Framework for TCAD augmented machine learning on multi-I-V characteristics using convolutional neural network and multiprocessing  

作　　者：Thomas Hirtz Steyn Huurman He Tian Yi Yang Tian-Ling Ren 

机构地区：[1]Institute of Microelectronics,Tsinghua University,Beijing 100084,China [2]Department of Computer Science,Tsinghua University,Beijing 100084,China

出　　处：《Journal of Semiconductors》2021年第12期86-94,共9页半导体学报（英文版）

摘　　要：In a world where data is increasingly important for making breakthroughs,microelectronics is a field where data is sparse and hard to acquire.Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices.This infrastructure is crucial for generating sufficient data for the use of new information technologies.This situation generates a cleavage between most of the researchers and the industry.To address this issue,this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing.The multi-I-V curves that we obtained were processed simultaneously using convolutional neural networks,which gave us the ability to predict a full set of device characteristics with a single inference.We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data.We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research,such as device engineering,yield engineering or process monitoring.Moreover,this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics,without the need for expensive experimentation infrastructure.

关 键 词：machine learning neural networks semiconductor devices simulation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]