research-article

A novel fuzzy matching model for lithography hotspot detection

Authors:

Sheng-Yuan Lin,

Shih-Chieh ChangAuthors Info & Claims

DAC '13: Proceedings of the 50th Annual Design Automation Conference

Article No.: 68, Pages 1 - 6

https://doi.org/10.1145/2463209.2488817

Published: 29 May 2013 Publication History

Abstract

In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. Our results are very encouraging with under 0.56 CPU-hrs/mm² runtime.

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Cited By

He XWang YFu ZWang YGuo Y(2023)A General Layout Pattern Clustering Using Geometric Matching-based Clip Relocation and Lower-bound Aided OptimizationACM Transactions on Design Automation of Electronic Systems10.1145/361029328:6(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3610293
Pei ZZhao WHe ZYu B(2023)Bit-Level Quantization for Efficient Layout Hotspot Detection2023 International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA59274.2023.10218502(465-470)Online publication date: 8-May-2023
https://doi.org/10.1109/ISEDA59274.2023.10218502
Khan IMittal NAnsari M(2023)Applications of VLSI Design in Artificial Intelligence and Machine LearningMachine Learning for VLSI Chip Design10.1002/9781119910497.ch1(1-17)Online publication date: 23-Jun-2023
https://doi.org/10.1002/9781119910497.ch1
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Index Terms

A novel fuzzy matching model for lithography hotspot detection
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Partitioning and floorplanning
  2. Hardware validation
    1. Functional verification

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cover image ACM Conferences

DAC '13: Proceedings of the 50th Annual Design Automation Conference

May 2013

1285 pages

ISBN:9781450320719

DOI:10.1145/2463209

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 29 May 2013

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DAC '13: The 50th Annual Design Automation Conference 2013

May 29 - June 7, 2013

Texas, Austin

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Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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Cited By

He XWang YFu ZWang YGuo Y(2023)A General Layout Pattern Clustering Using Geometric Matching-based Clip Relocation and Lower-bound Aided OptimizationACM Transactions on Design Automation of Electronic Systems10.1145/361029328:6(1-23)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3610293
Pei ZZhao WHe ZYu B(2023)Bit-Level Quantization for Efficient Layout Hotspot Detection2023 International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA59274.2023.10218502(465-470)Online publication date: 8-May-2023
https://doi.org/10.1109/ISEDA59274.2023.10218502
Khan IMittal NAnsari M(2023)Applications of VLSI Design in Artificial Intelligence and Machine LearningMachine Learning for VLSI Chip Design10.1002/9781119910497.ch1(1-17)Online publication date: 23-Jun-2023
https://doi.org/10.1002/9781119910497.ch1
Gai TQu TWang SSu XXu RWang YXue JSu YWei YYe T(2022)Flexible Hotspot Detection Based on Fully Convolutional Network With Transfer LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.313578641:11(4626-4638)Online publication date: Nov-2022
https://doi.org/10.1109/TCAD.2021.3135786
Nath BBarai SKumar PSrinivasan BMohapatra N(2021)A Novel ML Augmented DRC Framework for Identification of Yield Detractor PatternsIEEE Transactions on Semiconductor Manufacturing10.1109/TSM.2021.308397334:3(379-386)Online publication date: Aug-2021
https://doi.org/10.1109/TSM.2021.3083973
Zhu BChen RZhang XYang FZeng XYu BWong M(2021)Hotspot Detection via Multi-task Learning and Transformer Encoder2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)10.1109/ICCAD51958.2021.9643590(1-8)Online publication date: 1-Nov-2021
https://doi.org/10.1109/ICCAD51958.2021.9643590
Chen YLin YGai TSu YWei YPan D(2020)Semi-Supervised Hotspot Detection with Self-Paced Multi-Task LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.2912948(1-1)Online publication date: 2020
https://doi.org/10.1109/TCAD.2019.2912948
Ye WAlawieh MHsu CLin YPan D(2020)Dealing with Aging and Yield in Scaled TechnologiesDependable Embedded Systems10.1007/978-3-030-52017-5_17(409-429)Online publication date: 10-Dec-2020
https://doi.org/10.1007/978-3-030-52017-5_17
Ye WAlawieh MLi MLin YPan D(2019)Litho-GPA: Gaussian Process Assurance for Lithography Hotspot Detection2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE.2019.8714960(54-59)Online publication date: Mar-2019
https://doi.org/10.23919/DATE.2019.8714960
He XDeng YZhou SLi RWang YGuo Y(2019)Lithography Hotspot Detection with FFT-based Feature Extraction and Imbalanced Learning RateACM Transactions on Design Automation of Electronic Systems10.1145/337204425:2(1-21)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3372044
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