Bridging TCAD and AI: Its Application to Semiconductor Design

There is a growing consensus that the physics-based model needs to be coupled with machine learning (ML) model relying on data or vice versa in order to fully exploit their combined strengths to address scientific or engineering problems that cannot be solved separately. We propose several methodologies of bridging technology computer-aided design (TCAD) simulation and artificial intelligence (AI) with its application to the tasks for which traditional TCAD faces challenges in terms of simulation runtime, coverage, and so on. AI-emulator that learns fine-grained information from rigorous TCAD enables simulation of process technologies and device in real-time as well as large-scale simulation such as full-pattern analysis of stress without high demand on computational resource. To accelerate atomistic molecular dynamics (MD) simulation, we have done a comparison study of descriptor-based and graph-based neural net potential, and also show their capability with large-scale and long-time simulation of silicon oxidation. Finally, we discuss the use of hybrid modeling of AI- and physics-based model for the case where physical equations are either fully or partially unknown.

I. Introduction

Combination of atomistic, process, device, and circuit simulations, referred to as technology computer-aided design (TCAD), have been widely used to develop and optimize semiconductor process technologies and devices by using physical (or compact) models. Recently, with massive datasets and high computing power, data-driven models, called machine learning (ML) or artificial intelligence (AI), expand their application in many areas of semiconductor industry such as automating chip design [1], optimizing processes [2], [3], improving production yield [4]–[6], and so on.