Making digital twins using the Deep Learning Kit (DLK)

Linyong Pang; Suhas Pillai; Thang Nguyen; Mike Meyer; Ajay Baranwal; Henry Yu; Mariusz Niewczas; Ryan Pearman; Abhishek Shendre; Aki Fujimura

doi:10.1117/12.2538508

21 October 2019 Making digital twins using the Deep Learning Kit (DLK)

Linyong Pang, Suhas Pillai, Thang Nguyen, Mike Meyer, Ajay Baranwal, Henry Yu, Mariusz Niewczas, Ryan Pearman, Abhishek Shendre, Aki Fujimura

Author Affiliations +

Proceedings Volume 11148, Photomask Technology 2019; 111480B (2019) https://doi.org/10.1117/12.2538508
Event: SPIE Photomask Technology + EUV Lithography, 2019, Monterey, California, United States

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).

Citation Download Citation

Linyong Pang, Suhas Pillai, Thang Nguyen, Mike Meyer, Ajay Baranwal, Henry Yu, Mariusz Niewczas, Ryan Pearman, Abhishek Shendre, and Aki Fujimura "Making digital twins using the Deep Learning Kit (DLK)", Proc. SPIE 11148, Photomask Technology 2019, 111480B (21 October 2019); https://doi.org/10.1117/12.2538508

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