Preliminary Study on Noise-Resilient Artificial Neural Networks for On-Chip Test Generation
Preliminary Study on Noise-Resilient Artificial Neural Networks for On-Chip Test Generation
复制标题
用于片上测试生成的抗噪声人工神经网络的初步研究
DOI：
10.1109/gcce56475.2022.10014218
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yoshinobu Higami and Hiroshi Takahashi
中科院分区：
文献类型：
--
作者：
Tsutomu Inamoto;Tomoki Nishino;Senling Wang;Yoshinobu Higami and Hiroshi Takahashi
在本文中，我们提出了一个内置的测试模式生成机制，可以包含人工神经网络（ANN）作为测试模式生成器（ANN-TPG）。实施ANN-TPG的主要问题是噪音问题。因此，我们提出了一个模型，认为噪声作为人工神经网络中的噪声层。我们还揭示了测试模式生成的准确性的ANN-TPG考虑噪声的影响。
In this paper, we propose a builtin test pattern generation mechanism that can contain artificial neural networks (ANNs) as the test pattern generator (ANN-TPG). The primary concern of implementing the ANN-TPG is the noise issue. Therefore, we propose a model that considers the noise as the noise layers in ANNs. We also reveal the accuracy of the test pattern generation by the ANN-TPG with considering the noise affection.