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Proceedings Paper

Mask synthesis using machine learning software and hardware platforms
Author(s): Peng Liu
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Paper Abstract

Inspired by many success stories of machine learning (ML) in a broad range of artificial intelligence (AI) applications, both industrial and academic researchers are now actively developing ML solutions for challenging problems in computational lithography. In this work, we explore the possibility of utilizing ML software and hardware platforms for mask synthesis applications. Specifically, we demonstrate a standalone mask synthesis flow that runs entirely on the TensorFlow ML platform with a reinforcement learning (RL) approach and GPU acceleration. We will describe the architecture of our ML mask synthesis framework that comprises separable and interchangeable components including neural network (NN)-based 3D mask, imaging and resist models. We will discuss the readiness of these components and present the proof-of-concept evaluation results of the proposed ML mask synthesis framework.

Paper Details

Date Published: 23 March 2020
PDF: 16 pages
Proc. SPIE 11327, Optical Microlithography XXXIII, 1132707 (23 March 2020); doi: 10.1117/12.2551816
Show Author Affiliations
Peng Liu, Synopsys, Inc. (United States)

Published in SPIE Proceedings Vol. 11327:
Optical Microlithography XXXIII
Soichi Owa, Editor(s)

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