Bailey, "The application of optics to the quality control of automotive components," in Proc. SPIE, Solving QuaL Contr. and Reliability Problems with Optics, vol. 60, May 1975, pp. 77-84. [210] P. D. Poulson and L. 0. Ford, "Inspection of axially symmetric parts," in Proc. SPIE, Solving Qual. Contr. and Reliability Problems with Optics, vol. 60, May 1975, pp. 91-98. [211] J. Caulfield, E. R. Schildkraut, and J. C. Cruz, "An optical recognition and classification system for on-line inspection," in Proc. SPIE, Advances in Laser Eng., 1977, pp. 18-2 1. [212] R. N. West and W. J. Stocker, "Automatic inspection of cylinder bores," Metrol. Inspection, July 1977. [213] B. G. Batchelor and G. A. Williams, "Defect detection on the internal surface of hydraulics cylinder for motor vehicles," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 65-78. [214] D. J. Kopydlowski, "Missing hole detection system using solidstate video cameras," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 118-129. [2151 S. Sugiyama and N. Takahashi, "Optical measuring device for interior dimensions of automobiles," in Proc. SPIE, Advances in Laser Eng. Applications, vol. 247, July 1980. K. Inspection in Metal Processing Industry [216] R. A. Brook, "An experimental automatic surface inspection system," Metron, vol. 3, pp. 219-223, Aug. 1971. [217] I. G. Logan and J. E. S. Macleod, "An application of pattern recognition algorithms to the automatic inspection of steel strip surface," in Proc. 2nd Int. Joint Conf. Pattern Recognition, Copenhagen, Denmark, 1974, pp. 286-290. [218] L. Norton-Wayne and W. J. Hill, "The- automatic classification of defects on moving surfaces," in Proc. 2nd Int. Joint Conf. Pattern Recognition, Copenhagen, Denmark, 1974, pp. 476478. [219] J. T. Fleckenstein, "Inspection and process control in the metal industry," presented at the Automat. Inspection Contr. Conf., Oct. 1974. [220] R. V. Williams, "Applied optics in the European coal and steel

572

[2211

[2221

[223] [2241 [2251

[226]


community," in Proc. SPIE, Solving Qual. Contr. and Reliability Problems with Optics, May 1975, pp. 52-56. R. A. Brook, D. J. Purll, G. H. Jones, and D. 0. Lewis, "Practical experience of image processing in on-line industrial inspection applications," in Proc. SPIE, Automat. and Inspection Applications of Image Processing Techniques, vol. 130, Sira, London, 1977, pp. 84-89. D. J. Purll, "Automated surface inspection with solid-state image sensors," in Proc. SPIE, Industrial Applications of SolidState Image Scanners, vol. 145, Sira, London, 1978, pp. 18-25. A. J. Baker and R. A. Brook, "A design study of an automatic system for on-line detection and classification of surface defects on cold-rolled steel strip," Optical Acta, vol. 25, no. 12, pp. 1187-1196, 1978. J. L. Mundy, "Visual inspection of metal surfaces," in Proc. AFIPS Nat. Comput. Conf., NY, June 1979, pp. 227-231. G. B. Porter, T. M. Cipolla, and J. L. Mundy, "Automatic visual inspection of blind holes in metal surfaces," in Proc. IEEE Comput. Soc. Conf. Pattern Recognition and Image Processing, Aug. 1979, pp. 83-86. J. L. Mundy, "Visual inspection of metal surfaces," in Proc. 5th Int. Conf. Pattern Recognition, Miami Beach, FL, Nov. 1980.

