Vertical GaN diode BV maximization through rapid TCAD simulation and ML-enabled surrogate model. (December 2022)

• Record Type: Journal Article
• Authors: Lu, Albert; Marshall, Jordan; Wang, Yifan; Xiao, Ming; Zhang, Yuhao; Wong, Hiu Yung
• Is Part Of: Solid-state electronics. Volume 198(2022)
• Publication Date: 2022-12
• Subjects: Power electronics -- Power device -- Breakdown voltage -- Differential evolution -- Gallium nitride (GaN) -- Machine learning -- Technology Computer-Aided Design (TCAD) -- Diode; Semiconductors -- Periodicals; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.sse.2022.108468
• Languages: English
• ISSNs: 0038-1101

Abstract

Highlights: Optimization of GaN Diode Breakdown Voltage through Machine Learning. Simplified and accurate TCAD setup for GaN Breakdown simulation. Inverse design of GaN diode through ML-enabled surrogate model. Abstract: In this paper, two methodologies are used to speed up the maximization of the breakdown voltage (BV) of a vertical GaN diode that has a theoretical maximum BV of ∼ 2100 V. 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 (>1400 V) 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 1887 V (89 % of the ideal case) compared to ∼ 1100 V designed with human domain expertise.