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A 10 kPixel CMOS Hall Sensor Array With Baseline Suppression and Parallel Readout for Immunoassays Simone Gambini, Member, IEEE, Karl Skucha, Student Member, IEEE, Paul Peng Liu, Member, IEEE, Jungkyu Kim, and Reut Krigel
Abstract—A CMOS microsystem for detecting microparticles in magnetic immunoassays uses the intrinsic dynamics of magnetization in super-paramagnetic materials to reduce measurement baseline by compared to conventional methods, and to , with an improvement of achieve a signal to baseline ratio over 50 compared to systems that ignore bead dynamics. The microsystem integrates a 64 160 Hall-sensor array with columnparallel readout electronics that combine auto-zeroing and nested chopping to enable low-offset, power efficient signal acquisition and has an Allan deviation floor of 9 nT. Index Terms—Auto-zeroing, chopping, CMOS, Hall effect, immunosensor, magnetic immunoassays, offset, paramagnetism.
I. INTRODUCTION
R
ECENT years have seen a surge of interest in point-ofcare diagnostic devices [1] that enable disease detection outside of the hospital walls in settings such as a clinician’s office or a patient’s home. Amongst such future portable blood analyzers, devices employing super-paramagnetic micrometer or nanometer-sized particles [2] have drawn particular interest for their desirable properties [3]–[10]. In particular such particles: 1) enable testing in whole blood, which has no magnetic content and essentially no attenuation for low frequency magnetic fields 2) can be detected by miniature magnetic transducers [4] 3) are already commercially available in functionalized form and are utilized for sample separation or purification [2] in several analytical protocols. In the magnetically labeled immunoassay exemplified in Fig. 1, a critical step is the electronic quantification of the number of magnetic beads attached to the transducer (e.g., a Hall-effect sensor in Fig. 1), which becomes a proxy for the concentration of target biomarker present in the sample. Several academic and industrial research groups have developed platforms for the detection of super-paramagnetic particles, with solutions exploiting inductive sensors [3], GMR sensors ([4]–[7]), Hall-effect sensors ([8], [10]), or Nuclear Magnetic Resonance [9]. A Manuscript received April 23, 2012; revised October 04, 2012; accepted October 04, 2012. This paper was approved by Guest Editor Maurits Ortmanns. This work was supported by the Berkeley Sensor and Actuator member companies and NIH Genes, Environment and Health Initiative, Biological Response Indicators of Environmental Systems Center Grant U54 ES016115-01. The authors are with the University of California, Berkeley, CA 94720-1774 USA (e-mail:
[email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSSC.2012.2224531
summary of the state of the art is reported in Table I. While the solutions in Table I achieve outstanding magnetic sensitivity and integration, despite the variety of transducer technologies and measurement strategies presented, they all share a major challenge: the magnetic signal of the beads appears as a small change super-imposed on a large baseline (Fig. 2, left). Since the transducer sensitivity depends on temperature, the baseline drifts with temperature (or other environmental fluctuations), necessitating accurate system calibration. The presence of the baseline is due to the paramagnetic nature of the particles, which makes their net magnetic moment (and hence signal) zero unless an external excitation is provided. This external excitation almost invariably limits the measurement baseline BL, and it can be 100 to times larger than the signal generated by a single label . A system figure of merit, the signal-to-baseline ratio , is reported in the last row of Table I. Relative baseline stability and calibration accuracy proportional to are required, making an indicator of system robustness. For example, if , the baseline should be calibrated with relative accuracy much better than 1% for correct system operation. Achieving adequate baseline stability in [3] required for example the use of both a replica sensor and an active temperature control loop; while [5] and [6] apply continuous background gain and offset calibration of each sensor. In [11] the nonlinear characteristic of the magnetization curve of the labels is used to produce a measurement with reduced baseline. However, this solution necessitates a large (20 mT) time-varying magnetic field, which is difficult to generate on chip. In [10], the authors introduced a measurement principle that exploits the intrinsic magnetization dynamics of the labels to take an ideally baseline-free measurement ( ). We call this a relaxation measurement; in contrast to the conventional magnetization measurement where the magnetic field of the label is evaluated while it is being magnetized. This measurement principle is also highlighted in Fig. 2 (right half). The first proof-of-concept implementation [10] for a 256-element Hall-sensor array was equipped with a single-channel continuous-time readout to enable characterization as well as detection of particles. While [10] successfully demonstrated the ability to reduce baseline by over 300 using a relaxation measurement its 256-element array was too small to provide clinically relevant results. In addition, the system in [10] uses a single-channel readout chain that severely limits the speed at which the array can be measured. In this paper, we present a Hall-sensor microsystem that can detect magnetic particles on a large-scale array. As shown in
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Fig. 1. Principle of magnetic sandwich immune-assay: the sensor surface is functionalized with a capture antibody (1) and exposed to the sample (2). The magnetically labeled detection antibody is subsequently introduced (3). After non-specifically bound labels are washed away (4), counting the remaining labels (5) measures the concentration of target in the sample.
Fig. 2. Magnetic bead detection strategies. Left: magnetization measurement; right: relaxation measurement. The net bead signal is given by the area under the are highlighted. shaded curve. The components of signal-to-baseline ratio
TABLE I STATE OF THE ART IN INTEGRATED DETECTORS OF MAGNETIC LABELS.
the Section II-A, the chosen array size reduces biological shot noise to levels below the requirement of a bio-assay ([12], [13]). We improve on [10] by combining relaxation measurement with transducer and system optimization to increase the signal. The readout electronics have been completely redesigned to improve scalability and enable column-parallel readout.
This paper is organized as follows: in Section II we introduce the requirements on baseline stability and noise and discuss the sensor array and magnetization subsystem design. The electronic subsystem is discussed in Section III. Electrical characterization is reported in Section IV, while bio-assay results are discussed in Section V.
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Fig. 3. Readout figure of merit
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as a function of normalized modulation period.
