Semiconductor wafer defect classification using convolution neural network: a binary case

Abstract

With the multitude of steps used in semiconductor industry, automation is being practiced extensively in its manufacturing processes to guarantee quality of manufactured chips and improvement in production. At front end of line, wafer are probed and defective chips are segregated. From this data wafer bin map are generated, these show defects on the surface of wafers. If analysis of wafer bin map is done manually, this may result in incorrect categorization of defects due to human error and lack of judgement. Thus, the rationale behind this research study is to determine the scope of vision-based methods for automatic classification of wafer defects. In view of this, a classifier which detects Type A and Type B defects in wafers is proposed. It involves a Convolution Neural Network (CNN) based binary classifier. 250 Images of each class of wafer bin map dataset, is used to conduct the study on five layer CNN architecture. The proposed setup gives the test accuracy of 97.7%.