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논문 기본 정보

자료유형
학술저널
저자정보
U Jin Cho (Gangneung-Wonju National University) Youhyeong Jeon (Gangneung-Wonju National University) Sung Wook Park (Gangneung-Wonju National University) Min-Woo Kwon (Seoul National University of Science and Technology)
저널정보
대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.24 No.5
발행연도
2024.10
수록면
393 - 398 (6page)
DOI
10.5573/JSTS.2024.24.5.393

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초록· 키워드

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This paper presents a proof of concept that combines a nano-composite hydrogen detecting sensor and machine-learning technique to achieve accurate and fast detection of hydrogen leakage. The nano-composite hydrogen detecting sensor is fabricated by depositing MoS₂ on a SiO₂/Si wafer using chemical vapor deposition, followed by forming discrete Pd nanoparticles through DC (Direct current) sputtering. This sensor shows high sensitivity of 2.77 and fast response time of 4 to 5 seconds at room temparature, but has a significant dependency on environmental factors such as temperature, and humidity. A machine learning technique, i.e. random forest, was incorporated to filter out the environmental factors. Experimental results show that the combination, i. e. MiCS-2714 sensor not only retains sensitivity, response time of the nan-ocomposite but also attains R² score of 0.994, MSE 0.0506, and the state classification accuracy of 0.979.

목차

Abstract
I. INTRODUCTION
II. MATERIALS AND METHODS
III. RESULTS
IV. CONCLUSIONS
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