Variation-Aware Small Delay Fault Diagnosis on Compressed Test Responses
| Autor: | Hans-Joachim Wunderlich, Michael A. Kochte, Stefan Holst, Xiaoqing Wen, Eric Schneider |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Matching (statistics) Computer science 020208 electrical & electronic engineering Population Process (computing) Test compression 02 engineering and technology Fault (power engineering) 020202 computer hardware & architecture Process variation Computer engineering Scalability 0202 electrical engineering electronic engineering information engineering Benchmark (computing) education |
| Zdroj: | ITC |
| DOI: | 10.1109/itc44170.2019.9000143 |
| Popis: | With today's tight timing margins, increasing manufacturing variations, and new defect behaviors in FinFETs, effective yield learning requires detailed information on the population of small delay defects in fabricated chips. Small delay fault diagnosis for yield learning faces two main challenges: (1) production test responses are usually highly compressed reducing the amount of available failure data, and (2) failure signatures not only depend on the actual defect but also on omnipresent and unknown delay variations. This work presents the very first diagnosis algorithm specifically designed to diagnose timing issues on compressed test responses and under process variations. An innovative combination of variation-invariant structural analysis, GPU-accelerated time-simulation, and variation-tolerant syndrome matching for compressed test responses allows the proposed algorithm to cope with both challenges. Experiments on large benchmark circuits clearly demonstrate the scalability and superior accuracy of the new diagnosis approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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