@inproceedings{52757e68d65945c9993fde8c61f5771e,
title = "Neural Networks for Transient Modeling of Circuits: Invited Paper",
abstract = "Theoretical analyses as well as case studies have established that behavioral models based on a recurrent neural network (RNN) are suitable for transient modeling of nonlinear circuits. After training, an RNN model can be implemented in Verilog-A and evaluated by a SPICE-type circuit simulator. This paper describes hurdles that have prevented wide-scale adoption of the RNN as an IP-obscuring behavioral model for circuits and presents recent advances. A new stability constraint is formulated and demonstrated to guide model training and improve performance. Augmented RNNs that can accurately capture aging effects and represent process variations are presented.",
keywords = "circuit simulation, neural ODE, recurrent neural network, transient models",
author = "Jie Xiong and Yang, {Alan S.} and Maxim Raginsky and Elyse Rosenbaum",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd ACM/IEEE Workshop on Machine Learning for CAD, MLCAD 2021 ; Conference date: 30-08-2021 Through 03-09-2021",
year = "2021",
month = aug,
day = "30",
doi = "10.1109/MLCAD52597.2021.9531153",
language = "English (US)",
series = "2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 ACM/IEEE 3rd Workshop on Machine Learning for CAD, MLCAD 2021",
address = "United States",
}