Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations...

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Bibliographic Details
Main Authors: Natasha Dropka, Stefan Ecklebe, Martin Holena
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Crystals
Subjects:
neural networks
crystal growth
GaAs
process control
digital twins
Online Access:https://www.mdpi.com/2073-4352/11/2/138

Internet

https://www.mdpi.com/2073-4352/11/2/138

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