On-line real-time detection system for wafer surface defects based on deep learning and generative adversarial network

Chao-Ching Ho; Shou-Lin Chu

doi:10.1117/12.2657960

27 April 2023 On-line real-time detection system for wafer surface defects based on deep learning and generative adversarial network

Chao-Ching Ho, Shou-Lin Chu

Author Affiliations +

Proceedings Volume 12496, Metrology, Inspection, and Process Control XXXVII; 124962S (2023) https://doi.org/10.1117/12.2657960
Event: SPIE Advanced Lithography + Patterning, 2023, San Jose, California, United States

Conference Poster

Abstract

This study aims to improve defect detection and optimize deep learning models for wafer surfaces. Deep learning methods for high-yield wafer processes often suffer from insufficient sample size. The challenge of generating enough defective samples hinders the model's ability to accurately detect defects through training. This study resolves the issue by using a generative adversarial network style transfer method to generate synthetic data in batches and flexibly determine the location of defect generation. The optimized model achieved an accuracy reduction of only 2% and a 55% reduction in inference time. Edge computing was also applied for online, real-time detection of defects.

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Chao-Ching Ho and Shou-Lin Chu "On-line real-time detection system for wafer surface defects based on deep learning and generative adversarial network", Proc. SPIE 12496, Metrology, Inspection, and Process Control XXXVII, 124962S (27 April 2023); https://doi.org/10.1117/12.2657960

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