L. Inspection of Fabric

[227] A. L. Knoll, "Automatic fabric inspection," Textile Inst. Industry, Jan. 1975. [2281 S. Takamatsu, "Preparations and fabric inspection," J. Textile Mach. Soc. Japan, vol. 29, p. 9, Mar. 1976. [2291 A. Shibuya and N. Aisaka, "Inspection of fabric defects," J. Textile Mach. Soc. Japan, vol. 29, p. 22, Sept. 1976. [230] E. Kuse, "On the automated systems of fabric inspection and sewing in recent U.S.A.," J. Soc. Faber Sci. Technol. Japan, vol. 34, p. 28, Jan. 1978. [231] H. Koshimizu and S. Aoi, "Study on the fabric inspection by computer image processing," in Proc. 9th Conf. Image Eng. Japan, Dec. 1978, p. 55. [2321 R. N. Dixon and D. J. Taylor, "Automated asbestos fibre counting," in Machine-Aided Image Analysis, W. E. Gardner, Ed., 1978, pp. 178-185. [233] M. Sawaji, "Measuring method of quality of luster: Measurement and analysis of microstructural reflection characteristics," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, 1979, pp. 6-13. [234] H. Koshimizu, "Fundamental study on automatic fabric inspection by computer image processing," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, 1979, pp. 30-37. [2351 W. E. Wolf et al., "On-line inspection for nonwoven fabrics," in Proc. SPIE, Opt. in Metrology and Quality Assurance, vol. 220, Feb. 1980. M. Inspection by Radiographic Imaging [2361 L. W. Wright, "Results of X-ray television inspection of electronic parts," in Proc. 6th Symp. Nondestructive Evaluation of Aerosp. and Weapons Syst. Components and Materials, San Antonio, TX, 1967. [237] C. N. Jackson, Jr. and W. H. Gray, "X-ray real-time imaging for nondestructive testing of fast flux test facility fuel pins," in Proc. SPIE, Imaging Techniques for Testing and Inspection, vol. 29, 1972, pp. 11-16. [2381 "Radiography and digital computers open new doors in NDT field," Quality, pp. 12-14, June 1977. [239] K. A. Brasnett, "Towards automated analysis of gas turbine radiographs," in Machine-Aided Image Analysis, W. E. Gardner, Ed., 1978, pp. 168-177. [240] J. J. Pearson, M. A. Fischler, 0. Firschein, and M. Jacoby, "Application of image techniques to automatic radiographic inspection," in Proc. 7th Annu. EIA Automat. Imagery Pattern Recognition Symp., May 1977, pp. 170-176. [2411 J. J. Pearson, W. G. Eppler, 0. Firschein, M. H. Jacoby, J. Keng, and S. M. Jaffey, "Automatic inspection of artillery shell radiographs," in Proc. SPIE, Image Understanding Syst. and Industrial Applications, vol. 155, Aug. 1975, pp. 2142)21.

[242] Automatic X-Ray Inspection System, IMSC-D625398, Lockheed Palo Alto Res. Lab., Palo Alto, CA, 1978. [2431 0. Firschein, W. G. Eppler, and M. A. Fischler, "A fast defect measurement algorithm and its array processor mechanization," in Proc. IEEE Comput. Soc. Conf. Pattern Recognition and Image Processing, Aug. 1979, pp. 109-113. [244] D. L. Davies and B. E. Kinchen, "Radiography clearance measurement of turbine engines using digital image analysis techniques," presented at the ASNT Spring Conf., San Diego, CA, Apr. 1979. [2451 D. L. Davies, P. H. Smith, and J. F. Liutermoza, "Discontinuous registration of industrial radiographs using profile analysis and piecewise correlation techniques," Opt. Eng., vol. 19, pp. 425-432, May/June 1980. [246] T. Skramnstad, "Towards automatic radiographic inspection using image processing methods," in Proc. 1st Scandinavian Conf: Image Analysis, Jan. 1980, pp. 302-307. N. Other Applications [247] I. J. Spalding, "Laser systems development," Phys. Bull., pp. 401-402, 1971. [248] "Computer scans lumber-Locate defects," Automation, p. 24, Apr. 1973. [249] H. Ikeda, T. Inagaki, and Y. Nishimura, "Ceramic surface inspection using laser technique," in Proc. ICO Conf. Optical Methods in Sci. and Industrial Measurements, Tokyo, Japan, 1974, pp. 487-492. [2501 W. J. Coleman, F. R. Reich, M. D. Erickson, and W. S. Kelly, "On-line automatic high speed inspection of cartridge cases," in Proc. Conf. Automat. Inspection and Product Contr., Oct. 1974, pp. 113-123. [251] W. A. Dittrich, "Problems in high speed NDE," in Proc. Conf. Automat. Inspection and Product Contr., Oct. 1974, pp. 303305. [252] D. W. Hanity and R. Kasparas, "The Army looks at 100 percent inspection of links," in Proc. Conf. Automat. Inspection and Product Contr., Oct. 1974, pp. 287-300. [2531 J. E. Callen and P. N. Anderson, "Checking labeled bottles electro-optically," Electro-Opt. Syst. Design, pp. 44-46, July 1975. [254] B. J. Biddles and L. R. Baker, "Surface inspection of optical and semiconductor components," in Proc. SPIE, Solving Qual. Contr. Reliability Problems with Optics, vol. 60, May 1975, pp. 34-38. [255] D. H. Locke, "Lithographic plate surface inspection using fiber optics," in Proc. SPIE, Solving Qual. Contr. Reliability Problems with Optics, May 1975, pp. 39-44. [256] R. Paulson, J. Barney, R. F. Leftwich, and R. Friedman, "Infrared imaging for thermal analysis of quality control and reliability problems," in Proc. SPIE, Solving Qual. Contr. Reliability Problems with Optics, vol. 60, May 1975, pp. 85-90. [257] W. S. Maughan, "The use of automatic optical sorting equipment for industrial quality control," in Proc. SPIE, Solving QuaL Contr. Reliability Problems with Optics, vol. 60, May 1975, pp. 99-107. [258] S. C. Bottomley and E. G. Nightingale, "Optics a la carte," in Proc. SPIE, Solving Qual. Contr. Reliability Problems with Optics, vol. 60, May 1975, pp. 130-136. [2591 T. R. Pryor, "Laser inspection of manufactured products," presented at the Southwestem Tool and Manufacturing Exposition Eng. Conf., Jan. 1976. [260] G. S. Birth, "Applications of electro-optical techniques to nondestructive quality evaluation of foods," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 12, Nov. 1977, pp. 64-71. [261] L. C. Page, "Inspection of parts in micro-inches with a closed circuit television or how to measure to a half-millionth of an inch without hardly trying," in Proc. SPIE, Effective Utilization of Optics in Quality Assurance, vol. 129, Nov. 1977, pp. 94-97. [262] W. J. Coleman and K. L. Swinth, "Automatic cartridge case inspection and process control monitor," in Proc. SPIE, Advances in Laser Eng., vol. 122, Aug. 1977, pp. 33-43. [263] A. Kuni et al., "Study on automatic inspection of defects on contact parts," in Information Control Problems in Manufacturing Technology, IFAC. Pergamon, 1977, pp. 63-70.


