Enhancing Generalization of Wafer Defect Detection by Data Discrepancy-aware Preprocessing and Contrast-varied Augmentation

Wafer inspection locates defects at early fabrication stages and traditionally focuses on pixel-level defects. However, there are very few solutions that can effectively detect largescale defects. In this work, we leverage Convolutional Neural Networks (CNNs) to automate the wafer inspection process and propose several techniques to preprocess and augment wafer images for enhancing our model's generalization on unseen wafers (e.g., from other fabs). Cross-fab experimental results of both wafer-level and pixel-level detections show that the F1 score increases from 0.09 to 0.77 and the Precision-Recall area under curve (PR AUC) increases from 0.03 to 0.62 using our proposed method.