[16a-WL1_301-1~11] 12.7 Biomedical Engineering and Biochips

9:45 AM - 10:00 AM JST(12:45 AM - 1:00 AM UTC)

[16a-WL1_301-4] Virus Detection Using a Graphene FET Sensor with Supervised Learning

〇Daisuke Hirose¹, Kazuhiko Matsumoto², Yano Mamiko², Kiyoji Sakano², Kaori Yamamoto², Eriko Ohnishi², Ayano Fukumura², Yuzuru Takamura¹ (1.JAIST, 2.SANKEN Osaka Univ.)

Keywords:

supervised learning,graphene FET sensor,virus detection

Rapid and highly sensitive virus detection technologies are required. In this study, we investigated the effectiveness of supervised learning in addressing the difficulty of detection and classification caused by the high sensitivity of graphene FET sensors. The Vg–Id characteristics were standardized and subjected to principal component analysis, followed by classification using a random forest.As a result, we achieved an accuracy exceeding 95%, and found that characteristic changes near the Dirac point play a crucial role in the classification.