II. SYSTEM REQUIREMENTS AND HIGH-LEVEL DESIGN While no standard set of specifications exist for a bead detector, a few guidelines can be formulated considering the application. 1) Accuracy: To be competitive with commercial assays, the device should be able to provide measurements with a standard deviation to mean ratio (coefficient of variation or in the following) of the order of 5% in a non-temperature controlled environment and while ideally requiring no calibration [14]. The lowest concentration of target analyte at which this accuracy can be maintained represents the limit of detection (LOD) of the assay. 2) Speed: Since the bio-chemical reactions associated with the assay typically require 10 to 30 minutes to complete [5], the detector readout time should be less than 2 minutes, so it contributes negligibly to the processing time. 3) Portability: To maximize portability, the device should ideally be battery powered, e.g., by a single 2 cm lithium battery. Since the detection chip should be disposable to prevent cross-contamination, a single-test should consume only a small a fraction of the 2 kJ provided by such battery. These guidelines drive the high level design of this detection platform, as discussed below.
required dynamic range is also known , the sensing area can be calculated [13]. As an example, for 2.8 m-diameter particles, 1:100 dynamic range and , the minimum and maximum number of labels on the array are and , and the required sensing area is 0.3 mm . When smaller beads are used, the required area decreases. However, excess sensing area can be used for multiplexing several analytes on a single platform. This design targets 2.8 m labels in order to be compatible with the microfluidic technology and protocols described in [27]. It uses a total sensing area of 0.6 mm ; the area in excess of 0.3 mm is allocated to reference sensors and design margin. A single 0.6 mm Hall sensor would however be ineffective at measuring the local field from a bead. Instead, the sensing area is divided in four 64 40 arrays of 8 m 6 m Hall effect pixels, for a total of pixels. Of the 40 columns in the array, 36 are active while the outer 2 columns on each side are covered by a metal 1 plate and were designed to act as reference sensors to improve environmental tracking. These reference sensors were however not used in the final measurements. Fig. 4 shows the overall chip architecture, including the electronics. B. Thermal Noise Requirements
A. Biological Shot Noise and Array Format All affinity-based biosensors are ultimately limited by the stochastic nature of the chemical recognition process, and the associated biological shot noise [13]. For immunoassays, this chemical recognition is typically an antibody-antigen binding event. A Poisson distribution can describe the number of bound antibodies for a given concentration. If the expected number of bound labels is , we obtain a standard deviation of .As a result, once we specify assay performance in terms of a coefficient of variation a minimum number of labels must be present on the array. If the
Design targets for other noise sources can be found by making their contributions smaller than the level of biological shot noise. In the following, we’ll assume the array is read out by simply averaging the outputs of all sensors, since this method was also employed in all the measurements. For thermal noise, consider a situation with particles present on a sensor array composed of elements. If the average magnetic field per bead is , the signal then equals and biological shot noise introduces a standard deviation given by . In order for the measurement to be shot-noise limited even at the limit of detection we must have
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Fig. 4. Array and electronics block diagram.
where we assumed different sensors to contribute uncorrelated noise processes each with standard deviation . A signal-tonoise ratio of at least 14 dB per sensor is required for this design. C. Baseline Requirements and Calibration Since using longer averaging time or increasing power dissipation can always reduce thermal noise, baseline and drifts will limit the detector performance. Suppose again there are (each contributing a signal ) on an array with sensing sites; and each sensing site introduces a random baseline error with standard deviation . Using the same reasoning as in the previous paragraph we derive the requirement on the per-sensor signal to baseline ratio . In this case however the requirement depends on the spatial correlation between different error sources. Two special cases are considered here: fully correlated error sources (baseline errors add linearly) and spatially uncorrelated baseline errors (baseline errors add in power). In these two limit cases, the requirements are summarized in (1): fully correlated baseline errors uncorrelated baseline errors
(1)
the For the chosen array size and number of beads required value of ranges from (for uncorrelated baseline errors) to (for correlated baseline errors). These values exceed those in Table I by between 2 to 7 orders of magnitude, so that substantial improvements in circuit and system design are required to achieve the specifications in (1).
To simplify the problem, we perform calibration by measuring the offsets of each chip during the assay, immediately before the magnetic labels are introduced (i.e., between steps 2 and 3 of Fig. 1), and subtracting them from following measurements. Previous equations still apply; with the correction that and now refer to the offset’s drift after calibration, as determined by environmental factors such as temperature, stress or humidity fluctuations. Since single-point calibration reduces the offset but not its drift, the value of is still important. If we focus on temperature variations for example, and assume several detectors have baseline signals with the same temperature coefficient [10] but different values of , we get an idea of the environmental fluctuations tolerable. If , the maximum allowed temperature drift during the measurement is for (1) to hold. relaxes the requirement to 0.4 K and results in a . The system presented targets a pre-calibration signal-to-baseline ratio and consequently a temperature range of . The design techniques utilized to achieve such performance are described below. D. Optimal Magnetization Frequency for Relaxation Measurement The measurement utilizes an alternating magnetizing field to isolate the particle response from low frequency environmental interferers such as the earth magnetic field, magnetic field from neighboring current-carrying conductors and electronics noise [8]. The frequency of this magnetic field must be selected such that the signal-to-noise ratio is maximized. A magnetic label typically contains between (for 1 m particles) and (for 4.5 m particles) magnetic domains with diameter immersed in a polymer matrix that suppresses the mutual interactions and provides overall paramagnetic behavior. When
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Fig. 5. Readout channel schematic.
