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Proceedings Paper

Implementing Machine Learning for OPC retargeting
Author(s): Kevin Hooker; Marco Guajardo; Nai-Chia Cheng; Guangming Xiao
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Paper Abstract

As feature resolution and process variations continue to shrink for new nodes of both DUV and EUV lithography, the density and number of devices on advanced semiconductor masks continue to increase rapidly. These advances cause significantly increased pressure on the accuracy and efficiency of OPC and assist feature (AF) optimization methods for each subsequent process technology. To meet manufacturing yield requirements, significant wafer retargeting from the original design target is often performed before OPC to account for both lithographic limitations and etch effects. As retargeting becomes more complex and important, rule-table based approaches become ineffective. Alternatively, modelbased optimization approaches using advanced solvers, e.g., inverse lithography technology (ILT), have demonstrated process window improvement over rule-based approaches. However, model-based target optimization is computationally expensive which typically limits its use to smaller areas like hotspot repairs. In this paper, we present results of a method that uses machine-learning (ML) to predict optimal retargeting for line-space layers. In this method, we run ILT co-optimization of the wafer target and process window to generate the training data used to train a machine learning model to predict the optimum wafer target. We explore methods to avoid ML model overfitting and show the ML infrastructure used to integrate ML solution into a manufacturable OPC flow. Both lithographic quality and runtime performance are evaluated for an ML enabled retargeting flow, an ILT flow and a simple rule table flow at advanced node test cases.

Paper Details

Date Published: 26 March 2020
PDF: 8 pages
Proc. SPIE 11328, Design-Process-Technology Co-optimization for Manufacturability XIV, 113281D (26 March 2020); doi: 10.1117/12.2552402
Show Author Affiliations
Kevin Hooker, Synopsys, Inc. (United States)
Marco Guajardo, Synopsys, Inc. (United States)
The Univ. of Texas at Austin (United States)
Nai-Chia Cheng, The Univ. of Southern California (United States)
Guangming Xiao, Synopsys, Inc. (United States)


Published in SPIE Proceedings Vol. 11328:
Design-Process-Technology Co-optimization for Manufacturability XIV
Chi-Min Yuan, Editor(s)

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