Attention Mechanism Combined With Deep Recurrent Network for Nonlinear Circuit Macromodeling

This article proposes a novel macromodeling method for high-frequency nonlinear circuits, utilizing an attention-based deep recurrent neural network (ATDRNN). This method leverages the attention mechanism within the RNN, comparing each time step with other time steps to determine their similarities. It then applies some coefficients as weights to the features of each time step based on these similarities, enhancing the RNN’s ability to focus on more informative features. Consequently, this approach allows for more accurate modeling of nonlinear circuits. Additionally, having comprehensive signal information and similarities between various time steps mitigates the vanishing gradient problem commonly faced by RNNs. The models derived from this method not only exhibit superior accuracy compared to the conventional RNNs, but also run much faster than existing transistor-level models in circuit simulators. The effectiveness of the proposed method is demonstrated by modeling two nonlinear circuits, namely 2-coupled and 3-coupled line high-speed interconnects driven by multi-stage buffers.