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Support Vector Regression (SVR) Prediction for Molybdenum Disulfide Gas Sensor

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As a prototypical TMD, MoS2 is a two dimensional (2D) crystal, which has unique electronic properties and is widely explored for various application in nanoelectronic devices. The purpose of this research is to develop an analytical model for MoS2 sensor based on Field effect transistor (FET), assuming that the NO2 gas concentration and the gate voltage are proportional. We presented that support vector regression (SVR) algorithms can be used to predict the electronic property of MoS2. The relatively accurate results show the successful model construction.
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Keywords: FIELD EFFECT TRANSISTOR; MOS2; NANOSENSOR; NO2 GAS; SVR ALGORITHM

Document Type: Research Article

Publication date: November 1, 2018

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  • Journal of Nanoelectronics and Optoelectronics (JNO) is an international and cross-disciplinary peer reviewed journal to consolidate emerging experimental and theoretical research activities in the areas of nanoscale electronic and optoelectronic materials and devices into a single and unique reference source. JNO aims to facilitate the dissemination of interdisciplinary research results in the inter-related and converging fields of nanoelectronics and optoelectronics.
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