Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Lamia Alam; Nasser Kehtarnavaz

doi:10.1117/12.2663364

13 June 2023 Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN

Lamia Alam, Nasser Kehtarnavaz

Proceedings Volume 12528, Real-Time Image Processing and Deep Learning 2023; 1252809 (2023) https://doi.org/10.1117/12.2663364
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Defective dies on a silicon wafer form a pattern that is called a wafer map. In order to adequately train a deep learning-based automated optical inspection system to detect such defective patterns, a large number of defective patterns or wafer maps are needed. In practice, on an actual production line, defective patterns occur infrequently and thus are difficult and time consuming to collect. A computationally efficient defective pattern generation solution is developed in this paper by using the deep learning network of CycleGAN which is a variant of the generative adversarial network. The public domain WM-811K wafer dataset was used to generate or synthesize defective patterns or wafer maps. The two metrics of Fréchet inception distance and kernel inception distance were utilized to evaluate the resemblance of the generated defective images to the real defective images. The results obtained indicate that the developed defective pattern generation method produces realistic wafer maps at a computationally efficient rate of 3 synthesized images per second.

Conference Presentation

Citation Download Citation

Lamia Alam and Nasser Kehtarnavaz "Real-time generation of realistic defective wafer maps via deep learning network of CycleGAN", Proc. SPIE 12528, Real-Time Image Processing and Deep Learning 2023, 1252809 (13 June 2023); https://doi.org/10.1117/12.2663364

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