31‐2: Student Paper: Fermi Level Prediction of Solution‐processed Ultra‐wide Band gap a‐Ga2Ox via Supervised Machine Learning Models. Issue 1 (28th June 2022)

• Record Type: Journal Article
• Authors: Purnawati, Diki; Regonia, Paul Rossener; Bermundo, Juan Paolo; Ikeda, Kazushi; Uraoka, Yukiharu
• Is Part Of: Digest of technical papers. Volume 53:Issue 1(2022)
• Page Start: 369
• Page End: 372
• Publication Date: 2022-06-28
• Subjects: a-Ga2Ox -- Ultra-wide band gap -- Fermi level -- Machine learning; Information display systems -- Congresses; 621.3815422
• DOI: 10.1002/sdtp.15497
• Languages: English
• ISSNs: 0097-966X

Abstract

Abstract : This work presents machine learning (ML) assisted Fermi level prediction of solution‐processed ultra‐wide bandgap (UWB) amorphous gallium oxide (a‐Ga2 Ox ) which can significantly accelerate the fabrication of semiconducting UWB a‐Ga2 Ox ‐based material for future display application. Different models such as Kernel Ridge Regression (KRR), Support Vector Regression (SVR) and Random Forest Regression (RFR) were trained with empirical features, including experimental thickness, annealing temperature and environment during the solution‐processed UWB a‐Ga2 Ox film fabrication. This work is a big step towards rapid and cost‐effective optimization method of fabricating UWB a‐Ga2 Ox ‐based devices.