An Intelligently Controlled RF Power Amplifier With a ... - IEEE Xplore

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, VOL. 53, NO. 3, MARCH 2005

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An Intelligently Controlled RF Power Amplifier With a Reconfigurable MEMS-Varactor Tuner Dongjiang Qiao, Member, IEEE, Robert Molfino, Steven M. Lardizabal, Member, IEEE, Brandon Pillans, Peter M. Asbeck, Fellow, IEEE, and George Jerinic

Abstract—This paper presents an intelligently controlled RF power amplifier with a reconfigurable output tuner using microelectromechanical system (MEMS) switches and a varactor. By switching on/off the MEMS switches and varying the bias voltage of the varactor, the performance of the amplifier is optimized for input signals with known or unknown frequencies in a range of 8–12 GHz. Fabrication-related unit-to-unit variations of the amplifier are overcome by the reconfigurable tuner. Directed algorithms based on a characterization table and on black-box genetic algorithms are developed for optimization and search. Index Terms—Genetic algorithm, intelligent RF front-ends, power amplifier, reconfigurable tuner, RF microelectromechnical system (MEMS) switches, tunable matching networks.

I. INTRODUCTION

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DAPTIVE RF front ends provide exciting new opportunities for advancement of microwave systems such as reconfigurable functionality, maximization of performance under varying conditions, elimination of performance variations due to temperature drift and aging, and reduction of the need to tune individual reactive components to meet system specifications [1]–[3]. By changing operating parameters such as center frequency and bandwidth, multifunctional operation can be enabled. Due to characteristics of low insertion loss, broad-band operation, and high isolation, RF microelectromechanical system (MEMS) switches are of great interest for RF applications, particularly in reconfigurable/tunable RF systems, such as antennas, filters, phase shifters, and impedance tuners [3]–[11]. In this paper, a novel power amplifier, with a reconfigurable output tuner using MEMS switches and a semiconductor-based varactor, working over a 4-GHz bandwidth at -band, was demonstrated. The goal of the output tuner is to match a wide variety of loads under conditions of varying center frequency, varying output power, varying output load impedance, varying requirements for linearity, and the performance variations due to temperature drift, aging, and manufacturing tolerances. The output tuner is capable of both Manuscript received April 1, 2004; revised August 23, 2004. This work was supported by the Defense Advanced Research Projects Agency and managed by the Air Force Research Laboratory, Sensors Directorate, Aerospace Components and Subsystem Division, Wright-Patterson AFB. D. Qiao and P. M. Asbeck are with the Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093 USA (e-mail: [email protected]; [email protected]). R. Molfino, S. M. Lardizabal, and G. Jerinic are with the Raytheon Company, Andover, MA 01810 USA (e-mail: [email protected]; [email protected]; [email protected]). B. Pillans is with the Raytheon Corporation, Dallas, TX 75243 USA (e-mail: [email protected]). Digital Object Identifier 10.1109/TMTT.2005.843495

Fig. 1. Block diagram of the intelligent power amplifier.

discrete and continuous tuning to give a large tuning range and a high tuning resolution. Intelligent control algorithms were developed to optimize the performance of the amplifier by varying the output tuner for different input signals. The system can perform functions including searching for an unknown-frequency input; optimizing its output power or power-added efficiency (PAE) or a linear combination of output power and PAE; and reconfiguring its experience tables if malfunctioning parts in the tuner are detected. II. HARDWARE DESIGN Fig. 1 shows the block diagram of the intelligently controlled power amplifier. It comprises a 1.2-mm GaAs pseudomorphic high electron-mobility transistor (pHEMT), an input tuner, 12-bit A/D converters, power sensors, and an output tuner. The GaAs pHEMT is biased under class-AB condition. The input tuner consists of four narrow-band filters to improve the selectivity of the input signals. For comparison, amplifiers with a fixed broad-band input match were also designed. The output tuner consists of four Si MEMS switches, one varactor, and fixed capacitors. A total of 16 MEMS states can be obtained by combining the four MEMS switches. Each MEMS switch can be modeled as a capacitor with “on” or “off” capacitance. The “on” capacitance is approximately 1.5 pF, while the “off” capacitance is approximately 0.035 pF, providing a capacitance of approximately 43. Fig. 2 shows the schematic ratio of the output tuner. During operation, the output impedance can be tuned by switching on/off the MEMS switches. For each MEMS state, the load impedance can be fine tuned by varying

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Fig. 2. Schematic of the two-stub output matching network with four MEMS switches (M1–M4) and one varactor. C1–C8 are fixed capacitors. mlin is a microstrip transmission line.

