[20p-C32-1~11] 16.3 Bulk, thin-film and other silicon-based solar cells

3:00 PM - 3:15 PM JST(6:00 AM - 6:15 AM UTC)English Presentation

[20p-C32-8] Application of a Machine Learning Method, Random Forest, to the Deposition Conditions of Doped Amorphous Silicon Films

〇CHENXI LI¹, Huynh Thi Cam Tu¹, Keisuke Ohdaira¹ (1.JAIST)

Keywords:

amorphous silicon,machine learning,random forest

This study explores the use of the Random Forest (RF) algorithm within the ORANGE open-source data mining tool to predict deposition conditions for achieving high conductivity in amorphous silicon (a-Si) films. The model utilized k-fold cross-validation and Spearman correlations (SC) to enhance predictive accuracy. We demonstrated that the RF algorithm can reasonably predict the experimental conditions required for higher conductivity in both n-type and p-type a-Si films, offering significant potential for future advancements in the field.