Density gradient quantum corrections based performance optimization of triangular TG bulk FinFETs using ANN and GA

In this paper the electrical performance of triangular trigate bulk FinFET at 20 nm has been optimized using Artificial Neural Network (ANN) and Genetic Algorithm (GA). For training the ANN a set of 42 samples with two inputs and four outputs was created by 3D TCAD numerical simulator using Drift Diffusion approach with Density Gradient Quantum Corrections model. The optimal value of fin height (Hfin) and gate oxide thickness (Tox) was found using GA corresponding to which the short channel effects like drain induced barrier lowering (DIBL), subthreshold swing (SS) and off current (loFF) were minimum and on current (Ion) was maximum. The ANN and GA have been found to successfully predict and optimize the electrical performance of triangular TG FinFET for different device parameters like Hfin and Tox. After ANN and GA optimization Ion Hoff improved by 11.86 %, DIBL reduced by 32.35 % and off state leakage current reduced by 40.65% at expense of 33.41% reduction in the drive current.