[264] E. L. Graminski and R. A. Kirsh, "Image analysis in paper manufacturing," in Proc. IEEE Comput. Soc. Conf: Pattem Recognition and Image Processing, June 1977, pp. 137-143. [265] T. Kasvand, "Experiments on automatic extraction of paper pulp fibers," in Proc. 4th Int. Joint Conf. Pattern Recognition, Kyoto, Japan, Nov. 1978, pp. 958-959. [2661 D. P. Himmel, "A laser measuring system for automated industrial inspection," in Proc. 4th Int. Joint Conf: Pattern Recognition, Kyoto, Japan, Nov. 1978, pp. 952-955. [267] D. R. McLemore, D. H. Nyman, and R. S. Wiks, "Automatic surface flaw inspection of nuclear fule pellets," in Proc. SPIE, Applications of ElectrorL Imaging Syst., vol. 143, 1978, pp. 129-135. [268] L. T. Clarke, "The use of self scanned arrays in the glass industry," in Proc. SPIE, Ihdustrial Applications of Solid-State Image Scanners, vol. 145, Sira, London, 1978, pp. 28-36. [269] J. L. Mundy and R. E. Joynson, "Automatic visual inspection using syntactic analysis," in Proc. IEEE Comput. Soc. Pattern Recognition and Image Processing Conf., 1977, pp. 144-147. [270] S. Buckley and K. Stelson, "Phase monitoring for automated inspection, positioning and assembly," presented at the SME Assemblex IV Conf., Tech. Paper AD77-730, 1977. [271] R. B. Lewis, "The use of solid-state image scanners in the British railways track recording coach," in Proc. SPIE, Industrial Applications of Solid-State Image Scanners, voL 145, Sira, London, 1978, pp. 38-44. [272] B. D. Clay, "A photo-electronic method for the monitoring of deformation in nuclear fuel cladding tubes," in Proc. SPIE, Industrial Applications of Solid-State Image Scanners, vol. 145, Sira, London, 1978, pp. 45-49. [2731 I. McFarlane, "Development of the heightscan thickness gauge," in Proc. SPIE, Industrial Applications of iSolid-State Image Scanners, vol. 145, Sira, London, 1978, pp. 50-55. [274] J. F. Claridge and D. J. Purll, "On-line volume measurement of bulk materials," in Proc. SPIE, Industrial Applications of Solid-State Image Scanners, vol. 145, Sira, London, 1978, pp. 58-65. [275] R. N. West, "Automatic inspection of photographic materials," Optical Acta, vol. 25, no. 12, pp. 1207-1214, 1978. [276] K. Nakamura, K. Edamatsu, and Y. Sano, "Automated pattern inspection with boundary length comparison method," in Proc. 4th Int. Joint Conf. Pattern Recognition, Kyoto, Japan, Nov. 1978, pp. 955-957. [277] H. Ellinger, I. L. Morgan, R. Klinksiek, and F. Hopkins, "Tomographic analysis of structural materials," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 179-186. [278] C. F. Barton, K. Bailey, and J. P. Barton, "Computerized tomography for industrial applications," presented at the ASNT Spring Conf., San Diego, CA, Apr. 1979. [279] R. P. Kruger and R. A. Morris, "Simulated neutron tomography for nondestructive assays," in Proc. SPIE, Imaging Applications for Automat. Industrial Inspection and Assembly, vol. 182, Apr. 1979, pp. 158-164. [280] R. P. Kruger, G. W. Wecksung, and R. A. Morris, "Industrial applications of computer tomography at Los Alamos scientific laboratory," Opt. Eng., vol. 19, pp. 273-282, May/June 1980. [281] J. Kittler and L. F. Pau, "Automatic inspection by lots in the presence of classification errors," Pattern Recognition, vol. 12, no. 4, pp. 237-241, 1980. [282] S. Barnard, "Automated inspection using gray-scale statistics," in Proc. 5th Int. Conf. Pattern Recognition, Miami Beach, FL, Nov. 1980. [283] R. Horaud and J. P. Charras, "Automatic inspection and orien-

