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LBIST for Automotive ICs with Enhanced Test Generation


[ 1 ] Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Published in

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Journal year: 2022 | Journal volume: vol. 41 | Journal number: no. 7

Article type

scientific article

Publication language


  • embedded-test
  • logic built-in self-test
  • LFSR reseeding
  • scan-based testing
  • test application time

EN Contemporary and emergent automotive systems are heavily populated by complex integrated electronics. The number of safety-critical devices used in advanced driver-assistance systems or autonomous vehicles is growing with high-end models containing hundreds of embedded microcontrollers. Achieving functionally safe automotive electronics requires test solutions that might be costly to engineer. Therefore, to address challenges posed by high-quality and long-term reliability requirements, this article presents low-cost test pattern generation schemes for a scan-based hybrid logic BIST of automotive ICs. It may allow one to optimize test coverage and test time during in-system test applications. The first presented technique deploys a seed-flipping PRPG to periodically complement PRPG stages in a methodical tree-traversal manner. The second scheme is based on a seed-sorting approach that allows additional tradeoffs between test data volume and test coverage. As shown in this article, the proposed schemes can be easily integrated with a test compression environment and deployed in different modes of in-system testing, such as key-off, key-on, and periodic (incremental) online tests. Experimental results obtained for automotive designs and reported herein show improvements in test quality over conventional logic BIST schemes.

Date of online publication


Pages (from - to)

2290 - 2300




Points of MNiSW / journal


Impact Factor

2.807 [List 2020]

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