A Perspective View of Silicon Based Classical to Non-Classical MOS Transistors and their Extension in Machine Learning
Source abstract
Abstract
Unprecedented growth in CMOS technology and demand of high-density integrated circuits (ICs) in semiconductor industry has motivated to research community towards the development of MOS technology from micrometre regime to nano-meter regime. This paper presents the development of MOS technology from classical MOS technology to non-classical MOS technology and their extensions of learning towards the machine. Firstly, the studies are analysed with the scaling and limitations of bulk MOSFET beyond 100 nm in terms of constant and voltage field scaling and various short channel effects. Then we investigated the non-classical device architecture beyond 32 nm such as multiple-gate field-effect transistors, SOI MOSFETS, FINFETs, and gate-all-around FETs for the replacement of traditional CMOS devices. Next, we discussed the different architectures in the sub nano-technology regime. These structures include Carbon Nano Transistors, Single Electron Transistors (SET), Graphene Nano Ribbon, Nano Wire, and Nano Sheet Transistors. It is very conceivable that these kinds of devices will be utilized in the construction of high-density integrated electronic computers in near future. Lastly, we extend our studies on different machine learning method like ANN, KNN, DNN and Random Forest for the optimization of MOSFET, SOI FET, TFET, FinFET and GAA FET.
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Research context
OpenAlex topics: Advancements in Semiconductor Devices and Circuit Design · Semiconductor materials and devices · Nanowire Synthesis and Applications
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References (100+)
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