Five deep learning recipes for the mask-making industry

Ajay Baranwal; Mike Meyer; Thang Nguyen; Suhas Pillai; Noriaki Nakayamada; Mikael Wahlsten; Aki Fujimura; Mariusz Niewczas; Michael Pomerantsev

doi:10.1117/12.2538440

25 October 2019 Five deep learning recipes for the mask-making industry

Ajay Baranwal, Mike Meyer, Thang Nguyen, Suhas Pillai, Noriaki Nakayamada, Mikael Wahlsten, Aki Fujimura, Mariusz Niewczas, Michael Pomerantsev

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Proceedings Volume 11148, Photomask Technology 2019; 1114809 (2019) https://doi.org/10.1117/12.2538440
Event: SPIE Photomask Technology + EUV Lithography, 2019, Monterey, California, United States

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).

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Ajay Baranwal, Mike Meyer, Thang Nguyen, Suhas Pillai, Noriaki Nakayamada, Mikael Wahlsten, Aki Fujimura, Mariusz Niewczas, and Michael Pomerantsev "Five deep learning recipes for the mask-making industry", Proc. SPIE 11148, Photomask Technology 2019, 1114809 (25 October 2019); https://doi.org/10.1117/12.2538440

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KEYWORDS

Photomasks

Scanning electron microscopy

Image segmentation

Computer aided design

Neural networks

Data modeling

Manufacturing

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