AI enhances semiconductor device design and defect discovery with TCAD data
AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM
Machine Learning
Summary
Designing semiconductors and finding their defects is tricky because there's not enough data and the problems are very complex. The authors show how using computer simulations called TCAD can create accurate data that helps AI models learn much better. They teach AI to perform as well as expert engineers by using special machine learning techniques on this simulated data. They also explore how large language models might help automate designing devices and discovering defects in the future.
What this means in practice
- •For semiconductor device engineers: Use AI trained on TCAD-generated data to estimate device parameters like layer thicknesses and doping without destructive testing.
- •For integrated circuit test teams: Automatically calibrate simulation parameters and predict device behaviors to assist with quality control and design verification.
Authors
Hiu Yung Wong
Abstract
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.