Conference proceeding
On Using Design Partitioning to Reduce Diagnosis Memory Footprint
2011 Asian Test Symposium, pp.219-225
11/2011
DOI: 10.1109/ATS.2011.45
Abstract
Recently statistical yield learning based on volume diagnosis has become popular. Volume diagnosis requires a large amount of diagnosis results to be produced within a reasonable time. However, it is challenging to achieve the desired throughput for modern designs with continuously increasing size. In this paper, we propose a method to partition a design under diagnosis into blocks together with a diagnosis flow at the block level. The diagnosis throughput is improved because more diagnosis jobs can be run concurrently and each job runs faster due to the reduced memory. A measure is also proposed to estimate the impact on diagnosis caused by design partitioning. Experimental results on benchmark circuits and several industrial designs show that diagnosis using circuit blocks has minimal impact on diagnosis accuracy and resolution. It is also demonstrated that the proposed measure is a good metric in predicting the impact on diagnosis.
Details
- Title: Subtitle
- On Using Design Partitioning to Reduce Diagnosis Memory Footprint
- Creators
- Xiaoxin Fan - University of IowaHuaxing Tang - Mentor GraphicsSudhakar M Reddy - University of IowaWu-Tung Cheng - Mentor GraphicsBrady Benware - Mentor Graphics
- Resource Type
- Conference proceeding
- Publication Details
- 2011 Asian Test Symposium, pp.219-225
- DOI
- 10.1109/ATS.2011.45
- ISSN
- 1081-7735
- eISSN
- 2377-5386
- Publisher
- IEEE
- Language
- English
- Date published
- 11/2011
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197314602771
Metrics
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