Layout optimization through robust pattern learning and prediction in SADP gridded designs

Jen-Yi Wuu; Mark Simmons; Malgorzata Marek-Sadowska

doi:10.1117/12.916583

14 March 2012 Layout optimization through robust pattern learning and prediction in SADP gridded designs

Jen-Yi Wuu, Mark Simmons, Malgorzata Marek-Sadowska

Proceedings Volume 8327, Design for Manufacturability through Design-Process Integration VI; 832705 (2012) https://doi.org/10.1117/12.916583
Event: SPIE Advanced Lithography, 2012, San Jose, California, United States

Abstract

In this paper, we study the problem of placement-level layout optimization for designs built from cells with unidirectional self-aligned double patterning (SADP) metal-1 interconnect. Our goal is to minimize the number of potential bridging hotspots in design layouts using predictive, machine learning-based models and applying incremental placement adjustments. In the first part of the paper, we explain how to build layout pattern classification models using machine learning methods. Our support vector machine (SVM)-based model predicts a given layout clip as either robust or non-robust. In the second part of the paper, we apply the predictive models to placement-level optimization. Our algorithm identifies and eliminates potential hotspots in standard cell based layout by modifying local cell position.

Citation Download Citation

Jen-Yi Wuu, Mark Simmons, and Malgorzata Marek-Sadowska "Layout optimization through robust pattern learning and prediction in SADP gridded designs", Proc. SPIE 8327, Design for Manufacturability through Design-Process Integration VI, 832705 (14 March 2012); https://doi.org/10.1117/12.916583

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