Chen, Z., Raginsky, M. and Rosenbaum, E. (2017) Verilog-A Compatible Recurrent Neural Network Model for Transient Circuit Simulation. Proceedings of IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), San Jose, 15-18 October 2017.
https://doi.org/10.1109/EPEPS.2017.8329743

has been cited by the following article:

• TITLE: Deep Neural Network Based Behavioral Model of Nonlinear Circuits

  AUTHORS: Zhe Jin, Sekouba Kaba

  KEYWORDS: Nonlinear Circuits, Deep Neural Networks, Behavioral Model, Power Amplifier

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.9 No.3, March 11, 2021

  ABSTRACT: With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.