A new approach for prediction of graphene based ISFET using regression tree and neural network. (June 2019)

• Record Type: Journal Article
• Authors: Akbari, Elnaz; Moradi, Rasoul; Afroozeh, Abdolkarim; Alizadeh, Azar; Nilashi, Mehrbakhsh
• Is Part Of: Superlattices and microstructures. Volume 130(2019)
• Page Start: 241
• Page End: 248
• Publication Date: 2019-06
• Subjects: Graphene -- ISFET -- I-V characteristic -- K+ -- Regression tree -- ANN; Superlattices as materials -- Periodicals; Microstructure -- Periodicals; Semiconductors -- Periodicals; Superréseaux -- Périodiques; Microstructure (Physique) -- Périodiques; Semiconducteurs -- Périodiques; 621.38152
• DOI: 10.1016/j.spmi.2019.04.011
• Languages: English
• ISSNs: 0749-6036

Abstract

Abstract: In this work, ion sensitive field effect transistor (ISFET) which is a device sensitive to the ions in a solution is employed. It is shown that under a fixed bias configuration, the voltage change causes a subsequent change in the surface potential of graphene thin film, which induces a detectable current change in the conducting channel between drain and source electrodes. Thus the transduction from an analog signal as an ion concentration(K+) changes to an electrical signal as current change can be achieved. For prediction purpose, the regression tree algorithm and artificial neural network (ANN) have been employed to predict the I-V characteristic, however ANN outperforms the regression tree approach and gives more accurate results. Highlights: The graphene ISFET in an electrolyte solution with different K+ concentration has been investigated. The regression tree algorithm and artificial neural network (ANN) have been employed to predict the I-V characteristic. ANN outperforms the regression tree approach and gives more accurate results.