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Machine Learning for Mask Synthesis

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Machine Learning in VLSI Computer-Aided Design

Abstract

Recent mask synthesis takes longer runtime due to the demand on higher accuracy. It is common that optical proximity correction (OPC) takes a few days. Machine learning has recently been applied to mask synthesis process with some success. This chapter addresses two popular mask synthesis tasks, OPC and etch proximity correction (EPC), and presents how machine learning is applied for their efficient implementation. Discussion on learning parameters, preparation of learning data set, and techniques to avoid over-fitting are also provided.

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Notes

  1. 1.

    The complete form of the Bessel function is Ψ nm(r, φ) = J n(r)e imφ, but the real part is only considered in this chapter.

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Author information

Authors and Affiliations

  1. Samsung Electronics, Hwasung, South Korea

    Seongbo Shim

  2. School of Electrical Engineering, KAIST, Daejeon, South Korea

    Suhyeong Choi & Youngsoo Shin

Authors
  1. Seongbo Shim
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  2. Suhyeong Choi
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  3. Youngsoo Shin
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Corresponding author

Correspondence to Seongbo Shim .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering and Center for Cyber Physical Systems, Khalifa University, Abu Dhabi, United Arab Emirates

    Ibrahim (Abe) M. Elfadel

  2. Massachusetts Institute of Technology, Cambridge, MA, USA

    Duane S. Boning

  3. Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA

    Xin Li

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Shim, S., Choi, S., Shin, Y. (2019). Machine Learning for Mask Synthesis. In: Elfadel, I., Boning, D., Li, X. (eds) Machine Learning in VLSI Computer-Aided Design. Springer, Cham. https://doi.org/10.1007/978-3-030-04666-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-04666-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04665-1

  • Online ISBN: 978-3-030-04666-8

  • eBook Packages: EngineeringEngineering (R0)

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