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.
作者简介
Thomas Hirtz received his M.S.degree from the National Institute of Applied Science of Rennes in 2017.He is currently working towards a Ph.D.in Electronic Science and Technology at the Institute of Microelectronics,Tsinghua University.His research interests include reinforcement learning and applications of machine learning techniques in the domain of physics and electronics;Correspondence to:He Tian received the Ph.D.degree from the Institute of Microelectronics,Tsinghua University,in 2015.He is currently an associate professor in Tsinghua University.He was a recipient of the National Science Foundation for outstanding young scholars.He has co-authored over 100 papers and has over 6000 citations.He has been researching on various 2D material-based novel nanodevices.tianhe88@tsinghua.edu.cn。
