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Using machine learning in the physical modeling of lithographic processes
Author(s): Kostas Adam; Shashidhara Ganjugunte; Clement Moyroud; Kostya Shchehlik; Michael Lam; Andrew Burbine; Germain Fenger; Yuri Granik
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Paper 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.

Paper Details

Date Published: 4 April 2019
PDF: 8 pages
Proc. SPIE 10962, Design-Process-Technology Co-optimization for Manufacturability XIII, 109620F (4 April 2019); doi: 10.1117/12.2519848
Show Author Affiliations
Kostas Adam, Mentor, a Siemens Business (United States)
Shashidhara Ganjugunte, Mentor, a Siemens Business (United States)
Clement Moyroud, Mentor, a Siemens Business (France)
Kostya Shchehlik, Mentor, a Siemens Business (United States)
Michael Lam, Mentor, a Siemens Business (United States)
Andrew Burbine, Mentor, a Siemens Business (United States)
Germain Fenger, Mentor, a Siemens Business (United States)
Yuri Granik, Mentor, a Siemens Business (United States)


Published in SPIE Proceedings Vol. 10962:
Design-Process-Technology Co-optimization for Manufacturability XIII
Jason P. Cain, Editor(s)

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