Design of Terahertz InP pHEMT Using Machine Learning Assisted Global Optimization Techniques

Wang, J., Xue, L.-Y., Liu, B. and Li, C. (2022) Design of Terahertz InP pHEMT Using Machine Learning Assisted Global Optimization Techniques. In: European Microwave Week 2021, London, UK, 02-07 Apr 2022, pp. 67-70. ISBN 9781665447225 (doi: 10.23919/EuMIC50153.2022.9784068)

[img] Text
258729.pdf - Accepted Version



This paper presents an optimal design of terahertz InP-based pseudomorphic high electron mobility transistors (pHEMT) powered by an artificial intelligence (AI) technique. Unlike the traditional physics-based design optimization methods, the new technique employs a machine learning-assisted global optimization algorithm. A state-of-the-art commercial pHEMT operating at millimeter-wave frequencies was used to calibrate the physics-based model. Based on the pHEMT, the proposed machine learning-assisted optimization method was implemented with the constraint of gate length, i.e., 100 nm. The simulation results show significant improvement in terms of cut-off frequency, i.e., 57%, and maximum oscillation frequency, i.e., 30%, compared to the commercial design. To the best of our knowledge, this is the first time to employ machine learning-assisted global optimization techniques to pHEMT design, showing high potential in terms of numerical simulation and device design for ultrafast semiconductor devices.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Liu, Dr Bo and Li, Dr Chong and wang, jing and XUE, Liyuan
Authors: Wang, J., Xue, L.-Y., Liu, B., and Li, C.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Related URLs:

University Staff: Request a correction | Enlighten Editors: Update this record