Silicon wafer defect pattern detection using machine learning

Abstract

A silicon integrated circuit has been created on silicon wafers by diffusing doping materials on the surface of the same. The silicon wafer is a thin disk made from semiconductor material (mostly ultrapure silicon). Silicon chip fabrication is an industrial process; hence defects occur on silicon wafers caused by unclean factory environment, production machine errors and fabrication process errors. The accurate classification of wafer defects provides information for engineers through regular patterns in the processes. Manual inspection of every chip is not economically feasible. In this study, WM-811k is used as the dataset. This is one of the most extensive wafer bin map dataset available open source on kaggle. It was created by MIRLABS. The dataset was compiled by taking the test data on 811,457 silicon wafer, in which each wafer map image was collected from real-world fabrication. A CNN and DNN model is used to classify the defect patterns.