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The DG MOSFET is one of the most promising candidates for further CMOS scaling beyond the year of 2010. It will be scaled down to various degrees upon a wide range of system/circuit requirements (such as high-performance, low standby power and low operating power). The key electrical parameter of the DG MOSFET is the subthreshold swing (S). In this paper, we present the applicability of the artificial neural network for the study of the scaling capability of the undoped DG MOSFET. The latter is based on the development of a semi-analytical model of the subthreshold swing (S) using the Finite Elements Method (FEM). Our results are discussed in order to draw some useful information about the ULSI technology.

A neural computation to study the scaling capability of the undoped DG MOSFET