Conference proceeding
Deterministic Stellar BIST for In-System Automotive Test
2018 IEEE International Test Conference (ITC), Vol.2018-, pp.1-9
10/2018
DOI: 10.1109/TEST.2018.8624872
Abstract
With the growing number of very complex safety-critical components used in advanced driver assistance systems and autonomous vehicles, integrated circuits in this area must adhere to stringent requirements for high quality and long-term reliability driven by functional safety standards. This, in turn, requires advanced test solutions that have to respond to challenges posed by automotive parts. This paper presents Stellar BIST - a deterministic two-level compression scheme for in-system automotive test. The proposed solution seamlessly integrates with any sequential test compression scheme and takes advantage of the fact that certain clusters of test vectors detect many random-resistant faults where a cluster consists of a parent pattern and its transformed derivatives. Stellar BIST involves generating vectors based on simultaneous and multiple complements of scan slices of encodable parent patterns. The multiple complements are also skewed between successive patterns to diversify the resultant tests. The new scheme elevates compression to values unachievable through conventional reseeding-based solutions and provides significant trade-offs between area and time, critical for in-system automotive applications. Experimental results obtained for large industrial designs with stuck-at and transition faults illustrate feasibility of the proposed test scheme and are reported herein.
Details
- Title: Subtitle
- Deterministic Stellar BIST for In-System Automotive Test
- Creators
- Yingdi Liu - University of IowaNilanjan Mukherjee - Mentor GraphicsJanusz Rajski - Mentor GraphicsSudhakar M Reddy - University of IowaJerzy Tyszer - Poznań University of Technology
- Resource Type
- Conference proceeding
- Publication Details
- 2018 IEEE International Test Conference (ITC), Vol.2018-, pp.1-9
- DOI
- 10.1109/TEST.2018.8624872
- ISSN
- 1089-3539
- eISSN
- 2378-2250
- Publisher
- IEEE
- Language
- English
- Date published
- 10/2018
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197324002771
Metrics
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