Applying Data Augmentation and Mask R-CNN-Based Instance Segmentation Method for Mixed-Type Wafer Maps Defect Patterns Classification

Abstract

Defect patterns on semiconductor wafer maps point to different manufacturing problems. Consequently, they have become key factors in identifying and resolving the root causes of yield improvement. Perhaps not surprisingly, the probability of having multiple defect patterns on a wafer map has increased in tandem with advances in manufacturing technology. Prior research on defect patterns has focused primarily on image classification methods which are neither good at mixed-type defect pattern classification nor able to provide locational information for further analysis. This study develops a framework that integrates a Mask R-CNN-based instance segmentation model with copy-paste and rotational data augmentation. The proposed method is able to precisely classify and locate defect patterns on a wafer map given limited training data, tasks which can help companies identify the manufacturing root causes of defects in a timely manner when ramping up their production for yield enhancement. Our experiments were performed using real-world WM-811K data. Using COCO pre-trained weights and only 1,056 items of original training data, the model reached an accuracy of 97.7% on single-type classification. Mixed-type classification hamming loss, exact match and accuracy were 0.155, 69% and 82%, respectively.