@inproceedings{4cad8628cd984534a029dd3f4c2bd094,
title = "Machine learning compact device models applied to optoelectronic memristor",
abstract = "Machine learning compact device models (CM) have emerged as an alternative to physical CMs in terms of high flexibility and short development time. Based on our previous effort (Tran. Elec. Dev. Vol. 69, p1835), we have applied the same approach to optoelectronic memristors, and the flexibility of ML CMs is observed. The python/Tensorflow model is constructed to fit the devices, and Verilog-A and HSPICE are used for circuits. The fitting mean square error is 2.816 times 10{-12}} A2 in current and 0.00157 in the state, and a set-read-reset-read cycles in circuits in the dark and under illumination are demonstrated.",
keywords = "compact device modeling, machine learning, memristors, optoelectronic resistive random access memory (ORRAM)",
author = "Albert Lin and Tejender Rawat and Hsu, {Ming Hsien} and Chang, {Chung Yuan} and Tung, {Han Chun} and Tseng, {Tseung Yuen}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Photonics Conference, IPC 2022 ; Conference date: 13-11-2022 Through 17-11-2022",
year = "2022",
doi = "10.1109/IPC53466.2022.9975463",
language = "English",
series = "2022 IEEE Photonics Conference, IPC 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE Photonics Conference, IPC 2022 - Proceedings",
address = "United States",
}