Circuit Connectivity Inspired Neural Network for Analog Mixed-Signal Functional Modeling

Among different types of regression methods to model Analog/Mixed-Signal (AMS) circuits, the Artificial Neural Network (ANN) is a promising candidate due to its reasonable accuracy and fast evaluation. However, for complex AMS circuits with wide specification ranges, creating an ANN model requires a large training dataset. To reduce the required training dataset's volume, we have proposed a circuit-connectivity-inspired ANN (CCI-NN), including multiple sub-ANNs linked according to the actual circuit connections. For validation, we have employed CCI-NN to model a three-stage amplifier and a current-steering digital-to-analog converter. For a certain modeling accuracy, the training dataset requirement is reduced by 3.5x-7.6x.