Diagnosis of Scan Chain Faults Based-on Machine-Learning

Abstract

In order to improve yield of nanometer-scale chips, scan-based test and diagnosis are important. However, the scan chain can be subject to defects due to large hardware incurred by itself, which accounts for considerable portion of total chip area. Hence, scan chain test and diagnosis has played a critical role in recent years. In this paper, an efficient scan chain diagnosis method based on two-stage neural networks is proposed for not only stuck-at fault but also transition fault. Experimental results on benchmark circuits show that the proposed method is 10% more accurate than a previous work and CPU time for training the neural networks is also reduced dramatically.