基于自注意力机制的深度学习模拟电路故障诊断

模拟电路是集成电路中的重要组成部分，基于深度学习技术对模拟电路发生的故障进行检测，并精准识别故障的类型是当前集成电路测试领域的研究热点。针对模拟集成电路故障检测存在困难的问题，利用人工智能在图像识别领域、语音分类领域的先进技术，提出了基于自注意力机制检测Sallen-Key型低通滤波电路故障的深度学习模拟电路故障检测方案，将输出信号采样成音频信号，并将其输入到自注意力变换网络的音频分类模型中进行训练、测试和优化。结果表明，通过自注意力变换网络音频分类在9种不同的故障类型诊断中，平均准确率达93.1%,最高准确率达98.1%。该模型收敛速度更快，具有较强的模拟电路故障检测能力。

A fault diagnosis algorithm for analog circuits based on self-attention mechanism deep learning

Analog circuit is an essential part of the integrated circuit. One of the current research hotspots in integrated circuit testing isthe detection of faults occurring in analog circuits and the accurate identification of fault types based on deep learning techniques. Toaddress the difficulties in fault detection of analog integrated circuits, the advanced achievements of artificial intelligence in the field ofimage recognition and speech classification is referenced and an analog circuit fault detection idea based on a deep learning algorithm ofself-attention mechanism is proposed, which can be used to detect faults in Sallen-Key low-pass filter circuits. The output signal issampled into an audio signal and fed into an audio classification model based on a self-attentive transform network for training, testing,and optimization. The results show that fault detection based on the self-attentive mechanism audio classification has an average accuracyof 93. 1% and a maximum accuracy of 98. 1% . Nine different fault types can be detected. The model converges fast and can detect faultsin analog circuits, which thoroughly verifies the feasibility of the proposed idea.