Generative AI for Improved Defect Detection in Semiconductor Wafers

Traditional methods for identifying defects in industrial settings are often complicated, inefficient, and rely heavily on expert knowledge for manual feature extraction and pipeline development. However, with the emergence of machine learning and deep learning, there’s been a shift towards using these technologies for defect detection. Particularly, Generative AI has garnered attention for its ability to generate accurate defect identifications across various sectors. Our contribution lies in leveraging generative AI to revolutionize wafer manufacturing defect detection. We explore the balance between discriminative inference, known for its speed but susceptibility to shortcuts, and generative modeling, which offers enhanced robustness despite slower operation. Our investigation emphasizes the need for a model distinct from conventional discriminative ones, capable of adaptive learning to distinguish between normal and abnormal patterns in wafer manufacturing, aligning with expert judgment and utilizing historical data for predictive maintenance. We propose two innovative approaches using Generative AI: first, augmenting existing discriminative models with generative models for improved practicality; second, utilizing generative modeling to convert image-to-text models into classifiers.