all magnetic domains are identical, we refer to the particles as monodisperse, whereas in cases where the physical (and magnetic) properties of each domain are slightly different, the particles are referred to as polydisperse. The time-dependent magnetization of a magnetic domain of volume when an external field is applied for a long time and then suddenly switched off at time is given by [15] (2) (3) where is the material magnetic anisotropy, its volume susceptibility and . The particles used in this experiment and in [10] have and at room temperature. Similarly, when an initially non-magnetized domain is suddenly subject to the external field , its magnetic moment responds as
An optimal detector will perform a matched filtering operation by correlating the measured magnetic field with the expected , and subsequently average multiple measurements together to further boost the SNR. For a fixed total measurement time , the number of measurements that can be performed is proportional to the magnetizing frequency . The product of the per-measurement SNR with the magnetization frequency gives then the appropriate figure of merit. Assuming the noise density is independent of magnetizing frequency (an accurate assumption as long as the magnetizing frequency is above the electronics noise corner frequency ) , this is equivalent to maximize the quantity defined below:
The results of such optimization (normalized to the peak value of F for magnetization measurements) are shown for monodisperse particles in Fig. 3 ( ). For magnetization measurements, the best performance is always obtained by minimizing the magnetization frequency. In practice, the frequency would simply be chosen to be higher than the noise corner of the electronics. The optimal magnetization frequency for relaxation measurements is instead approximately the inverse of
the decay time constant and and lies in the 1–10 MHz range. At this optimal frequency, the signal is reduced by 4 compared to the maximum obtainable in magnetization. Practical particles are composed of magnetic domains with a distribution of diameters following a log-normal law [16]. For this reason, the relaxation loss is higher than the previously calculated factor of 4. For commercially available magnetic particles the optimal drive frequency is approximately 2 MHz (with reference to Fig. 3, ) and the average magnetic moment is about 1/8 of its maximum value (2 lower than for ideal mono-disperse particles). Furthermore, for poly-disperse particles the time-domain response is not well modeled by a single-pole exponential function [16]. For this reason, the IC does not perform correlation but simply integrates the decaying magnetization shape, suffering an in return for drastically simplified electronics (black curve in Fig. 3). III. READOUT ELECTRONICS The Hall sensors employed in this design are based on those already described in [10], and introduce a noise floor close to 100 nT/rt(Hz). As a result, achieving a signal-to-noise ratio in excess of 14 dB on a 1.4 signal (corresponding to the average relaxation field of a single 2.8 m particle) will require a measurement time in excess of . For the 10,240-element array at hand, scanning each sensor sequentially would result in an unacceptably large total measurement time of over 20 minutes. To reduce this figure, we employ column-parallel electronics, reducing the array readout time by 160 to approximately 10 seconds. Fig. 4 shows a high-level diagram of the entire system. The chip is sub-divided in two sub-arrays, each comprising a magnetization current generator (implemented as in [10]), a timing generation block, and two blocks of Hall sensors with associated readout electronics. Each readout chain serves 64 sensor cells and contains low noise amplification, averaging and digitization. The acquisition chain was designed to have a width equal to exactly two sensor pixels, forming a scalable layout where electronics are arrayed at the north and south periphery of the sensing area. The circuit used to achieve these functions is shown in Fig. 5, and consists of an auto-zeroed / converter that operates as a synchronous demodulator (referred to as “ / converter” in the following) and provides offset suppression,
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Fig. 6. Timing diagram for A/D converter operation and offset cancellation for the left sub-array. Demodulator phases - . For the right sub-array, magnetization occurs in phases and and readout in phases and . and duty cycle.
followed by a current-mode first order incremental A/D converter. Two time-scales co-exist in the system: fast clocks are used for magnetic modulation and demodulation (signals - , in Figs. 4 and 6) and to clock the oversampled ADC (signal ). Slow clocks are used to read-out the decimated ADC output and operate the digital nested chopping loop ( , see Section III-D below and Fig. 6) as well as to reset the A/D loop filter ( also shown in Fig. 6). In Fig. 6, the 2 MHz-frequency, four-phase, 25% duty cycle clocks control the magnetization generator as it steers current source alternatively to the left and right group of sensors. For example, for the array shown on the left of Fig. 4, phases and correspond to a magnetizing field directed out of the page ( ) and subsequently into the page ( ); while phases and are reserved to perform the relaxation measurement. For the sensor group on the right of Fig. 4, the roles are inverted and field is applied in phases and , while relaxation is observed in phases and . Therefore, in each modulation period, a measurement is acquired from each of the sensor groups, reducing the total readout time by a factor of 2 as in [10]. Clock phases are non-overlapping version of and are used by the first stage of the readout chain, which is connected to the sensors during phases and for the left group of sensor and during phases and for the right group of sensors. The goal of the non-overlap time ( ) is to allow magnetization field applied in phases and to decay before a measurement is taken, preventing it from corrupting the bead relaxation signal in phases and (Fig. 6).
-
are shown in gray together with have respectively 50% and 0.1%
The current-input, continuous-time incremental A/D converter ([30]), which is clocked by the 1 MHz signal, converts its average input current into a 10-bit digital output updated by the 1 kHz (Fig. 6). After nested chopping demodulation (see Section III-D), the ADC outputs are further averaged in software to reduce noise. Additional details about the readout strategy and circuitry are described below. A. Hall-Effect Pixel Implementation The sensor structure is a modification of the one discussed in [10] and is shown in Fig. 7. Each Hall-effect pixel contains a3 4 m Hall effect device implemented with an n-well resistor to maximize mobility and hence transduction gain, as well as access transistors used for addressing, current lines used to generate the magnetizing field, and and ground buses (see Fig. 6) for biasing the sensor. The voltage drop across the sensors is chosen equal to ( ) to maximize carrier velocity and hence sensitivity. The array arrangement is dictated by layout area considerations as well as by the goal of controlling the voltage drop on the and ground lines within each row. The sensor can be modeled as a Wheatstone bridge with nominal resistance value . [17] The measured sensitivity to magnetic field is 50 mV/T, while the measured offset standard deviation is 6 mV (120 mT) [10]. As in [13], a RIE etch is used to thin down the metal stack in order to reduce the distance of the active surface from the sensor from an initial 12 m down to 3 m. The etch is applied uniformly across the entire sensing area to preserve a flat surface and maximize the effectiveness of washing steps that are integral part of most immunoassay protocols. Since the magnetic
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TABLE II SOURCES OF BASELINE AND MITIGATION TECHNIQUES ADOPTED (RESIDUAL BASED ON MEASURED DATA FOR MAGNETIZATION).
and polarization lines are carried out in metal 1 while the sensor outputs are routed vertically using polysilicon and incur significant series resistance (18 ). B.
