research-article

Detecting hardware trojans using backside optical imaging of embedded watermarks

Authors:

Mahmoud Zangeneh,

Bennett Goldberg,

Ajay JoshiAuthors Info & Claims

DAC '15: Proceedings of the 52nd Annual Design Automation Conference

Article No.: 111, Pages 1 - 6

https://doi.org/10.1145/2744769.2744822

Published: 07 June 2015 Publication History

Abstract

Hardware Trojans are a critical security threat to integrated circuits. We propose an optical method to detect and localize Trojans inserted during the chip fabrication stage. We engineer the fill cells in a standard cell library to be highly reflective at near-IR wavelengths so that they can be readily observed in an optical image taken through the backside of the chip. The pattern produced by their locations produces an easily measured watermark of the circuit layout. Replacement, modification or re-arrangement of these cells to add a Trojan can therefore be detected through rapid post-fabrication backside imaging. We evaluate our approach using various hardware blocks where the Trojan circuit area is less than 0.1% of the total area and it consumes less than 2% leakage power of the entire chip. In addition, we evaluate the tolerance of our methodology to background measurement noise and process variation.

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Cited By

Vidaković MVinko D(2023)Hardware-Based Methods for Electronic Device Protection against Invasive and Non-Invasive AttacksElectronics10.3390/electronics1221450712:21(4507)Online publication date: 2-Nov-2023
https://doi.org/10.3390/electronics12214507
Trippel TShin KBush KHicks M(2023)T-TER: Defeating A2 Trojans with Targeted Tamper-Evident RoutingProceedings of the 2023 ACM Asia Conference on Computer and Communications Security10.1145/3579856.3582837(746-759)Online publication date: 10-Jul-2023
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Dong MPan WQiu ZGao YQi XZheng L(2022)An Efficient Framework with Node Filtering and Load Expansion for Machine-Learning-Based Hardware Trojan DetectionElectronics10.3390/electronics1113205411:13(2054)Online publication date: 30-Jun-2022
https://doi.org/10.3390/electronics11132054
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Index Terms

Detecting hardware trojans using backside optical imaging of embedded watermarks

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cover image ACM Conferences

DAC '15: Proceedings of the 52nd Annual Design Automation Conference

June 2015

1204 pages

ISBN:9781450335201

DOI:10.1145/2744769

Copyright © 2015 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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Published: 07 June 2015

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DAC '15: The 52nd Annual Design Automation Conference 2015

June 7 - 11, 2015

California, San Francisco

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

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Cited By

Vidaković MVinko D(2023)Hardware-Based Methods for Electronic Device Protection against Invasive and Non-Invasive AttacksElectronics10.3390/electronics1221450712:21(4507)Online publication date: 2-Nov-2023
https://doi.org/10.3390/electronics12214507
Trippel TShin KBush KHicks M(2023)T-TER: Defeating A2 Trojans with Targeted Tamper-Evident RoutingProceedings of the 2023 ACM Asia Conference on Computer and Communications Security10.1145/3579856.3582837(746-759)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3579856.3582837
Dong MPan WQiu ZGao YQi XZheng L(2022)An Efficient Framework with Node Filtering and Load Expansion for Machine-Learning-Based Hardware Trojan DetectionElectronics10.3390/electronics1113205411:13(2054)Online publication date: 30-Jun-2022
https://doi.org/10.3390/electronics11132054
Vashistha NLu HShi QWoodard DAsadizanjani NTehranipoor M(2022)Detecting Hardware Trojans Using Combined Self-Testing and ImagingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.309874041:6(1730-1743)Online publication date: Jun-2022
https://doi.org/10.1109/TCAD.2021.3098740
Vashistha NAl Hasan MAsadizanjani NRahman FTehranipoor M(2022)Trust Validation of Chiplets using a Physical Inspection based Certification Authority2022 IEEE 72nd Electronic Components and Technology Conference (ECTC)10.1109/ECTC51906.2022.00365(2311-2320)Online publication date: May-2022
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Mukherjee RRajendran SChakraborty R(2022)A comprehensive survey of physical and logic testing techniques for Hardware Trojan detection and preventionJournal of Cryptographic Engineering10.1007/s13389-022-00295-w12:4(495-522)Online publication date: 16-Jul-2022
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Tehranipoor MPundir NVashistha NFarahmandi FTehranipoor MPundir NVashistha NFarahmandi F(2022)Embedded WatermarksHardware Security Primitives10.1007/978-3-031-19185-5_11(185-211)Online publication date: 12-Oct-2022
https://doi.org/10.1007/978-3-031-19185-5_11
Faezi SYasaei RAl Faruque M(2021)HTnet: Transfer Learning for Golden Chip-Free Hardware Trojan Detection2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9474076(1484-1489)Online publication date: 1-Feb-2021
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Zhang HZhou JGao DWang XChen ZWang H(2021)Hardware Trojan Detection Based on Ordered Mixed Feature GEPSecurity and Communication Networks10.1155/2021/66826742021Online publication date: 1-Jan-2021
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Sharma RSharma GPattanaik M(2021)A Few Shot Learning based Approach for Hardware Trojan Detection using Deep Siamese CNN2021 34th International Conference on VLSI Design and 2021 20th International Conference on Embedded Systems (VLSID)10.1109/VLSID51830.2021.00033(163-168)Online publication date: Feb-2021
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