research-article

A unified framework for simultaneous layout decomposition and mask optimization

Authors:

Bei YuAuthors Info & Claims

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

Pages 81 - 88

Published: 13 November 2017 Publication History

Abstract

In advanced technology nodes, layout decomposition and mask optimization are two key stages in integrated circuit design. Due to the inconsistency of the objectives of these two stages, the performance of conventional layout and mask optimization may be suboptimal. To tackle this problem, in this paper we propose a unified framework, which seamlessly integrates layout decomposition and mask optimization. We propose a gradient based approach to solve the unified mathematical formulation, as well as a set of discrete optimization techniques to avoid being stuck in local optimum. The conventional optimization process can be accelerated as some inferior decompositions can be smartly pruned in early stages. The experimental results show that the proposed unified framework can achieve more than 17X speed-up compared with the conventional two-stage flow, meanwhile it can reduce EPE violations by 18%, and thus maintain better design quality.

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Cited By

Zhu BZhang XLin YYu BWong MFranzon PKahng ALi HLi B(2022)Efficient Design Rule Checking Script Generation via Key Information ExtractionProceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD10.1145/3551901.3556494(77-82)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3551901.3556494
Zhong WHu SMa YYang HMa XYu BLi Z(2020)Deep learning-driven simultaneous layout decomposition and mask optimizationProceedings of the 57th ACM/EDAC/IEEE Design Automation Conference10.5555/3437539.3437738(1-6)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3437539.3437738
Yang HZhong WMa YGeng HChen RChen WYu BCheng KYang H(2020)VLSI Mask Optimization: From Shallow To Deep LearningProceedings of the 25th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC47756.2020.9045241(434-439)Online publication date: 17-Jan-2020
https://dl.acm.org/doi/10.1109/ASP-DAC47756.2020.9045241
Show More Cited By

A unified framework for simultaneous layout decomposition and mask optimization
1. Theory of computation
  1. Design and analysis of algorithms

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cover image ACM Conferences

ICCAD '17: Proceedings of the 36th International Conference on Computer-Aided Design

November 2017

1077 pages

Conference Chair:
Sri Parameswaran
GENERAL CHAIR

Sponsors

CEDA: Council on Electronic Design Automation
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CAS: Circuits & Systems

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IEEE-EDS: Electronic Devices Society

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IEEE Press

Publication History

Published: 13 November 2017

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ICCAD '17

Sponsor:

CEDA
SIGDA
IEEE-CAS

ICCAD '17: IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN

November 13 - 16, 2017

California, Irvine

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Zhu BZhang XLin YYu BWong MFranzon PKahng ALi HLi B(2022)Efficient Design Rule Checking Script Generation via Key Information ExtractionProceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD10.1145/3551901.3556494(77-82)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3551901.3556494
Zhong WHu SMa YYang HMa XYu BLi Z(2020)Deep learning-driven simultaneous layout decomposition and mask optimizationProceedings of the 57th ACM/EDAC/IEEE Design Automation Conference10.5555/3437539.3437738(1-6)Online publication date: 20-Jul-2020
https://dl.acm.org/doi/10.5555/3437539.3437738
Yang HZhong WMa YGeng HChen RChen WYu BCheng KYang H(2020)VLSI Mask Optimization: From Shallow To Deep LearningProceedings of the 25th Asia and South Pacific Design Automation Conference10.1109/ASP-DAC47756.2020.9045241(434-439)Online publication date: 17-Jan-2020
https://dl.acm.org/doi/10.1109/ASP-DAC47756.2020.9045241
Yang HLi SMa YYu BYoung E(2018)GAN-OPCProceedings of the 55th Annual Design Automation Conference10.1145/3195970.3196056(1-6)Online publication date: 24-Jun-2018
https://dl.acm.org/doi/10.1145/3195970.3196056

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