In the last decades scaling of MOSFETs based on Moore's law faced a problem. Carbon-Nano-Tube (CNT) is a promising candidate for scaling the Field-Effect Transistors (FET) into nanometer. The gate insulator thickness and CNT length play vital role in the CNTFET performance. In this paper, a new method is proposed to improve the CNTFET performance. In this novel method, artificial intelligence algorithm is utilized to select the best gate insulator thickness and CNT length in the CNTFETs. The proposed method is simulated using MATLAB. The simulation results show that the performance of the proposed CNTFET model is considerably improved compared to other CNTFETs.
The use of carbon nano tubes has been increased in the last decades. This paper presents and evaluates a new method to achieve highperformance Carbon Nano Tube Field Effect Transistor (CNTFET) architecture. The proposed method utilizes genetic algorithm. Using Matlab and genetic algorithm, the best length and radios of CNTFET tubes are given. Our simulation results show that the proposed method provides an improvement in comparison with other methods in terms of performance.
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