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Carbon nano tubes (CNT) are the main parts of the electronic devices. Due to high carrier mobility, these devices have very high speed. CNT has a wide range of application in making sensors which can detect different types of diseases, materials, viruses and bacteria. The main problem in the CNT based field effect transistors (CNTFETs) is leakage current. For the reduction of leakage current, high k-gate dielectric are the most suitable materials. The purpose of this research is to find the most suitable high k-gate dielectric for the CNTFETs using Neutrosophic Hypersoft set TOPSIS (NHSSTOPSIS). Since NHSS-TOPSIS are useful when attributes are more than one and are further bifurcated. The concepts of TOPSIS has practical applications in computational intelligence, machine learning, image processing, neural networks, medical diagnosis, and decision analysis. A practical application for ranking of alternatives with newly developed NHSS-TOPSIS approach is illustrated by a numerical example for CNTFETs. The validity and superiority of NHSS-TOPSIS with existing approaches is also given with the help of a comparison analysis.