Fig. 4.

Block diagram of the amplifier in MATLAB/Simulink.

power. It can be seen that, for input power from 5 to 18.5 dBm, the tuner can adjust the load impedance to be very close to the desired value. Similar results were observed for inputs at other frequencies. III. INTELLIGENT CONTROL ALGORITHMS

Fig. 3. Load–pull simulation results of the output matching network. (solid curves) and PAE (dashed curves) load–pull contours of (a) Typical P and the transistor. (b) The required output impedance to obtain maximum P the tuning range of the output tuner at 9.5 GHz. Each curve represents the load impedance of one MEMS state, but scanning the bias voltage of varactor from 10 to 4 V. The dots represent the positions that yield the maximum output power at different input power (5–18.5 dBm).

0

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the varactor bias voltage. The impedance of the output tuner is designed to cover the range of the required impedance to or PAE) of the amplifier for optimize the performance ( inputs with frequencies in the range of 8–12 GHz and input powers up to 25 dBm. This tuning range was calculated by load–pull simulations of the transistor using Advanced Design System (ADS), Agilent Technology, Palo Alto, CA. Fig. 3(a) shows typical load–pull contours of the transistor. The centers and PAE contours usually change for different input of the powers and frequencies. When the input power is relatively large, even for the same input signal, the center of the contour is different from that of the PAE contour. Fig. 3(b) shows the tuning range of the output tuner at 9.5 GHz compared to the load impedance required to yield the maximum output

The objectives of the control algorithms are to optimize the performance of the amplifier in its various operating modes, while minimizing the number of iterations necessary to converge to the optimal result, maintaining stability of the control loop, and maintaining robust operation even if the amplifier or the tuners change characteristics. The algorithms were developed on the basis of simulations using Mathwork’s MATLAB and Simulink along with ADS. Fig. 4 shows the block diagram of the amplifier in Simulink. The transistor, sensors, tuners, and A/D converters are represented by behavioral models. With fixed gate voltage and drain voltage, the behavioral model of the transistor is represented by equations of the form (1a) (1b) is the output power, PAE is the power-added effiwhere is the load reflection coefficient, ciency, is the input filter, is the signal frequency, and is the input power. The behavioral equations were chosen to reproduce, in analytic form, the is calculated load–pull simulation data obtained from ADS. from the MEMS settings and the varactor bias voltage. The controller receives inputs from the sensors, as well as commands from higher level system controllers, and produces outputs to drive the tuners. The core of the controller is a finitestate machine, which is represented using MATLAB and Mathwork’s Stateflow.

