AI与TCAD用于逆向设计与缺陷发现：从简单机器学习到大语言模型
AI and TCAD for Inverse Design and Defect Discovery: From Simple Machine Learning to LLM
- San Jose State University（圣何塞州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文利用TCAD生成数据结合机器学习，实现器件逆向设计与缺陷发现，并展望大语言模型在自动化设计中的应用。
AI中文摘要：
人工智能已彻底改变了众多工程领域，但其在半导体器件设计与缺陷发现方面的影响仍然有限，原因在于数据有限以及维度灾难。本文中，我们将讨论利用技术计算机辅助设计（TCAD）生成机器学习（ML）所需精确数据的工作，以实现仿真增强的机器学习。我们证明，仅需最少的领域专业知识，就能构建一个在特定任务上表现与器件工程师相当的机器。我们将展示基于自编码器的机器学习模型以及对TCAD数据应用的噪声工程在学习潜在物理规律方面的有效性，并且这些模型可以无缝应用于实验数据。我们将通过多个示例逐步演示如何构建器件工程师级别的模型，包括仅使用非破坏性电学数据逆向工程PiN二极管层厚度变化、Ga2O3肖特基二极管的掺杂和阳极功函数变化，以及反相器中晶体管接触电阻。示例还包括生成FinFET IV/CV预测模型、晶体管图像与IV曲线之间的映射，以及Ga2O3肖特基二极管TCAD参数的自动校准，这些任务通常只有经验丰富的TCAD工程师才能妥善处理。最后，为充分释放人工智能的潜力，大语言模型（LLMs）和多模态大语言模型（MLLMs）被认为是必要的。我们将讨论LLMs在TCAD命令文件创建中的应用，以及我们对MLLMs在自动化器件设计和缺陷发现中的愿景。
英文摘要：
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.
