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Computer Science > Machine Learning

arXiv:2208.01142 (cs)
[Submitted on 18 Jul 2022]

Title:Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model

Authors:Albert Lu, Jordan Marshall, Yifan Wang, Ming Xiao, Yuhao Zhang, Hiu Yung Wong
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Abstract:In this paper, two methodologies are used to speed up the maximization of the breakdown volt-age (BV) of a vertical GaN diode that has a theoretical maximum BV of ~2100V. Firstly, we demonstrated a 5X faster accurate simulation method in Technology Computer-Aided-Design (TCAD). This allows us to find 50% more numbers of high BV (>1400V) designs at a given simulation time. Secondly, a machine learning (ML) model is developed using TCAD-generated data and used as a surrogate model for differential evolution optimization. It can inversely design an out-of-the-training-range structure with BV as high as 1887V (89% of the ideal case) compared to ~1100V designed with human domain expertise.
Comments: 4 pages, 7 figures
Subjects: Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2208.01142 [cs.LG]
  (or arXiv:2208.01142v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2208.01142
Related DOI: https://doi.org/10.1016/j.sse.2022.108468

Submission history

From: Hiu Yung Wong [view email]
[v1] Mon, 18 Jul 2022 05:55:45 UTC (774 KB)
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