Small-signal Modeling of InP HBT based on PSO-ELM Neural Network

Jincan Zhang; Yunhang Fan; Min Liu; Jinchan Wang; Liwen Zhang

doi:10.5573/JSTS.2022.22.6.407

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자료유형: 학술저널

저자정보: Jincan Zhang (Henan University of Science and Technology) Yunhang Fan (Henan University of Science and Technology) Min Liu (Henan University of Science and Technology) Jinchan Wang (Henan University of Science and Technology) Liwen Zhang (Henan University of Science and Technology)

저널정보: 대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.22 No.6

발행연도: 2022.12

수록면: 407 - 416 (10page)

DOI: 10.5573/JSTS.2022.22.6.407

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Extreme Learning Machine (ELM) is a new learning algorithm for single-hidden layer feedforward neural network, which has been widely used in lots of fields. However, it still has the insufficiency of randomly determining the hidden layer threshold and output weight, which leads to ill-conditioned output. In order to avoid the risk of decreasing prediction accuracy caused by this possibility, the ELM is optimized using particle swarm algorithm. A Particle Swarm Optimization (PSO) enhanced ELM algorithm is proposed to accurately model the small-signal properties of InP Heterojunction Bipolar Transistors (HBTs). PSOELM algorithm solves the problem of unstable prediction data caused by random determination of input weights in ELM. Comparing the modeling effects of the PSO-ELM model and the ELM model under different bias conditions for a 1 μm×15 μm InP HBT, it is proved that the PSO-ELM algorithm has better consistency with the measured data.

#PSO-ELM neural network algorithm #small-signal model #optimization #InP HBT

참고문헌 (35)

참고문헌 신청

A. Arias-Purdue, P. Rowell, M. Urteaga et al., “A 120-mW, Q-band InP HBT Power Amplifier with 46% Peak PAE,” 2020 IEEE/MTT-S International Microwave Symposium (IMS), pp. 1291-1294, 2020.

Z. Liu, T. Sharma, C. R. Chappidi et al., “A 42-62 GHz Transformer-Based Broadband mm-Wave InP PA With Second-Harmonic Waveform Engineering and Enhanced Linearity,” IEEE Trans. Microw. Theory Tech., vol. 69, no. 1, pp. 756-773, 2021.

Y. Zhang, Y. Chen, Y. Li et al., “Modeling technology of InP heterojunction bipolar transistor for THz integrated circuit,” Int. J. Numer. Model. Electron. Netw. Devices Fields, vol. 33, no. 3, p. e2579, 2020.

X. Jin, M. Müller, P. Sakalas et al., “Advanced SiGe:C HBTs at Cryogenic Temperatures and Their Compact Modeling With Temperature Scaling,” IEEE J. Explor. Solid-State Comput. Devices Circuits, vol. 7, no. 2, pp. 175-183, 2021.

K. Nidhin, S. Pande, S. Yadav et al., “An Efficient Thermal Model for Multifinger SiGe HBTs Under Real Operating Condition,” IEEE Trans. Electron Devices, vol. 67, no. 11, pp. 5069-5075, 2020.

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