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Nai-Chia Cheng

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Proceedings Article | 26 March 2020 Paper

Implementing Machine Learning for OPC retargeting

Kevin Hooker, Marco Guajardo, Nai-Chia Cheng, Guangming Xiao

Proceedings Volume 11328, 113281D (2020) https://doi.org/10.1117/12.2552402

KEYWORDS: Data modeling, Optical proximity correction, Photomasks, Lithography, Machine learning, Statistical modeling, Error analysis, Semiconducting wafers, Optimization (mathematics), Optical lithography

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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.

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