AI helps design semiconductors and find defects using simulation data
AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM
Machine Learning
Summary
Designing tiny semiconductor devices and finding mistakes inside them is very hard because of limited data and complex details. The authors show how using computer simulations (called TCAD) can create precise data to teach AI models, helping these models learn the underlying physics without needing experts all the time. They demonstrate AI methods that can understand electrical measurements to figure out device details like layer thickness and doping. The paper also suggests that large language models could further improve automated device design and defect detection in the future.
AITechnology Computer-Aided Design (TCAD)machine learningauto-encoderlarge language models (LLMs)semiconductor deviceinverse designdefect discoveryFinFETSchottky diode
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.