High performance lithographic hotspot detection using hierarchically refined machine learning

Authors:
Duo Ding;Andres J. Torres;Fedor G. Pikus;David Z. Pan
Affiliations:
Univ. of Texas at Austin, Austin, TX;Mentor Graphics Corporation, Wilsonville, OR;Mentor Graphics Corporation, Wilsonville, OR;Univ. of Texas at Austin, Austin, TX
Venue:
Proceedings of the 16th Asia and South Pacific Design Automation Conference
Year:
2011

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Abstract

Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly out-performs previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.