573

[284]

[285] [286] [287] [288]

tation of extemal screws," in Proc. 5th Int. Conf. Pattern Recognition, Miami Beach, FL, Nov. 1980. K. S. Fu and W. H. Tsai, "A syntactic statistical approach to recognition of industrial objects," in Proc. 5th Int. Conf. Pattern Recognition, Miami Beach, FL, Nov. 1980. C. B. Chittineni, "Signal classification for automatic industrial inspection," presented at the Workshop on Digital Signal and Waveform Anal., Miami Beach, FL, Nov. 1980. E. Bres, P. Meyrueis, and P. D. Meyer, "Application of laser engineering to industrial measurement of commercialized diamonds," in Proc. SPIE, Advances in Laser Eng. and Applications, vol. 247, San Diego, CA, July 1980. R. C. Grove, B. E. Rapp, and C. W. Souder, "A real-time image processing system for automated inspection of drilled holes," in Proc. SPIE, Real-Time Signal Processing III, vol. 241, San Diego, CA, July 1980. R. Cederbery and S. Svensson, "Machine detection of chips on pieces of turnery," in Proc. 1st Scandinavian Conf. on Image Anal., Jan. 1980, pp. 270-274.

Roland T. Chin (S'75-M'79) was bom in Macau on September 16, 1952. He received the B.S. degree with honors in 1975, the M.S. degree in 1976, and the Ph.D. degree in 1979, all in electrical engineering from the University of Missouri, Columbia. While completing his doctoral studies at the University of Missouri, he held- both a Research Assistantship and a Teaching Assistantship in the Image Analysis Laboratory, working on automated visual inspection. From 1979 to i981, he was with Business and Technological Systems, Inc., Seabrook, MD, where he engaged in research in remote sensing data analysis and classification. He is presently an Assistant Professor of Electrical and Computer Engineering at the University of Wisconsin, Madison. His current research interests are in the area of image processing, pattern recognition, industrial advanced automation, artificial intelligence, and computer systems. Dr. Chin is a member of Eta Kappa Nu and Tau Beta Pi.

Charles A. Harlow (S'62-M'67) was born in New Boston, TX, on March 14, 1940. He received the B.S. and Ph.D. degrees from the University of Texas in 1963 and 1967, respectively, both in electrical engineering. In 1967 he joined the staff of the Department of Electrical Engineering at the University of Missounr, Columbia, as Assistant Professor. ln 1972 he was appointed Professor of Electrical E ngineering. In 1974 he was visiting Professor of Electrical Engineering and Computer Science at the University of Califomia, Berkeley. In 1978 he joined the faculty of Louisiana State University as Professor of Electrical Engineering. His principal areas of research interest are image analysis and software design. Dr. Harlow is a member of Tau Beta Pi, Eta Kappa Nu, the Association for Computing Machinery, and the American Society of Photogrammetry.