Deep Learning-based Multi Project InP Wafer Simulation for Unsupervised Surface Defect Detection
This addresses a domain-specific problem in semiconductor manufacturing by automating defect detection where golden standards are unavailable, though it appears incremental as it builds on existing template matching with a new simulation method.
The paper tackles the problem of manual defect detection in Indium-Phosphide wafer manufacturing by proposing a deep learning method to generate synthetic golden standards from CAD data, which outperforms a baseline decision-tree approach for more efficient defect detection.
Quality management in semiconductor manufacturing often relies on template matching with known golden standards. For Indium-Phosphide (InP) multi-project wafer manufacturing, low production scale and high design variability lead to such golden standards being typically unavailable. Defect detection, in turn, is manual and labor-intensive. This work addresses this challenge by proposing a methodology to generate a synthetic golden standard using Deep Neural Networks, trained to simulate photo-realistic InP wafer images from CAD data. We evaluate various training objectives and assess the quality of the simulated images on both synthetic data and InP wafer photographs. Our deep-learning-based method outperforms a baseline decision-tree-based approach, enabling the use of a 'simulated golden die' from CAD plans in any user-defined region of a wafer for more efficient defect detection. We apply our method to a template matching procedure, to demonstrate its practical utility in surface defect detection.