Modeling and Circuit Implementation of Complex Nonlinear Component Behavior Based on Attention-Assisted BiLSTM

Due to the strong generalization and fitting capabilities of neural networks, behavior modeling techniques based on neural networks have been gradually applied in the modeling process of nonlinear components or circuit modules. However, ordinary neural networks still fail to achieve a high level of fitting for behavior data with more complex nonlinearity, and most modeling techniques are still only used in numerical simulations on computers and have not been practically applied in system simulations as circuit units. This paper proposes a hybrid network architecture that integrates bidirectional LSTM and additive attention mechanisms for behavior modeling of PIN limiter diodes and compares it with the equivalent circuit model of PIN diodes in ADS software. Finally, we define the proposed PIN diode neural network model in VerilogA language and use the encapsulated VerilogA component for SPICE circuit simulation, verifying the effectiveness of the model in this paper.