Improving scan chain diagnostic accuracy using multi-stage artificial neural networks
| Autor: | Gaurav Veda, Shih-Wei Lee, Mason Chern, Kun-Han Tsai, Wu-Tung Cheng, Yu Huang, Shi-Yu Huang |
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| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Process (computing) Scan chain Pattern recognition 02 engineering and technology 020202 computer hardware & architecture 0202 electrical engineering electronic engineering information engineering Key (cryptography) Hit rate Benchmark (computing) Domain knowledge 020201 artificial intelligence & image processing Artificial intelligence business Electronic circuit |
| Zdroj: | ASP-DAC |
| Popis: | Diagnosis of intermittent scan chain failures remains a hard problem. We demonstrate that Artificial Neural Networks (ANNs) can be used to achieve significantly higher accuracy. The key is to take on domain knowledge and use a multi-stage process incorporating ANNs with gradually refined focuses. Experimental results on benchmark circuits show that this method is, on average, 20% more accurate than a state-of-the-art commercial tool for intermittent stuck-at faults, and improves the hit rate from 25.3% to 73.9% for some test-case. |
| Databáze: | OpenAIRE |
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