Optimization of Electrical Properties in TiO2/WSix-based Vertical DG-MOSFET using Taguchi-based GRA with ANN

Abstract

This study describes a proposed method to determine the most optimal level of process parameters, considering multiple electrical properties of titanium dioxide/tungsten silicide (TiO2/WSix)-based vertical double-gate MOSFET. The proposed method utilizes a combination of the L9 orthogonal array (OA) of Taguchi-based grey relational analysis (GRA) and the artificial neural network (ANN). The VTH implant energy, halo implant dose, source/drain (S/D) implant dose and S/D implant tilt angle are the selected processs parameters to be optimized for the optimal value of on-current (ION), off-current (IOFF) and subthreshold slope (SS). The design of experiment (DoE) is based on the L9 OA of Taguchi method and the experimental value for multiple electrical properties are converted into a grey relational grade (GRG). The well-trained ANN based on the Levenberg-Marquardt algorithm is developed to predict the best optimization results. The most optimal level of four process parameters towards ION, IOFF and SS are selected based on the highest GRG predicted by welltrained ANN. The most optimal value for ION, IOFF and SS after the optimization are observed to be 1612.1 µA/µm, 8.801E-10 A/µm and 67.74 mV/dec respectively with 0.7417 of predicted GRG.