[17p-K505-1~13] 23.1 Joint Session N "Informatics"

3:45 PM - 4:00 PM JST(6:45 AM - 7:00 AM UTC)

[17p-K505-11] Principal Component Analysis and Machine Learning of Transmitted THz Waves in Single-Layer Graphene

〇(M1)Limin Zhao¹, Suguru Yamauchi², Shin-ichiro Yanagiya^2,3, Yasuhide Ohno², Masao Nagase², Yasuo Minami¹ (1.Nihon Univ., 2.Tokushima Univ., 3.pLED Inst., Tokushima Univ.)

Keywords:

Machine Learning,Terahertz Time Domain Spectroscopy (THz-TDS),Principal Component Analysis(PCA)

Transmission waveforms were acquired for single layer graphene fabricated on SiC substrates using Terahertz Time Domain Spectroscopy (THz-TDS). Transmission was imaged by calculating the power spectrum at each measurement point, and Principal Component Analysis (PCA) was used to evaluate dry air, graphene and mounts. The samples were also identified by machine learning using Random Forest and Convolutional Neural Network (CNN). These results show that it is possible to evaluate materials based on their THz wave transmission properties.