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Five deep learning recipes for the mask-making industry
Author(s): Ajay Baranwal; Mike Meyer; Thang Nguyen; Suhas Pillai; Noriaki Nakayamada; Mikael Wahlsten; Aki Fujimura; Mariusz Niewczas; Michael Pomerantsev
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Paper Abstract

Deep learning has an increasing impact on our personal and professional lives. Deep learning has the potential to transform mask, semiconductor and electronics manufacturing. This paper reviews key results from the Center for Deep Learning in Electronics Manufacturing’s (CDLe’s) first year of operation. We consider results from adapting five common types of deep learning recipes to solve key challenges in the manufacture of photomasks, printed circuit boards (PCBs), and flat panel displays (FPDs). These deep learning applications include 1) grouping similar items to automatically categorize mask rule errors; 2) using U-Net architecture to construct fast mask designs; 3) using vision-based object classification to find and classify pick-and-place (PnP) errors on PCB assembly lines; 4) using anomaly detection to improve the quality of FPDs; and 5) using digital twins to create SEM images and optimize Inverse Lithography Technology (ILT). While we compare the relative benefits of these techniques, all show the importance of data to improve the success of deep learning networks and of electronics manufacturing. These applications rely on varying neural network architectures such as autoencoders, segmentation networks, deep convolutional networks, anomaly detection, and generative adversarial networks (GANs).

Paper Details

Date Published: 25 October 2019
PDF: 19 pages
Proc. SPIE 11148, Photomask Technology 2019, 1114809 (25 October 2019); doi: 10.1117/12.2538440
Show Author Affiliations
Ajay Baranwal, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Mike Meyer, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Thang Nguyen, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Suhas Pillai, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Noriaki Nakayamada, NuFlare Technology, Inc. (Japan)
Mikael Wahlsten, Mycronic AB (Sweden)
Aki Fujimura, D2S, Inc. (United States)
Mariusz Niewczas, D2S, Inc. (United States)
Michael Pomerantsev, D2S, Inc. (United States)


Published in SPIE Proceedings Vol. 11148:
Photomask Technology 2019
Jed H. Rankin; Moshe E. Preil, Editor(s)

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