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A Novel Adaptive Testing Method Using Convolutional Neural Networks
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DOI：10.5573/JSTS.2025.54.5.610.png)
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Abstract
En 中文
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.
Keywords:
Adaptive testing
circuit
convolutional neural networks
fast Fourier transform
test pattern
Journal
J
IF:
0.8
Papers:
51
Citations:
0