Machine Learning for Mask Synthesis and Verification

Haoyu Yang (), Yibo Lin () and Bei Yu ()
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Haoyu Yang: nVIDIA Corp.
Yibo Lin: Peking University
Bei Yu: The Chinese University of Hong Kong

Chapter Chapter 15 in Machine Learning Applications in Electronic Design Automation, 2022, pp 425-470 from Springer

Abstract: Abstract The explosion of machine learning and AI techniques has brought great opportunities of data-assisted optimization for VLSI design automation problems. Recent studies have demonstrated promising results dealing with lithography compliance issues. In this chapter, we will introduce successful attempts using machine learning for mask synthesis and verification, including lithograph modeling, hotspot detection, mask optimization, and layout pattern generation. We hope this chapter can motivate future research on AI-assisted DFM solutions.

Keywords: Lithography modeling; Hotspot detection; Mask optimization; Pattern generation; Supervised learning; Generative learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13074-8_15

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DOI: 10.1007/978-3-031-13074-8_15

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