Fig. 7. Annotated Hall-effect pixel: top view (top) and cross-section including 4.5 m bead (bottom).
field from the particle decays as the third power of this distance, this etch increases the per-label signal by over 10 ; however, it also means that any routing in the array must be carried out using either metal 1 or poly. Horizontal connections for ,
/ Converter
Since the proposed relaxation measurement greatly attenuates the magnetic baseline, the main challenge in the design of the electronics is achieving low flicker and thermal noise and low power consumption while suppressing interference from the Earth’s magnetic field, EMI and sensor’s electrical offset. Table II lists the residual interferers that must be suppressed as well as the techniques used to reduce their impact. The sensor’s electrical offset is by far the strongest (400 mT vs. 43 for the Earth’s magnetic field), and hence requires most of the design attention. The signal of interest is modulated at a 2 MHz frequency by the on-chip magnetization generator so that the sensor offset can be filtered out. However this term must be removed prior to amplification or demodulation to prevent saturation or large ripples. In [10], this is achieved using a multi-stage amplifier with a 16-bit distributed D/A converter, with the input to the D/A converter controlled by software. This comes with a noise penalty due to the extra noise current generated by the D/A converter [18] as well as the un-balanced operation of the first amplified stage, which cannot operate with low overdrive to preserve linearity. The offset cancellation scheme used in this work is shown in Fig. 8. It exploits the 4-phase clock used for magnetic relaxation to perform (almost)1 noise-free auto-zeroing and boxcar sampling [19]. During phase , positive magnetization field is applied to the sensor by driving current through the magnetizing lines. Concurrently, the Hall sensor produces an output consisting of its electrical DC offset, the magnetization baseline, and the magnetization signal from any label present on 1There is indeed a residual noise penalty associated with auto-zeroing ([31]) and is never identical and as a result because the duration of noise is not completely eliminated. For this work, this contribution can be shown to have a PSD of approximately 60 pV/rt(Hz), and is hence negligible.
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Fig. 8.
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- converter operation, from top to bottom
(Auto-zero),
(First integration),
the surface above the sensor. During this period, the / converter is disconnected from the A/D converter and auto-zeroed, storing all the aforementioned error terms on the AC coupling capacitor . At the end of , the auto-zero switch is opened, and further error terms and due to sampling noise and charge injection are stored on . During the magnetization field is removed and the decay from the magnetic beads is measured by the Hall sensor, generating voltage at the input of the / converter in Fig. 8. The / converter is connected to the ADC through the direct path of the chopper CH1 during . The rising edge of is delayed by approximately 2 ns from the falling edge of to allow the magnetization signal to decay (with time constant [10]) before the measurement is initiated. The signal appearing at the sensor output is then integrated on the ADC loop filter. In this phase, residual error terms are integrated along with the signal, which are respectively due to:
(idle) and
(second integration).
1) The un-compensated magnetization signal measured by the Hall sensor during and stored on by the auto-zeroing operation; 2) The residual offset of the / converter stage after autozeroing, given by its initial offset reduced by its open loop gain ; and 3) The un-attenuated error voltages due to charge injection and sampling noise from the auto-zeroing switch. The total output current ( ) in phase 2 reads ( Fig. 8) where is the sum of all the above error terms. At this stage is 100–1000 bigger than the desired signal and must therefore be further suppressed. This is achieved by repeating the measurement in and (and ) using opposite magnetization field polarity. To preserve errors unchanged from , the amplifier is disconnected from the A/D during and the ac-coupling capacitors are not refreshed in this time. Instead, the / converter’s outputs are shorted to-
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gether to prevent drifts and memory effects. At the same time, an inverted magnetizing field (i.e., out of plane negative) is applied by reversing the direction of current flow through the magnetizing wires. As a result, when in the / converter is connected again to the A/D loop filter, this time using the inverting path of chopper CH1, the error sources 1–3 above are integrated a second time with opposite polarity, and hence to first order eliminated. In practice, the amount of suppression achievable is determined by the difference between the duration of and that of , which at 2 MHz can easily be by careful clocking design. Since the magnetization field in is reversed, the signal from the bead is added constructively. The schematic of the / converter is shown in Fig. 9. A current reuse stage with both top and bottom current source bias is chosen to increase the ratio and the noise efficiency while preserving supply noise rejection. The chosen transistor sizes result in a ratio of 20 for the NMOS devices and 18 of the PMOS devices, leading to an overall . Aggressive biasing is enabled by the auto-zeroing function, which ensures that the input of the / converter is balanced. Common-mode feedback is applied through transistor and by degenerating the top-side current source [25]. The non-linearity and differential-mode to common-mode cross talk of this architecture are avoided as the / converter’s output is connected to a differential-mode AC ground. The area of input transistors is chosen based on a trade-off between area and noise (and hence power) efficiency. A large channel length for the input transistors reduces their noise and hence lowers the post-chopping white noise floor [19]. However, this demands both a larger area for the transistor, as well as a larger value and area for in order to prevent significant voltage loss from the parasitic at the gate of the / converter during and . For the chosen sizing, the simulated corner of the amplifier is 830 kHz, and the gate parasitic is . is set to 1.1 pF and realized with a custom capacitor consisting of poly-poly cap stacked with custom M3-M5 finger capacitor to minimize area. The chopper is implemented with minimum-size transistors to minimize charge injection mismatch. The overall transconductance of this stage is 1.2 mS, while the loop filter integration capacitor is set to 200 fF obtaining a conversion gain to the integrator output of: (4)
