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

arXiv: https://arxiv.org/abs/2208.01142 · HTML: https://mlsanity.com/paper/2208.01142/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact](https://mlsanity.com/paper/2409.10803/) (2024)
- [Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction](https://mlsanity.com/paper/2304.00738/) (2023)
- [AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM](https://mlsanity.com/paper/2609.07046/) (2026)

## Abstract (arXiv)

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.