Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga2Ox)
Machine-Learned Fermi Level Prediction of Solution-Processed Ultrawide-Bandgap Amorphous Gallium Oxide (a-Ga2Ox)
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DOI：
10.1021/acsaelm.2c01013
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发表时间：
2022-11
影响因子：
4.7
通讯作者：
D. Purnawati;Paul Rossener Regonia;J. Bermundo;K. Ikeda;Y. Uraoka
中科院分区：
文献类型：
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作者：
D. Purnawati;Paul Rossener Regonia;J. Bermundo;K. Ikeda;Y. Uraoka
费米能级（EF）相对于导带最小值的位置是控制薄膜晶体管、传感器和光电探测器中的导电性和半导体器件性能的关键考虑因素。通过实验方法预测EF的实验复杂性和昂贵的材料资源使得机器学习（ML）方法更合适。本工作提出了ML辅助EF预测的溶液处理超宽带（UWB）的非晶氧化镓（a-Ga 2 Ox）。三个回归模型-核岭回归，支持向量回归，和随机森林回归-训练与实验功能，包括薄膜厚度，烘烤温度，和气体环境在溶液沉积的a-Ga 2 Ox薄膜。结果表明，ML模型可以用来预测超宽带a-Ga_2 O_x薄膜的EF，并确定优化的工艺参数，以实现最佳的EF。此外，ML方法可以显着加速半导体UWB a-Ga 2 Ox基材料的制造，用于未来的器件应用。这项工作是朝着开发UWB a-Ga 2 Ox基器件的快速和具有成本效益的优化方法迈出的一大步。
The Fermi level (EF) relative position to the conduction band minimum is a crucial consideration for controlling electrical conductivity and semiconductor device performance in thin-film transistors, sensors, and photodetectors. Experiment complexity and expensive material resources for predictingEFvia an experimental approach render a machine learning (ML) approach to be more appropriate. This work presents ML-assistedEFprediction of solution-processed ultrawide-bandgap (UWB) amorphous gallium oxide (a-Ga2Ox). Three regression models─kernel ridge regression, support vector regression, and random forest regression─were trained with experimental features including the film thickness, baking temperature, and gas environment during solution deposition of the a-Ga2Oxfilm. The results show that ML models can be used to predictEFof the UWB a-Ga2Oxfilm and also identify optimized fabrication parameters to achieve the optimizedEF. Moreover, the ML approach can significantly accelerate the fabrication of semiconducting UWB a-Ga2Ox-based material for future device applications. This work is a big step toward rapid and cost-effective optimization methods for developing UWB a-Ga2Ox-based devices.