Improving FinFET Device Parameters Through an Integrated Method of Particle Swarm and Whale Optimization Techniques

The geometrical parameters of the fin‐shaped field‐effect transistor (FinFET) significantly affect the outcomes of the FinFET‐based designs. Various machine learning (ML) schemes have been presented to optimize the geometrical parameters of the FinFET devices to limit the adverse effect of scaling. This paper presents the FinFET width and height optimization using the proposed hybrid particle swarm optimization algorithm‐based whale optimization algorithm (HPSO‐WOA). To improve device performance, the whale optimization algorithm (WOA) is utilized to find optimal geometric parameters of FinFET, such as width and height. Meanwhile, the PSO is used to optimize WOA hyperparameters. It uses various parameters such as on‐state current, transconductance, subthreshold swing (SS), early voltage ( V EA ), and transconductance generation factor (TGF) for analyzing the impact of fin width and height on the FinFET device. Additionally, we have analyzed the impact of the proposed parameters on the design of 6‐T static random access memory (SRAM). Microwind 3.9 EDA is used to design, simulate, and improve the physical layout utilizing the FinFET 14 nm and BSIM 4 MOS modeling technique. The proposed HPSO‐WOA improves 0.17% in SS, 0.62% in g m , 0.05% in TGF, and 70.86% in optimization time over the quasi‐random sequence (QRS)‐WOA. However, it resulted in overall improvement of 0.90% in SS, 0.74% in g m , 1.05% in TGF, and 173% in optimization time over traditional WOA.