[11p-N107-1~16] 23.1 Joint Session N "Informatics"

[11p-N107-16] Prediction of carrier recombination velocity of grain boundaries from photoluminescence intensity profile using machine learning

〇Kentaro Kutsukake¹, Kazuki Mitamura², Takuto Kojima³, Noritaka Usami² (1.AIP RIKEN, 2.Grad. Eng. Nagoya Univ., 3.Grad. Info. Nagoya Univ.)

Keywords:machine learning, simulation, photoluminescence

We applied machine learning to estimate recombination velocity directly from the photoluminescence intensity profile. In this presentation, we report on the improvement of prediction accuracy by adding information on the grain boundary inclination angle, and the evaluation of recombination velocity distribution along the grain boundaries.