C. Noise Analysis The preamplifier is the largest contributor the system inputreferred noise. We will now analyze its contribution in detail. Referring back to Fig. 5, the comparator in the ADC loop filter samples a signal proportional to the net charge integrated over the preceding clock period. If we call the comparator input voltage (Fig. 5), and assume the clocks are ideal 25% duty cycle waveforms, we have
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Expressing the results in terms of input-referred noise voltage and transconductance , and remembering that the circuit is auto-zeroed in , we can also write , . Since as mentioned above , ; however the following holds for any value of . We have
Recognizing that
is a constant we obtain (5)
To first order therefore the auto-zero in phase 1 has no effect on the noise performance of the system. To calculate the variance of , we realize that effectively is first chopped (and hence its PSD whitened) and then sampled through windowed integration. Recalling that the integral of white noise is a random walk process with variance proportional to the integration time [21] and using results from [20] we find that (6) where is the white input referred noise PSD of the stage, and is its flicker corner. Using the expression for gain (4), the input-referred noise variance and power spectral density are respectively
(7) where the PSD is white because after chopping, the noise current is to first order white as well. Before chopping, the / converter has an input-referred noise density and corner frequency , and the calculated input-referred noise density using (7) is 10.55 nV/rt(Hz). The simulated input-referred noise density of this / converter after chopping at 2 MHz ( ) is 10.8 nV/rt(Hz) (216 nT/rt(Hz) after referring to the sensor input) while consuming 80 from a 1.8 V supply. This corresponds to a noise-efficiency factor [22] of 3.5. The factor of 2 in the (7) above is caused by the fact that the RMS value of the chopping waveform is ½ and not 1 as in conventional chopping. In other words, the signal is observed and averaged over only 50% of the clock period. If the signal were available during 100% of the clock period, the longer averaging time could result in a 2 gain in SNR. Finally note that the thermal noise from auto-zeroing in has a PSD of (8) It is hence vital to cancel this noise contribution through double integration.
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D. A/D Converter and Nested Chopping The A/D converter employs a first order, continuous-time incremental modulator with current input. A critical advantage of this architecture is that the integrator in the loop filter not only provides quantization-noise shaping, but it also accumulates the charge produced by the / converter during each 500 ns modulation period over the 1 ms ADC readout period, effectively reducing the input-referred noise. Since no separate charge accumulator is required, this reduces the interface component count, allowing a more compact implementation. The ADC operates on a 2 divided clock compared to the / converter ( ) and produces an output digital word at 1 kS/s, providing a nominal 10-bit resolution. The comparator sampling instants occurs on the rising edge of signal , which rises a short time after , when the output of the / converter is disconnected from the converter input, and the errors from the previous phases have already been canceled. The converter offset, largely determined by the OTA used in the loop filter, can be as large as 500 when referred to the input of the Hall sensor. The ADC also limits the residual system input-referred flicker noise corner to 1 kHz. This offset and its drift are suppressed by a nested chopping loop with digital demodulation ([23], [24]) that operates by exchanging the sign of the magnetization field in and after each A/D sample is taken, effectively up-modulating the bead signal with a 1 kS/s square wave (Fig. 6, signal ). The signal is demodulated subtracting consecutive A/D output words in the digital domain, removing residuals noise and offset, including the contribution of the A/D converter itself. The field polarity is switched in a silent clock phase (signal in Fig. 6), which is also allocated to resetting the integration capacitor. As a result, to first order no nested chopping transients are observed. Because the offset is suppressed only after digitization, the A/D converter is designed to have a full scale to prevent residual offsets from saturating this block prior to demodulation. The corresponding quantization noise has an input-referred PSD of 52 nT/rt(Hz), well below the / converter thermal noise. The 1-bit D/A converter is realized with a complementary structure, where both the top and the bottom current sources are switched, leading to a 2 reduction of thermal noise (Fig. 10). The full-scale current of this DAC is set to 50 nA and as a result its thermal noise, which directly adds to the noise contributed by the / converter, is negligible, while its 1/f noise is suppressed by the nested chopping loop. The OTA used in the integrator uses a folded-cascode topology with extra-cascode devices in the input path to provide 75 dB of DC gain and ensure effective averaging over 1000 samples. Its current consumption is set to 80 to provide a low effective input impedance to the / converter, thus minimizing signal-dependent charge injection from CH1 without resorting to a two-stage design. Common-mode feedback is realized with a switched-capacitor network [25] clocked at 250 kHz. A 14-bit counter and shift register provides decimation and data output serialization. E. Arraying and Peripheral Circuitry The area occupied by each readout channel is 16 750 m, with the / converter and the ADC occupying each 16 225 m while the decimator and serializer taking up the
Fig. 9.
-stage schematic with annotated transistor sizes.
Fig. 10. Circuit Schematics of A/D Converter building blocks: D/A ( left) and OTA (right, bias not shown).
