In-design DFM pattern optimization with ML models for dynamic fixing guidance selection and implementation

Fadi Batarseh; Piyush Pathak; Jac Condella; Keerthana Bhoopanam; Jeff Nelson; Lynn Wang; Ya-Chieh Lai

doi:10.1117/12.3023887

11 April 2024 In-design DFM pattern optimization with ML models for dynamic fixing guidance selection and implementation

Fadi Batarseh, Piyush Pathak, Jac Condella, Keerthana Bhoopanam, Jeff Nelson, Lynn Wang, Ya-Chieh Lai

Author Affiliations +

Proceedings Volume 12954, DTCO and Computational Patterning III; 1295406 (2024) https://doi.org/10.1117/12.3023887
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

In the realm of Design for Manufacturability (DFM) optimization, Pattern-Based Layout Optimization (PBLO) has been a go-to approach for detecting and repairing DFM violations. However, to enhance the effectiveness of DFM rules in addressing hotspots, it becomes imperative to encompass a broader array of design situations (layout contexts). This expansion leads to an increased number of potential fixing guidance “hints”. Nonetheless, employing a static fixing hint order, unaware to the specific in-design topologies, can potentially diminish the output metrics i.e., fixing rate and runtime performance. In pursuit of optimizing these output metrics, we present an ML-powered PBLO workflow. In this innovative approach, a Machine Learning (ML) model is trained using an extensive dataset of preranked fixing guidance hints that are associated with a DFM rule. The topology aware supervised ML model is trained to dynamically guide and select the most suitable in-design fixing guidance order per situation, ultimately leading to an improved fixing rate, runtime and quality of results. In this study, we illustrate a workflow and mechanism for seamlessly integrating machine learning capabilities into the in-design fixing router. This involves developing multiclass machine learning algorithms and models to facilitate the generation of an optimal fixing guidance sequence.

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Citation Download Citation

Fadi Batarseh, Piyush Pathak, Jac Condella, Keerthana Bhoopanam, Jeff Nelson, Lynn Wang, and Ya-Chieh Lai "In-design DFM pattern optimization with ML models for dynamic fixing guidance selection and implementation", Proc. SPIE 12954, DTCO and Computational Patterning III, 1295406 (11 April 2024); https://doi.org/10.1117/12.3023887

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