Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition-Reference-Cited by-同舟云学术

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Multiscale Physics-Informed Neural Network Framework to Capture Stochastic Thin-Film Deposition

Published:2024-02-27 Issue:9 Volume:128 Page:3733-3750
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Chaffart Donovan¹,Yuan Yue²,Ricardez-Sandoval Luis A.³^ORCID

Affiliation:

1. College of Engineering, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China

2. Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang 315200, China

3. Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

Funder

Government of Canada

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.3c07168

Reference70 articles.

1. Multiscale Modeling of Thin-Film Deposition: Applications to Si Device Processing

2. Model reduction for optimization of rapid thermal chemical vapor deposition systems

3. Estimation and control of surface roughness in thin film growth using kinetic Monte-Carlo models

4. Model Predictive Control of ${\hbox{Si}}_{1-x}{\hbox{Ge}}_{x}$ Thin Film Chemical–Vapor Deposition

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1. Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges;AI;2024-08-29

2. Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2;The Journal of Physical Chemistry A;2024-07-11

3. Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2;The Journal of Physical Chemistry B;2024-07-11

4. Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2;The Journal of Physical Chemistry C;2024-07-11

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