QIAO et al.: INTELLIGENTLY CONTROLLED RF POWER AMPLIFIER WITH RECONFIGURABLE MEMS-VARACTOR TUNER

The amplifier has the functions of: 1) optimizing performance (which can be selected to be the output power, PAE, or a predetermined linear combination of output power and PAE) for an input signal with a known frequency; 2) searching for the best performance for an input signal with an unknown frequency and optimizing the setting; and 3) detecting a nonfunctioning part in the tuner and reconstructing a characterization table for a white-box optimization algorithm. The input signals have a frequency in the range of 8–12 GHz. The optimization of the tuners includes tuning both discrete variables (MEMS switches and input filters) and a continuous variable (the bias voltage of the varactor). Simulation of the tuner shows that the nature of the optimization of the tuner is a multipeak problem: different settings may yield the same output and PAE. impedance and, therefore, the same The search algorithm, referred to as the directed search algorithm in this paper, starts with determining some initial test settings from a characterization table. The characterization table is created by a calibration routine, in which the best settings for some inputs with various power and frequency values, as well as the optimized power and PAE results, are stored. These best settings are obtained by sweeping the MEMS settings and varactor bias voltage. The initial settings for a new input are chosen to be those for inputs with similar input power from the table. After getting the initial settings and comparing the value of the ( , PAE, or a linear combination of objective function and PAE), the fine-tuning routine is performed for the best one or two settings. For fine tuning, only the varactor voltage is changed. ADS simulation shows that, for one combination of the MEMS switches, tuning the varactor voltage in a range of 10 4 V only yields one maximum or PAE. To optimize the varactor voltage, the algorithm first determines a bias with , voltage range of where is the objective function, followed by using quadratic interpolation repeatedly until the algorithm converges. Two algorithms were developed for the optimization. The first one, called the directed optimization algorithm, also starts with testing the output for a few initial MEMS and varactor settings selected from the characterization table for signals with similar input power and frequency. This is followed by comparing the outputs obtained and subsequently selecting the best one or two settings to perform fine tuning. The second optimization algorithm is based on a genetic algorithm. Due to unexpected reasons, such as aging, change of temperature, or damage of components in the tuners, the calibration table may not be suitable to provide a good initial guess. This will typically also cause a failure when using the directed optimization/search algorithms. To overcome this problem, the genetic algorithm was used. For the genetic algorithm, the settings of the tuners are encoded into 11-bit-long binary strings (called chromosomes), in which 2 bits are for the input filters (four tuner states), 4 bits are for the MEMS switches (one bit for each MEMS switch, totally 16 combinations), and 5 bits are for the varactor bias 4 V, the resolution for voltage. For a tuning range of 10 the bias voltage is approximately 0.2 V. The algorithm starts by randomly generating a set of trial chromosomes (called popuassociated lation), followed by measurement of the fitness

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was chosen to be with each individual chromosome. Here, , where is the output power measured is the maximum for the specific tuner settings, and available output power of the transistor. The next generation of test chromosomes is generated based on the current generation and the fitness of each chromosome by a series of operations of reproduction, selection, crossover, and mutation in a fashion well established for genetic algorithms [12], [13]. In this series of operations, the first step is reproduction, in which chromosomes in the current generation are selected using a roulette wheel to form an intermediate population with the same population size as the original population. The weight of the th , where is the chromosome in the roulette wheel is total fitness of the current population. After the reproduction is completed, two chromosomes are randomly selected from the intermediate population. With a predetermined probability, each of these chromosomes is then split into two fragments. The fragments of the two selected chromosomes are swapped and recombined to form two new chromosomes. Each bit of each new chromosome is then mutated with a small probability. The new chromosomes after mutation are two members in the new population. The process of selection, crossover, and mutation is repeated until all the chromosomes for the new population are generated. This is followed by measurement of the fitness of the new population. The process of reproduction, selection, crossover, mutation, and measurement is repeated until the best fitness converges or a preset maximum generation number is reached. Since the genetic algorithms combine elements of directed search and stochastic search algorithms, they are more robust than either of these. They usually give several potential solutions [13], while the directed optimization/search algorithms usually just give one solution. However, the genetic algorithms usually exhibit relatively slow convergence. Since the genetic algorithm needs neither detailed knowledge of the tuner behavior, nor information about the input signal, it can be used for both the optimization for a known-frequency input and search for an unknown-frequency input. Since the genetic algorithm generates new test settings only based on the measured performance of previous settings, it can also be used for tuners with nonfunctioning parts. The detection of nonfunctioning MEMS switches is done by flipping the switches and measuring the performance of the power amplifier for the two states. If statistically flipping a switch does not produce any performance difference, the switch is considered to be nonfunctioning. In this case, the characterization table used by the directed algorithms must be reconstructed. Since a signal generator may not be available for recalibration after the integration of the power amplifier into a microwave system, the recalibration is performed based on previous optimization results obtained by the genetic algorithm for known-frequency signals. The results of optimizations using a genetic algorithm are recorded for various input signals to construct a new characterization table, in which the nonfunctioning MEMS switches are excluded. After the reconstruction of the characterization table, the directed algorithms are once again used to reduce the optimization/search time. To measure the performance of the algorithms, an exhaustive search algorithm was also developed, in which the MEMS

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Fig. 5. Benefits obtainable by having tunable matching networks. The dots represent the optimized output power as a function of frequency. Each curve represents the frequency response of the amplifier when it is optimally configured for one specific frequency. TABLE I SIMULATION RESULTS OF THE DIRECTED OPTIMIZATION AND SEARCH ALGORITHM FOR 1000 TEST SIGNALS WITH P = 15 25 dBm, FREQUENCY = 8–12 GHz. THE PASS RATE IS DEFINED AS THE PERCENTAGE OF THE OUTPUT POWER WITHIN 0.5 dB OF THE MAXIMUM AVAILABLE POWER

