This paper focuses on the design and validation of an analog artificial neural network. Basic building blocks of the analog ANN have been constructed in UMC 90 nm device technology. Performance metrics of the building blocks have been demonstrated through circuit simulations. The weights of the ANN have been estimated through an automated back-propagation algorithm, which is running circuit simulations during weight optimization. Two case studies, the operation an XOR logic gate and a full adder circuit have been captured using the proposed analog ANN. Monte Carlo analysis of the XOR gate reveals that the analog ANN operates with an accuracy of 99.85%.

中文翻译：

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基于模拟电路的人工神经网络的设计与验证

本文着重于模拟人工神经网络的设计和验证。模拟人工神经网络的基本构件已采用UMC 90 nm器件技术构建。通过电路仿真已证明了构建块的性能指标。ANN的权重已通过自动反向传播算法进行了估算，该算法在权重优化期间运行电路仿真。使用建议的模拟ANN捕获了两个案例研究，即XOR逻辑门操作和完整的加法器电路。异或门的蒙特卡洛分析表明，模拟ANN的工作精度为99.85％。