| Autor: |
Hiroshi Takahashi, Senling Wang, Tsutomu Inamoto, Tomokazu Nakamura, Kewal K. Saluja, Yoshinobu Higami |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). |
| DOI: |
10.1109/itc-cscc52171.2021.9501474 |
| Popis: |
Use of a fault dictionary is an effective and efficient method for deducing candidate faults during fault diagnosis process. It contains output responses for every test pattern and every target fault, and therefore the size of the fault dictionary for large LSIs tends to be very large. This paper proposes methods for compacting a fault dictionary without loss of diagnosis ability. We assume that output responses are compacted by an XOR tree compactor, and we investigate how we make the groups of primary outputs for which values are compacted by XOR operation. The methods introduce measures that are based on the number of distinguished fault pairs and the number of detecting test patterns. The effectiveness of the proposed methods is demonstrated by conducting experiments on a number of benchmark circuits. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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