Fig. 6. Software simulation results of the search algorithm for: (a) known-frequency and (b) unknown-frequency RF input.

states, filters, and varactor voltage are all swept over their entire ranges in order to determine the optimal settings. IV. SIMULATION RESULTS Fig. 5 compares the optimized output power of the adaptive amplifier with that which would be obtained if the tuners were optimally configured for just one specific frequency. It is clear that the amplifier with reconfigurable tuners has a considerable advantage over the amplifiers with fixed matching networks. For the amplifier with reconfigurable tuners, a relatively constant output power was obtained over a frequency range of 8–12 GHz. For the amplifiers with fixed tuners, however, the output power was flat only over a small range. Table I shows the simulated results of the amplifier for known-frequency and unknown-frequency inputs. To obtain reasonable statistics, 1000 runs were made in each case. For the known-frequency case, the directed optimization algorithm converged with 10.9 average iterations and a 0.07-dB average deviation between converged power and the maximum achievable power. For the unknown-frequency case, 19.2 average iterations and 0.12-dB average deviation were found. Fig. 6 shows histograms, which summarize the deviations between converged and maximum achievable powers for the simulations run. Fig. 7 compares the performance of the directed optimization algorithm, genetic algorithm, and exhaustive search algorithm for 200 input cases. For the genetic algorithm, a fixed population of 20–25 was used for each generation. In total, 12–15 generations were used for the optimization. In this study , we found

Fig. 7. P difference between the genetic algorithm, directed algorithm, and exhaustive search algorithm for 200 input signals with P in the range of 15–25 dBm and frequency in the range of 8–12 GHz.

that these values gave a pass rate higher than 99% without requiring an excessively high number of measurements. Fig. 8(a) compares the performance of the algorithms when one of the MEMS switches is stuck at zero (the off state). For this comparison, the original characterization table was still used for the directed optimization algorithm to obtain the initial settings. This figure shows that the performance of the directed optimization (84% pass rate) was substantially degraded. However, the genetic algorithm consistently yielded performance similar to the exhaustive search algorithm (which indicates the best obtainable performance). Fig. 8(b) shows the results of


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Fig. 9. Fabricated (a) overall amplifier, (b) input matching network and power transistor, and (c) MEMS output matching network.

Fig. 8. P difference between the genetic algorithm, directed algorithm, and exhaustive search algorithm. One of the MEMS switch was set to be stuck at zero. The results of directed optimization algorithm were obtained based on the characterization table: (a) generated with all MEMS switches functioning and (b) reconstructed by the genetic algorithm.

the algorithms for the case that the same MEMS switch was stuck at zero, but the directed optimization algorithm used a reconstructed characterization table. The pass rate of the directed optimization algorithm was significantly improved from 84% to 95%. V. EXPERIMENTAL RESULTS Fig. 9 shows a complete fabricated hybrid power amplifier. The algorithm developed in MATLAB was replicated in Labview to control the amplifier through use of a peripheral component interconnect (PCI) card and general-purpose interface bus (GPIB) interfaces. After initial testing, it was found that the input GaAs input filter bank was not needed to assist in selectivity. As a result, all subsequent testing was performed on amplifiers with a wide-band input match rather than those with the input filter bank. Three total units were tested. In addition, due to the hybrid configuration of the test vehicle, parasitic effects shifted the operation frequency band of the amplifier down 1 GHz to 7–11 GHz. For each unit, the calibration routine was performed to create the initial characterization table. The tests were performed on 100 randomly generated input signals with

Fig. 10. Hardware testing iteration counts for one intelligent power amplifier for: (a) known-frequency and (b) unknown-frequency RF input (100 trial cases).

frequency restricted to between 7–11 GHz and input powers from 10 to 21 dBm. The optimization parameter was output power. Fig. 10 shows a sample iteration histogram for one unit tested. For the tests on all three units, 100% convergence was obtained. It was demonstrated that the algorithms converged with an average of 9.7 iterations for a known-frequency input and 15.0 iterations for unknown-frequency input. The average deviation from the optimum power was 0.05 dB in all the tests. The reconstruction of the characterization table to this point has not been tested, nor has the genetic algorithm. Fig. 11 shows the tunability of the intelligent amplifier. It can be seen that, compared to power amplifiers with fixed matching networks, the intelligent amplifier showed the advantage to obtain a relatively constant output power over a wide frequency range. One of the important benefits of having a tunable amplifier is the ability to overcome unit-to-unit variations resulting from variations in areas such as processing and assembly. To accomplish this test, the exhaustive search algorithm was performed on three units at three different drive powers

