Using machine learning in the physical modeling of lithographic processes

Kostas Adam; Shashidhara Ganjugunte; Clement Moyroud; Kostya Shchehlik; Michael Lam; Andrew Burbine; Germain Fenger; Yuri Granik

doi:10.1117/12.2519848

4 April 2019 Using machine learning in the physical modeling of lithographic processes

Kostas Adam, Shashidhara Ganjugunte, Clement Moyroud, Kostya Shchehlik, Michael Lam, Andrew Burbine, Germain Fenger, Yuri Granik

Author Affiliations +

Proceedings Volume 10962, Design-Process-Technology Co-optimization for Manufacturability XIII; 109620F (2019) https://doi.org/10.1117/12.2519848
Event: SPIE Advanced Lithography, 2019, San Jose, California, United States

Abstract

We show how combining machine learning with physical models can improve the overall accuracy of modeling the lithographic process for OPC applications by up to 40%. This level of model accuracy improvement is critical to meet the stringent requirements of the 5nm node and below. We demonstrate how the judicious design of the neural network can create a model capable of high accuracy and high contour quality, even when no contour data is available. This allows the neural network model to be introduced without disrupting the model calibration flow used in OPC.

Citation Download Citation

Kostas Adam, Shashidhara Ganjugunte, Clement Moyroud, Kostya Shchehlik, Michael Lam, Andrew Burbine, Germain Fenger, and Yuri Granik "Using machine learning in the physical modeling of lithographic processes", Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620F (4 April 2019); https://doi.org/10.1117/12.2519848

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available