A Low-Power BIST Scheme using Weight-Aware Scan Grouping and Scheduling for Automotive ICs

Kwonhyoung Lee, Sangjun Lee, Jongho Park, Inhwan Lee, Sungho Kang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Scan-based logic built-in self-test (LBIST) is widely used for supporting the in-system test in automotive systems. Although this technology has the advantage of low-cost testing, it suffers from low fault coverage and high switching activity during the test. This can lead to many undetected defects, excessive heat dissipation, and IR drop, inducing catastrophic risks to functional safety. Therefore, improving these two key factors is crucial to alleviate reliability problems in the automotive domain. Most previous works have focused on controlling the enormous toggling level of random patterns; however, one of the main disadvantages of these approaches is low fault coverage. Unfortunately, additional hardware costs associated with memory elements or test points are required for detecting the remaining faults. We propose a novel LBIST scheme based on weight-aware scan grouping and scheduling (WGS) to overcome these difficulties. Since the required test time of each automotive product is limited, the proposed scheme freezes the test time and focuses on improving both aforementioned factors significantly. Our approach divides scan cells into two categories: the coverage-efficient scan group and power-efficient scan group, and then it conducts weight-based scan cell reordering. Biased random patterns are fed to enhance fault coverage for the first category. For the second category, scheduling and disabling are performed to reduce switching activity. Finally, physical-aware reordering based on an inverter is performed to reduce routing overhead. Experimental results demonstrate the feasibility of the WGS methodology on the ITC’99 and OpenRISC benchmark circuits.

Original languageEnglish
JournalIEEE Access
Publication statusAccepted/In press - 2021

Bibliographical note

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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