Deep learning-based multi project InP wafer simulation towards unsupervised surface defect detection

• Convolutional neural networks can generate wafer synthetic golden standards. • The use of perceptual losses generates natural looking images, with realistic noise. • Usage of distance metrics as training losses generate clean, uniform images. • Unsupervised wafer surface defect detection can be performed if the input data is of sufficient quality. 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.