Scan‐chain‐fault diagnosis using regressions in cryptographic chips for wireless sensor networks

Hyunyul Lim, Minho Cheong, Sungho Kang

Research output: Contribution to journalArticlepeer-review

Abstract

Scan structures, which are widely used in cryptographic circuits for wireless sensor networks applications, are essential for testing very‐large‐scale integration (VLSI) circuits. Faults in cryptographic circuits can be effectively screened out by improving testability and test coverage using a scan structure. Additionally, scan testing contributes to yield improvement by identifying fault locations. However, faults in circuits cannot be tested when a fault occurs in the scan structure. Moreover, various defects occurring early in the manufacturing process are expressed as faults of scan chains. Therefore, scan‐chain diagnosis is crucial. However, it is difficult to obtain a sufficiently high diagnosis resolution and accuracy through the conventional scan‐chain diagnosis. Therefore, this article proposes a novel scan‐chain diagnosis method using regression and fan‐in and fan‐out filters that require shorter training and diagnosis times than existing scan‐chain diagnoses do. The fan‐in and fan‐out filters, generated using a circuit logic structure, can highlight important features and remove unnecessary features from raw failure vectors, thereby converting the raw failure vectors to fan‐in and fan‐out vectors without compromising the diagnosis accuracy. Experimental results confirm that the proposed scan‐chain‐diagnosis method can efficiently provide higher resolutions and accuracies with shorter training and diagnosis times.

Original languageEnglish
Article number4771
Pages (from-to)1-23
Number of pages23
JournalSensors (Switzerland)
Volume20
Issue number17
DOIs
Publication statusPublished - 2020 Sep 1

Bibliographical note

Funding Information:
Funding: This research was supported by Multi‐Ministry Collaborative R&D Program (R&D program for complex cognitive technology) through the National Research Foundation of Korea (NRF) funded by MOTIE (2018M3E3A1057248).

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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