remaining 16 300 m. Since the sensors’ horizontal pitch is 8 m, readout channels are placed on both the north and the south peripheries of the array. Channels on the north periphery are connected to even columns in the sensor array, while channels on the south periphery are connected to odd columns. The polarization field generator separates horizontally the matrices ( m distance in the horizontal direction) so that the magnetizing field leakage from one area to the other is lower than 10 nT and can be safely ignored. Each of the eigth readout blocks contains 20 acquisition chains and has its dedicated bias generation block comprising common-mode reference generation and three separate current DACs to set the bias current of the / converter, the ADC integrator and the feedback DAC independently. A single reference bias current is supplied from off-chip, replicated by a current mirror and routed to these bias blocks, while all within-array distribution occurs in voltage domain. All clock phases are generated from a single master using synchronous division. These dividers are also distributed , with a total of 4 clocking generation blocks on the whole chip. A ring-divider is utilized to minimize timing inaccuracy in the critical - signals. The output signals of this ring divider are
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fed to a pulse-shaping circuit that introduces a programmable non-overlap time before feeding the readout chain, and to a second logic block that introduces make-before break timing before driving the magnetizing current source. Further synchronous dividers generate the remaining clock signals for the incremental A/D, nested chopping and data serialization. F. Residual Offset Sources The residual baseline sources in the system are due to the finite suppression of magnetization field, sensor and A/D converter offset. The residual offset due to magnetization is due the imbalance between the duration of and that of , ; as well as the finite decay rate of the magnetization field during non-overlap time. Referring to this non-overlap time as and the time constant of the field decay in the magnetization coil as , we can write
In this implementation , , so the field is suppressed by a factor of 20,000. The timing error is random in nature and simulations indicate . The expected residual baseline is therefore of the order of 0.75 . These errors are not mitigated by the nested chopper loop, and so they are only reduced through calibration in this work. The sensor and electronics offset is suppressed first by the auto-zeroing action of the / converter, and subsequently by the double integration and the second level of chopping. Since the Hall sensor offset is added before the AC-coupling capacitors, the residual error is only limited by leakage and can be assumed to be zero. Before applying the second level of chopping we can write
where and are the input referred offset of the / converter and ADC respectively, and is the offset introduced by the switching action in the first chopper. In this design, the first term is approximately 500 nT and can be ignored. is determined by the parasitic capacitance at the output of the / converter, ,which has a value of approximately 100 fF. During the auto-zero phase , is charged to the input referred offset of the -stage, storing a charge . During , the outputs of the / converter are connected to the virtual ground of the integrator, and this charge is transferred to the integration capacitor. (9) Lastly, while charge injection mismatch in CH1 also contributes to offset, this effect only adds approximately 1.5 for this design and so is negligible; as a result . After the second level of chopping, we obtain
Fig. 11. Die photograph after post-processing . Removing the TiN liner exposes the circuits. The irregular jagged line around the IC is due to irregularity in the photoresist mask which is applied manually.
where is the offset contributed by the switching transients in the nested chopper, and is the suppression obtained in the second chopper. Since the switching of the nested chopper occurs during a guard phase, . Even for (80 dB rejection from the slow chopper), the expected residual offset from the electronics is approximately 60 nT, much smaller than the residual due to magnetization. As a result, we expect the measurement baseline to remain dominated by magnetization. IV. ELECTRICAL MEASUREMENT RESULTS The chip was fabricated in a 2P6M 0.18 m CMOS process and occupies 5.1 mm 3.5 mm (Fig. 11). For electrical characterization, all chips were housed in standard 28-pin DIP packages after performing RIE in the UC Berkeley Nanolab for metal-stack thinning., The bond-wires were subsequently covered with epoxy to prevent contact with fluid. Under typical operating conditions, with only 5120 sensors active, the chip consumes 153 mA from a 2 V supply. Over half the current is consumed by the Hall-sensors (one row, comprising 80 sensors, is on at a time, consuming a total of 88 mA). The magnetization generator consumes 32 mA, and the readout electronics contribute the remaining 33 mA. Activating all the 10240 sensors the power consumption increases to 273 mA. The response of the chip to directly applied magnetic fields was evaluated by changing the bias current of the on-chip magnetization current generator and is reported in Fig. 12. Finite-element simulations were used to determine that the average field across the sensor area is 2.6 mT when the magnetization current is 32 mA. The measured transfer curve is linear for applied magnetic fields below 2 mT and indicates a saturation field of . In the linear region, the relationship between ADC output and magnetic field is well modeled by the relationship , hence the system transduction gain is 480 LSB/mT. A plot showing the residual baseline of the microsystem averaged across sensor rows is shown in Fig. 13. This measurement was repeated with a 20 mT permanent magnet in contact with
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Fig. 12. Transducer gain.
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Fig. 14. Measurement baseline as a function of magnetizing field.
TABLE III SIGNAL, BASELINE AND SIGNAL TO BASELINE RATIO FOR SEVERAL COMMERCIAL BEADS USING AMPLITUDE AND RELAXATION MEASUREMENTS.
Fig. 13. Measurement baseline as a function of array location.
the package, finding no measureable difference and thus verifying the DC magnetic field rejection. The bathtub shape can be explained observing that the offset increases in south-to-north direction (i.e., offset increases as we move from row 64 to row 1) for even columns and north-to-south direction on odd columns. This is due to the distributed RC delay of the sensor access line, which effectively reduces the non-overlap time. For the current implementation, this distributed delay can be as large as 0.5 ns. We can also observe that the magnetic baseline is not perfectly symmetric about row 32. The cause of this second asymmetry is not known at the time of writing. The mean offset (measured across the entire sensor array for 16 chips) is 7 , an over 300 reduction from the 2.6 mT magnetization baseline, but significantly larger than the calculated value. The standard deviation of the offset (measured across the same 16 samples) is 1.7 . This discrepancy in the average offset is also thought to be due to the distributed RC delay of the sensor access lines. Fig. 14 shows the dependence of this value on magnetization bias current. The relationship is approximately linear and confirms that the residual is due to incomplete decay of the magnetization field during the non-overlap time or to timing mismatch. Since the residual offset at zero magnetizing field is 0.7 , an approximate 8 improvement in signal-to-baseline ratio can be obtained by using a slightly larger
Fig. 15. Channel offset prior to nested chopper demodulation.
no-overlap time to account for the distributed delay, obtaining the desired signal-to-baseline ratio of (See Table III). The raw digital output from the A/D converter (prior to nested chopping demodulation) is shown in Fig. 15 and confirms that this error is always within , smaller than the 2.5 mT system full-scale. Fig. 16 shows the measured magnetic field as a function of the number of beads present in the sensing area, obtained from summing the output of all active sensors, for several kinds of commercially available beads. The measurement was performed by applying serial dilutions of bead stock solutions, allowing them to dry, and comparing an optical count of the beads performed under a microscope with the magnetic field measured by the
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Fig. 16. Measured magnetic field as a function number of magnetic labels.
