Open AccessDirt Classification of Silicon Wafers Based on Deep Learning
Author(s) -
Xingxing Li,
Chao Duan,
Zhi Yan,
Panpan Yin
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/5/052080
Subject(s) - deep learning , wafer , artificial intelligence , silicon , computer science , dirt , residual neural network , backpropagation , artificial neural network , residual , jump , process (computing) , pattern recognition (psychology) , materials science , engineering , nanotechnology , optoelectronics , mechanical engineering , algorithm , physics , quantum mechanics , operating system
Deep learning technology is now widely used in the industry, and some irregular blemishes and stains can be solved by deep learning methods. In this paper, the deep learning resnet network is used to classify and judge the dirt that appears on the silicon chip. It is a residual network structure. The mapping relationship between layers is realized through jump connections to ensure that the number of network layers’ increases. In the process of gradient backpropagation, there will be no gradient disappearance and gradient explosion, and the goal of training deep networks is achieved. This article is to apply the resnet deep neural network on the silicon wafer image to make a certain area on the silicon wafer make the right judgment for the dirty problem.