Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga<sub>2</sub>O<sub>3</sub> Thin Films by Deep-Learning Approach

Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga<sub>2</sub>O<sub>3</sub> Thin Films by Deep-Learning Approach

In this work, we train a hybrid deep-learning model (fDNN, Forest Deep Neural Network) to predict the doping level measured from the Hall Effect measurement at room temperature and to investigate the doping behavior of Si dopant in both (100) and (010) β-Ga<sub>2</sub>O<sub>3</s...

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Bibliographic Details
Main Authors: Ta-Shun Chou, Saud Bin Anooz, Raimund Grüneberg, Klaus Irmscher, Natasha Dropka, Jana Rehm, Thi Thuy Vi Tran, Wolfram Miller, Palvan Seyidov, Martin Albrecht, Andreas Popp
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Crystals
Subjects:
β-Ga<sub>2</sub>O<sub>3</sub>
deep learning
doping
MOVPE
Online Access:https://www.mdpi.com/2073-4352/12/1/8

Internet

https://www.mdpi.com/2073-4352/12/1/8

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