Fig. 18. Measured noise floor dependence on sensor row.
Fig. 17. Baseline temperature dependence normalized to signal from a 2.8 m bead for relaxation (left) and magnetization (right) measurements.
Fig. 19. Noise floor dependence on A/D conversion time.
chip. A linear function was the fitted to each measurement ( ). Table III summarizes the measured baseline and per-bead signal and compares the results to those obtained for magnetization detection using the same chip. The use of magnetic relaxation guarantees an over 50 improvement of the signal-tobaseline ratio when compared to magnetization detection. The measured temperature dependence of the baseline is shown in Fig. 17 for 2.8 m particles. In relaxation, the temperature coefficient of the baseline is 0.35%/K. For amplitude measurements, under the same conditions the baseline change is 0.4%/K. The signal-baseline-ratio is however improved from 0.5% in magnetization to 14% in relaxation. The similarity in the temperature coefficient further confirms that the residual offset measured in relaxation is due to incompletely decayed magnetization field. Fig. 18 shows the input-referred noise of the system across the sensors in an odd column. The system input-referred noise is 260 nT/rt(Hz) in the magnetic domain, which is slightly higher than the simulated value of 250 nT/rt(Hz). Of this 215 nV/rt(Hz) comes from the electronics , 100 nV/rt(Hz) from the Hall sensor and access transistors, and up to 120 nV/rt(Hz) from the polysilicon access lines (in the case of sensor at the top periphery of the array and electronics at bottom). The increase in input referred noise is caused by the larger contribution of the polysilicon resistance as the distance from the electronics is increased.
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In Fig. 19, the input referred noise of the system is shown as a function of A/D converter averaging time. A number of Comparator Decisions per Conversion (CDC) greater than 120 ( ) is required to obtain thermal noise-limited measurements. For lower CDC, input referred noise has an dependence, indicating quantization-limited behavior, while for , good fit is obtained with an shape, confirming that at the design point , the system is thermal noise limited. We characterized the minimum detectable signal of an individual sensor by taking a long (8 hrs) measurement and calculating Allan deviation [26] shown in Fig. 20. This results in 9 nT for relaxation measurement. For magnetization measurement, Allan deviation cannot be directly measured, since the baseline is actually larger than the system full scale. We performed a measurement after reducing the magnetization field by a factor of 5 and obtained an Allan deviation of 130 nT. The corresponding maximal averaging times (for a single sensor) are respectively 20 minutes and 10 s. Table IV reports the minimum obtainable standard deviation of a measurement in which, for each bead type, a sufficient number of sensors is chosen to satisfy the biological shot noise requirement. In such a measurement, spatial averaging reduces thermal noise from different sensors, whereas drift can be assumed to be equal for all sensors (since they all share the same magnetization subsystem). The measurement time is chosen so as to operate at the edge of drift-limited measurements. Because
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TABLE V SUMMARY AND COMPARISON WITH [10]. THE SIGNAL-TO-BASELINE RATIO VALUE FOR [10] IS REPORTED FOR A BEAD LOCATED IN THE CENTER OF THE SENSOR. READOUT TIME IS CALCULATED FOR 2.8 m PARTICLES FOR THIS WORK AND 4.5 m PARTICLES FOR [10], FOR 4096 SENSORS.
Fig. 20. Allan deviation for magnetization and relaxation measurement.
TABLE IV ARRAY NOISE FLOOR IN NUMBER OF BEADS. MEASUREMENT TIME IS FIXED AT 19 SECONDS IN ALL CASES.
biological shot noise is 20 beads ( ). With single-point calibration, relaxation based measurements are always biological shot-noise limited. In Table V, this work is compared with [10]. Thanks to the adopted parallel readout interface, sensing area and number of sensing sites are increased by almost 2 orders of magnitude, while measurement time is reduced by 8 . This combination enables rapid testing over an area that is sufficiently large to mitigate biological noise. As far as signal-to-baseline ratio is concerned, [10] obtains a value of 5 when the beads are in center of the sensor. Under the same conditions, the sensors in this array have signal-to-baseline ratios as high as 9.5 for the best performing devices, which have baselines as low as 2 . Since our measurements average together the outputs of all sensors Table V uses instead spatially averaged values of bead signal (1.7 lower than for beads located in the center of the sensor) and sensor offset (See Table III) to obtain a more representative value of 1.6 for S. This figure is over 50 better than for detectors utilizing magnetization measurement. V. ASSAY RESULTS
of the above mentioned spatial averaging of thermal noise, this is achieved with a readout time of 19 seconds for the entire array. The temperature window over which the measurements remain limited by biological shot noise according to (1) is also indicated in Table IV, and varies between for 4.5 m particles to 0.4C for 2.8 m labels. At the detection limit, the error due to
To finally verify the functionality of the sensor in an assay context, a reverse phase direct assay for Human Serum Albumin was performed. The assay used 2.8 m Dynal beads as labels. The sensor surface was first functionalized with a known concentration of Human Serum Albumin (HSA) using physical adsorption. After blocking the surface with 1% Bovine Serum Albumin (BSA), anti-HSA antibody coated magnetic beads were added and incubated for 2 min. After incubation, non-specifically bound beads were removed with external magnets. The measured output from the magnetic sensor is shown as a function of HSA concentration in Fig. 21. The figure also shows optical images of the chip surface after the washing step for each of the concentrations reported. The 1 ng/ml case has no residual
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Fig. 21. HSA assay using 2.8 m labels: measurement results and substrate photos after washing; chip output is normalized to ADC full scale.
