Machine Learning Based Fault Diagnosis for Stuck-at Faults and Bridging Faults
Yoshinobu Higami, Takaya Yamauchi, Tsutomu Inamoto, Senling Wang, Hiroshi Takahashi, Kewal K. Saluja · 2022
This paper presents a fault diagnosis method using a machine learning technique. The method neither needs to perform fault simulation nor it needs to store fault dictionaries in deducing candidate faults. The output responses of a circuit under diagnosis are applied to a trained neural network, and candidate faults are obtained as a result. The paper also investigates the generation of data that are used to train the neural network. The effectiveness of the proposed method is shown by the experimental results for benchmark circuits.