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Making digital twins using the Deep Learning Kit (DLK)
Author(s): Linyong Pang; Suhas Pillai; Thang Nguyen; Mike Meyer; Ajay Baranwal; Henry Yu; Mariusz Niewczas; Ryan Pearman; Abhishek Shendre; Aki Fujimura
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Paper Abstract

Deep learning (DL) is one of the fastest-growing fields in artificial intelligence (AI). While still in its early stages of adoption, DL has already shown it has the potential to make significant changes to the lithography and photomask industries through the automation or optimization of equipment and processes. The key element required for application of DL techniques to any process is a large volume of data to adequately train the DL neural networks. The accuracy of the classification or prediction of any DL system is dependent on the depth and breadth of the training data to which it is exposed. For semiconductor manufacturing, finding adequate data – especially for corner cases – can be difficult and/or expensive. In this paper, we will present two digital twins that are themselves built from DL as a part of a DL Starter Kit. We will demonstrate the creation of DL-based digital twins for a mask scanning electron microscope (SEM) and for curvilinear inverse lithography technology (ILT).

Paper Details

Date Published: 21 October 2019
PDF: 13 pages
Proc. SPIE 11148, Photomask Technology 2019, 111480B (21 October 2019); doi: 10.1117/12.2538508
Show Author Affiliations
Linyong Pang, D2S, Inc. (United States)
Suhas Pillai, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Thang Nguyen, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Mike Meyer, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Ajay Baranwal, The Ctr. for Deep Learning in Electronics Manufacturing (United States)
Henry Yu, D2S, Inc. (United States)
Mariusz Niewczas, D2S, Inc. (United States)
Ryan Pearman, D2S, Inc. (United States)
Abhishek Shendre, D2S, Inc. (United States)
Aki Fujimura, 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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