specifically bound beads after washing, due to excessive magnetic force. We can estimate the performance of this detector in an assay using the results from [27], which reports that for PSA (Prostate Specific Antigen, a prostate cancer biomarker) on a glass substrate, the number of specifically bound 2.8 m beads as a function of concentration follows the relation
When compared to detectors that assume that labels are paramagnetic, we obtain a 300 reduction in measurement baseline, as well as over 50 improvement in the signal-to-baseline ratio. While our demonstration utilizes Hall-effect transducers to minimize cost, the techniques presented in this paper can be employed with other types of magnetic sensors, such as GMR devices. ACKNOWLEDGMENT
where [PSA] is expressed in ng/ml. At the biological shot-noise induced detection limit of , . A standard deviation of 24 beads (based on our measurements reported above), translates in a standard deviation in the concentration of 200 pg/ml, giving a total measurement . These metrics are sufficient for cancer screening, where [PSA] values below 4 ng/ml are considered normal [28]. A similar sensitivity would also be sufficient for early detection of myocardial infarction through Troponin-I testing, where elevated values are in 1 ng/ml level, while resting values are below 200 pg/ml [29]. VI. CONCLUSIONS Exploiting the super-paramagnetic nature of labels in detector design enables ideally baseline-free measurement of magnetic label concentration, greatly improving system robustness. The microsystem described in this paper requires only single point calibration to measure the concentration of sub-1 m diameter particles in less than 20 seconds and has a sensing area of 0.6 , sufficient to overcome biological shot noise. The fast readout time is enabled by a column-parallel readout interface that combines nested chopping and auto-zeroing to minimize noise and drifts. The proposed system and readout architecture achieve comparable signal-to-baseline to the proof-of-concept system [10], while demonstrating detection over an almost 100 larger area, and not requiring any external component.
The authors wish to thank Texas Instruments (formerly National Semiconductor) for CMOS fabrication and Berkeley Design Automation for access to the AFS platform. They further acknowledge Prof. R. Mathies, Prof. B.E. Boser, R. Muller, I. Izyumin, E. Alon and M. Megens of U.C. Berkeley, and S. Kavusi of Bosch RTC for technical discussions, and the anonymous reviewers of this manuscript for their valuable feedback. REFERENCES [1] F. Myers and L. Lee, “Innovations in optical microfluidic technologies for point of care diagnostics,” Lab on a Chip, no. 8, pp. 2015–2031, 2008. [2] S. Miltenyi, W. Muller, W. Weichel, and A. Radbruch, High Gradient Magnetic Cell Separation with MACS Cytometry, vol. 11, no. 2, pp. 231–238, 1990. [3] H. Wang, A. Hassibi, A. Scherer, and A. Hajimiri, “A frequency-shift CMOS magnetic biosensor array with single-bead sensitivity and no external magnet,” in IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, 2009, p. 438,439. [4] D. R. Baselt, G. Lee, M. Natesan, S. Metzger, P. Sheehan, and R. Colton, “A biosensor based on magnetoresistance technology,” Biosensors and Bioelectronics, vol. 13, no. 7–8, pp. 731–739, Oct. 1998. [5] D. Hall, R. S. Gaster, T. Lin, S. J. Osterfeld, S. Han, B. Murmann, and S. X. Wang, “GMR Biosensor arrays: A system perspective,” Biosensors and Bioelectronics, vol. 25, no. 9, pp. 2051–2057, May 2010. [6] D. Hall, R. Gaster, S. Osterfeld, K. Makinwa, S. Wang, and B. Murmann, “A 256 channel magnetoresistive biosensor microarray for quantitative proteomics,” in IEEE VLSI Circuits Symp. Dig. Tech. Papers, Jun. 2011, pp. 174–175. [7] M. Megens and M. Prins, “Magnetic biochips: A new option for sensitive diagnostics,” J. Magnetic Materials, vol. 293, no. 1, pp. 702–708, May 2005.
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Simone Gambini received the Laurea Specialistica degree from University of Pisa and Sant’Anna School of Advanced Studies in 2004, and the Ph.D. from U.C. Berkeley in 2009, all in electrical engineering. His doctoral research was developed at the Berkeley Wireless Research Center and was partially funded by an Intel Fellowship. In 2010, he was with Telegent Systems, where he designed RF circuits for highly integrated mobile TV tuners. From 2011 to 2012 he was affiliated with the Berkeley Sensor and Actuator Center, where he conducted research on biosensors and MEMS interfaces. He is currently a Lecturer with the Electrical Engineering Department of the University of Melbourne (Australia). His research interests are the application of low-power electronics to create microsystems integrating sensing and communication, and the development of circuit design techniques for post-CMOS devices.
Karl Skucha is currently completing the Ph.D. in electrical engineering with a minor in management of technology at UC Berkeley, where he also received the BS and MS degrees in electrical engineering in 2006 and 2009, respectively. He is an Intel Robert Noyce Fellow and his doctoral work is focused on CMOS-integrated magnetic particle detectors for diagnostic applications.
Paul Peng Liu received the B.S. degree in microelectronics from Peking University and the M.S. degree in electrical engineering from North Carolina University. He is currently pursuing the Ph.D. degree in electrical engineering at the University of California, Berkeley. Prior to joining Berkeley in 2007, he was a staff design engineer with Xilinx Inc. working on high-speed serial link transceiver. His current research interests include CMOS/MEMS sensor and analog/mixed-signal integrated circuit design.
Jungkyu Kim received the Ph.D. degree in biomedical engineering from the University of Utah, Salt Lake City, in 2009. He is currently a Postdoctoral Researcher with the Department of Chemistry and Bioengineering, University of California, Berkeley. He has been working in the area of cell/tissue engineering, microfluidic sample processing, nucleic acid sample preparation and on-chip amplification, protein microarray, CNT biosensor and magnetic bead labeled immunoassay. Dr. Kim has received numerous awards, including the Korea Research Foundation (KRF) fellowship, Pierre Lassonde Center fellowship, and the Best Paper Award from the Korea Orthopedic Research Society (KORS).
Reut Krigel received the Bachelor of Sciences degree in electrical engineering from Tel Aviv University, Israel, in 2011. In the same year, she joined the University of California, Berkeley where she served as a research assistant conducting experiments on CMOS biosensors and micromachined piezo-electric ultrasound transducers.
