@inproceedings{ba4b60fe5c0e4b69bf9a1f9ae919f534,
title = "Machine learning for circuit aging simulation",
abstract = "The widespread availability of high-quality open source software for behavioral model optimization motivates the investigation of a behavioral approach to the modeling of aged circuits. A continuous-time formulation of a recurrent neural network (RNN) is compatible with transient circuit simulation, and this work evaluates RNN applicability to the modeling of aged circuits. For any reasonable input, the model should be required to produce an output response that is physically plausible. Approaches to imposing physical constraints on black-box models are outlined briefly.",
author = "E. Rosenbaum and J. Xiong and A. Yang and Z. Chen and M. Raginsky",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 66th Annual IEEE International Electron Devices Meeting, IEDM 2020 ; Conference date: 12-12-2020 Through 18-12-2020",
year = "2020",
month = dec,
day = "12",
doi = "10.1109/IEDM13553.2020.9371931",
language = "English (US)",
series = "Technical Digest - International Electron Devices Meeting, IEDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "39.1.1--39.1.4",
booktitle = "2020 IEEE International Electron Devices Meeting, IEDM 2020",
address = "United States",
}