[17p-PB07-1~15] 15.4 III-V-group nitride crystals

[17p-PB07-5] Study on prediction of bottom area of GaN:Eu nanowires using machine learning

〇Taketo Matsuyama¹, Takaya Otabara², Kyoko Kitamura^1,2, Jun Tatebayashi^2,3, Yasufumi Fujiwara² (1.Kyoto Inst. Tech., 2.Grad. Sch. Eng., Osaka Univ., 3.QIQB, Osaka Univ.)

Keywords:nanowire, machine learning, rare earth

We report on the growth prediction system of GaN:Eu/GaN core-shell nanowires (NWs) grown by organometallic vapor phase epitaxy (OMVPE) using machine learning. In this paper, we established systems which predict the distribution of bottom areas of NWs based on SEM images of NWs grown by OMVPE and growth parameters including mask pattern diameter, growth temperature, V/III ratio and Eu molar flow. From the predictions with various growth conditions, the system shows the average correlation coefficient of ~0.75 and the result indicates enough consistency with experimental results.