# AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM

arXiv: https://arxiv.org/abs/2609.07046 · HTML: https://mlsanity.com/paper/2609.07046/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Vertical GaN Diode BV Maximization through Rapid TCAD Simulation and ML-enabled Surrogate Model](https://mlsanity.com/paper/2208.01142/) (2022)
- [Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise](https://mlsanity.com/paper/2408.07921/) (2024)
- [Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction](https://mlsanity.com/paper/2304.00738/) (2023)
- [Ga2O3 TCAD Mobility Parameter Calibration using Simulation Augmented Machine Learning with Physics Informed Neural Network](https://mlsanity.com/paper/2504.02283/) (2025)
- [Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors](https://mlsanity.com/paper/2509.11113/) (2025)

## Abstract (arXiv)

AI has revolutionized various engineering domains, but its impact on semiconductor device design and defect discovery is still limited, due to limited data and the curse of dimensionality. In this paper, we will discuss our work on using the Technology Computer-Aided-Design (TCAD) to generate precise data needed for machine learning (ML) to enable simulation-augmented ML. We demonstrate that with minimal domain expertise, it is possible to create a machine that performs as well as a device engineer on a specific task. We will show that auto-encoder-based machine learning models and noise engineering applied to TCAD data are effective at learning latent physics, and that the models can be seamlessly applied to experimental data. We will demonstrate how to build a device-engineer-level model step by step through various examples, including using only non-destructive electrical data to inverse-engineer the PiN diode layer thickness variations, the Ga2O3 Schottky diode doping and anode workfunction variations, and the transistor contact resistance in an inverter. Examples also include the generation of a FinFET IV/CV prediction model, the mapping between transistor images and IV curves, and the automatic calibration of TCAD parameters for a Ga2O3 Schottky diode, which can only be handled well by experienced TCAD engineers. Finally, to fully realize the potential of AI, large language models (LLMs) and multimodal LLMs (MLLMs) are believed to be necessary. We will discuss the application of LLMs to TCAD command file creation and our vision for MLLMs in automated device design and defect discovery.