Antecedent hash modality learning and representation for enhanced wafer map defect pattern recognition

Abstract

In wafer map defect pattern recognition, deep learning methods are predominantly used. These models autonomously learn features without explicit human intervention due to their black-box type network architectures. While preceding feature extraction and modality representation may seem neglected in deep learning for wafer map defect pattern recognition, we addressed this question by proposing an innovative approach. Our proposed method employs an antecedent feature learning module to create a more compact and informative hash modality representation from input wafer maps. The method exceeds in achieving high recall, crucial for identifying actual defective wafers as much as possible in semiconductor manufacturing, where defects can impact integrated circuit functionality and reliability. Smaller-sized hash modalities against larger wafer maps allow the same model to speed up the process and require fewer computational resources.