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from optimum power was 0.05 dB in all the tests, while the input power varied from 10 to 21 dBm. A genetic algorithm has been developed to reconstruct the calibration table for the amplifier if a nonfunctioning MEMS switch was detected, after which the success rate of the directed algorithm could be greatly improved. ACKNOWLEDGMENT The authors are grateful to E. Martinez, Defense Advanced Research Projects Agency (DARPA) for his support of the effort, and to K. Herrick, G. Burnham, and J. Reddick, all of the Raytheon Corporation, Andover, MA, for their collaboration. Fig. 11. Tunability of the intelligent amplifier. Each curve represents the amplifier frequency response when the optimal settings for a single specific frequency are set (see legend). The dots outline the performance envelope of the tunable amplifier.

REFERENCES [1] G. Boeck, D. Plenkowski, R. Circa, M. Otte, B. Heyne, P. Rykaczewski, R. Wittmann, and R. Kakerow, “RF front-end technology for reconfigurable mobile systems,” in Proc. SBMO/IEEE MTT-S Int. Microwave Optoelectronics Conf., vol. 2, 2003, pp. 863–868. [2] C. Goldsmith, J. Kleber, B. Pillans, D. Forehand, A. Malczewski, and P. Frueh, “RF MEMS: Benefits & challenges of an evolving RF switch technology,” in 23rd Annu. IEEE GaAs IC Symp. Tech. Dig., Piscataway, NJ, 2001, pp. 147–148. [3] E. R. Brown, “RF-MEMS switches for reconfigurable integrated circuits,” IEEE Trans. Microw. Theory Tech., pt. 2, vol. 46, no. 11, pp. 1868–1880, Nov. 1998. [4] W. H. Weedon, W. J. Payne, and G. M. Rebeiz, “MEMS-switched reconfigurable antennas,” in IEEE Antennas and Propagation Society Int. Symp. Dig., vol. 3, Piscataway, NJ, 2001, pp. 654–657. [5] K. J. Vinoy, Y. Hargsoon, J. Taeksoo, and V. K. Varadan, “RF MEMS and reconfigurable antennas for communication systems,” Proc. SPIE–Int. Soc. Opt. Eng., vol. 4981, pp. 164–174, 2003. [6] R. Aigner, J. Ella, H.-J. Timme, L. Elbrecht, W. Nessler, and S. Marksteiner, “Advancement of MEMS into RF-filter applications,” in IEEE Int. Electron Devices Meeting Tech. Dig., Piscataway, NJ, 2002, pp. 897–900. [7] B. Pillaus, S. Eshelman, A. Malczewski, J. Ehmke, and C. Goldsmith, -band RF MEMS phase shifters for phased array applications,” in “ IEEE Radio Frequency Integrated Circuits Symp. Dig., Piscataway, NJ, 2000, pp. 195–199. [8] H.-T. Kim, S. Jung, K. Kang, J.-H. Park, Y.-K. Kim, and Y. Kwon, “Low-loss analog and digital micromachined impedance tuners at the -band,” IEEE Trans. Microw. Theory Tech., vol. 49, no. 12, pp. 2394–2400, Dec. 2001. [9] J. Papapolymerou, K. L. Lange, C. L. Goldsmith, A. Malczewski, and J. Kleber, “Reconfigurable double-stub tuners using MEMS switches for intelligent RF front-ends,” IEEE Trans. Microw. Theory Tech., pt. 2, vol. 51, no. 1, pp. 271–278, Jan. 2003. [10] N. Bushyager, K. Lange, M. Tentzeris, and J. Papapolymerou, “Modeling and optimization of RF reconfigurable tuners with computationally efficient time-domain techniques,” in IEEE MTT-S Int. Microwave Symp. Dig., vol. 2, 2002, pp. 883–886. [11] J. Brank, Z. J. Yao, M. Eberly, A. Malczewski, K. Varian, and C. L. Goldsmith, “RF MEMS-based tunable filters,” Int. J. RF Microwave Computer-Aided Eng., vol. 11, no. 5, pp. 276–284, Sep. 2001. [12] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison-Wesley, 1989. Data Structures Evolution [13] Z. Michalewicz, Genetic Algorithms Programs. Berlin, Germany: Springer-Verlag, 1996.

