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Article


Title

LBIST for Automotive ICs with Enhanced Test Generation

Authors

[ 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

2022

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

english

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

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

28.07.2022

Pages (from - to)

2290 - 2300

DOI

10.1109/TCAD.2021.3100741

URL

https://ieeexplore.ieee.org/document/9499119

Points of MNiSW / journal

100.0

Impact Factor

2.807 [List 2020]

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