A Novel Adaptive Testing Method Using Convolutional Neural Networks

In this work, we present a novel adaptive testing method based on convolutional neural networks (CNNs). We propose a conversion method of test patterns into spectral images using the fast Fourier transform (FFT), which enables consistent dimensionality across various circuits and allows the CNN to extract frequency-domain features. Moreover, we investigate the effect of different types of spectral images by comparing a single-channel magnitude image with a multi-channel image that includes magnitude, real, and imaginary parts. Experimental results on the ISCAS ’85 benchmark circuits show that the proposed method achieves over 95% accuracy with a maximum of 97% reduced parameters compared to MLP and conventional CNNs. Therefore, we demonstrate the effectiveness and scalability of the proposed method for adaptive testing.