TY - GEN
T1 - DATA-DRIVEN DESIGN OF HIGH ELECTRON MOBILITY TRANSISTOR DEVICES USING PHYSICS-INFORMED GAUSSIAN PROCESS MODELING
AU - Renteria, Anabel
AU - Xu, Yanwen
AU - Hamdan, Bayan
AU - Li, Zhou
AU - Cordero, Sergio
AU - Senesky, Debbie
AU - Wang, Pingfeng
N1 - Publisher Copyright:
© 2023 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2023
Y1 - 2023
N2 - High electron-mobility transistors (HEMTs) have emerged as an attractive alternative for high-efficiency power systems, due to their good material properties to perform at high voltages, temperatures, and frequencies. For that reason, design optimization of HEMTs becomes imperative to ensure the quality and capability of the device when in service. There have been models derived from experimentation to guide the design of HEMTs. Nonetheless, due to its expensive manufacturing process, the relationship of the temperature channel with respect to the design parameters has not been investigated thoroughly. This paper presents a multiphysics finite element (FE) simulation to predict the HEMT's device maximum channel temperature when varying different design parameters. Furthermore, Gaussian Process (GP) based surrogate model was developed using the simulation results as the training database with adaptive sampling techniques for the optimization process. The proposed high-fidelity surrogate model effectively predicts the channel temperature of the HEMT device and enables an optimum search over the design space.
AB - High electron-mobility transistors (HEMTs) have emerged as an attractive alternative for high-efficiency power systems, due to their good material properties to perform at high voltages, temperatures, and frequencies. For that reason, design optimization of HEMTs becomes imperative to ensure the quality and capability of the device when in service. There have been models derived from experimentation to guide the design of HEMTs. Nonetheless, due to its expensive manufacturing process, the relationship of the temperature channel with respect to the design parameters has not been investigated thoroughly. This paper presents a multiphysics finite element (FE) simulation to predict the HEMT's device maximum channel temperature when varying different design parameters. Furthermore, Gaussian Process (GP) based surrogate model was developed using the simulation results as the training database with adaptive sampling techniques for the optimization process. The proposed high-fidelity surrogate model effectively predicts the channel temperature of the HEMT device and enables an optimum search over the design space.
KW - HEMT device
KW - design optimization
KW - multiphysics FE simulation
KW - surrogate model
UR - http://www.scopus.com/inward/record.url?scp=85179137444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179137444&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-117200
DO - 10.1115/DETC2023-117200
M3 - Conference contribution
AN - SCOPUS:85179137444
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 49th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -