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Analysis of security of split manufacturing using machine learning

Published: 24 June 2018 Publication History

Abstract

This work is the first to analyze the security of split manufacturing using machine learning, based on data collected from layouts provided by industry, with 8 routing metal layers, and significant variation in wire size and routing congestion across the layers. We consider many types of layout features for machine learning including those obtained from placement, routing, and cell sizes. For the top split layer, we demonstrate dramatically better results in proximity attack compared to a recent prior work. We analyze the ranking of the features used by machine learning and show the importance of how features vary when moving to the lower layers. Since the runtime of our basic machine learning becomes prohibitively large for lower layers, we propose novel techniques to make it scalable with little sacrifice in effectiveness of the attack.

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Cited By

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  • (2023)Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding StudyMachines10.3390/machines1102029811:2(298)Online publication date: 17-Feb-2023
  • (2019)Attacking Split Manufacturing from a Deep Learning PerspectiveProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317780(1-6)Online publication date: 2-Jun-2019
  • (2019)Protect Your Chip Design Intellectual PropertyProceedings of the International Conference on Omni-Layer Intelligent Systems10.1145/3312614.3312657(211-216)Online publication date: 5-May-2019
  • Show More Cited By

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cover image ACM Conferences
DAC '18: Proceedings of the 55th Annual Design Automation Conference
June 2018
1089 pages
ISBN:9781450357005
DOI:10.1145/3195970
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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Publication History

Published: 24 June 2018

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Author Tags

  1. machine learning
  2. reverse engineering
  3. split manufacturing

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DAC '18
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DAC '18: The 55th Annual Design Automation Conference 2018
June 24 - 29, 2018
California, San Francisco

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

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Cited By

View all
  • (2023)Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding StudyMachines10.3390/machines1102029811:2(298)Online publication date: 17-Feb-2023
  • (2019)Attacking Split Manufacturing from a Deep Learning PerspectiveProceedings of the 56th Annual Design Automation Conference 201910.1145/3316781.3317780(1-6)Online publication date: 2-Jun-2019
  • (2019)Protect Your Chip Design Intellectual PropertyProceedings of the International Conference on Omni-Layer Intelligent Systems10.1145/3312614.3312657(211-216)Online publication date: 5-May-2019
  • (2012)IntroductionThe Next Era in Hardware Security10.1007/978-3-030-85792-9_1(1-34)Online publication date: 24-Feb-2012

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