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Fig. 12. Unit-to-unit repeatability of the intelligent amplifier. Three units were tested (differentiated by line type) at three input powers.

( 17, 19.2, and 21 dBm) and nine different frequencies (7–11 GHz in 500-MHz steps) to find the optimal settings for output power. Fig. 12 shows the unit-to-unit repeatability for the three tested units. Nearly identical results were obtained for the three units (as shown by the overlap of the three line types for each power). It is found that the optimal settings at each input power and frequency combination for each unit were not necessarily the same, suggesting that the unit-to-unit variation can be overcome by the reconfigurable tuners. VI. CONCLUSIONS A novel power amplifier with a reconfigurable output tuner using MEMS switches and a varactor has been demonstrated. Intelligent control algorithms were developed to control the tuner. By switching on/off the MEMS switches and varying the bias voltage of the varactor, the performance of the amplifier was optimized over 4-GHz bandwidth at the -band. The unit-to-unit variation was overcome by the reconfigurable tuners. It is demonstrated that the algorithms converged with average 9.7 iterations for known-frequency inputs and 15.0 iterations for unknown-frequency inputs. The average deviation

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Dongjiang Qiao (M’02) received the Bachelor’s degree in materials science from Tsinghua University, Beijing, China, in 1992, the M.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 1995, and the Ph.D. degree in electrical engineering from the University of California at San Diego, La Jolla, in 2002. He is currently a Post-Graduate Researcher involved with RF power amplifiers at the University of California at San Diego.


Robert Molfino, photograph and biography not available at time of publication.

Steven M. Lardizabal (S’88–M’95) received the B.S.E.E., M.S.E.E., and Ph.D. degree in the area of field-effect transistor (FET) noise modeling for monolithic-microwave integrated-cirucit (MMIC) design degrees from the University of South Florida (USF), Tampa, in 1988, 1991, and 1997, respectively. In 1991, he was involved with millimeter-wave noise-measurement techniques as part of the Air Force Office of Scientific Research Fellowship Program. Since joining Raytheon RF Components, Raytheon Corporation, Dallas, TX, in 1997, he has been involved in the research and development of Metamorphic high electron-mobility transistor (HEMT) low-noise amplifier design from 1 to 110 GHz. He has contributed to MMIC design and modeling for programs within the Raytheon Corporation related to Microwave/Analog Front-End Technology-Thrust 2 (DARPA), the Advanced Multi-function RF Systems Critical Enabling Technology Program (Office of Naval Research), and the Intelligent RF Front-End Technology Program (DARPA). He is currently the RF Design Leader of the Raytheon lead Intelligent RF Front End Phase-2 Program.

Brandon Pillans, photograph and biography not available at time of publication.

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Peter M. Asbeck (M’75–SM’97–F’00) received the B.S. and Ph.D. degrees from the Massachusetts Institute of Technology (MIT), Cambridge, in 1969 and 1975, respectively. He is currently the Skyworks Chair Professor with the Department of Electrical and Computer Engineering, University of California at San Diego (UCSD), La Jolla. He was with the Sarnoff Research Center, Princeton, NJ, and the Philips Laboratory, Briarcliff Manor, NY, where he was involved in the areas of quantum electronics and GaAlAs/GaAs laser physics and applications. In 1978, he joined the Rockwell International Science Center, where he was involved in the development of high-speed devices and circuits using III–V compounds and heterojunctions. He pioneered the effort to develop HBTs based on GaAlAs/GaAs and InAlAs/InGaAs materials and has contributed widely in the areas of physics, fabrication, and applications of these devices. In 1991, he joined UCSD. He has authored or coauthored over 250 publications. His research interests are in development of high-speed HBTs and their circuit applications. Dr. Asbeck is a Distinguished Lecturer for the IEEE Electron Devices Society and the IEEE Microwave Theory and Techniques Society (IEEE MTT-S). He was the recipient of the 2003 IEEE David Sarnoff Award for his work on HBTs.

George Jerinic, photograph and biography not available at time of publication.