| Title |
A Novel Adaptive Testing Method Using Convolutional Neural Networks |
| Authors |
(DaeRyong Shin) ; (SuMin Oh) ; (WanSoo Kim) ; (HyunJin Kim) |
| DOI |
https://doi.org/10.5573/JSTS.2025.25.5.610 |
| Keywords |
Adaptive testing; circuit; convolutional neural networks; fast Fourier transform; test pattern |
| Abstract |
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. |