Optimal design of junctionless double gate vertical MOSFET using hybrid Taguchi-GRA with ANN prediction

Random parameter variations have been an influential factor that deciding the performance of a metal-oxide-semiconductor field effect transistor (MOSFET), especially in nano-scale regime. Thus, controlling the variation of those parameters becomes extremely crucial in order to attain an acceptable performance of an ultra-small MOSFET. This paper proposes an approach to optimally design a n-type junctionless double-gate vertical MOSFET (n-JLDGVM) via hybrid Taguchi-grey relational analysis (GRA) with artificial neural networks (ANN) prediction. The device is designed using a combination of 2-D simulation tools (Silvaco) and hybrid Taguchi-GRA with a well-trained ANN prediction. The investigated device parameters consist of channel length (Lch), pillar thickness (Tp), channel doping (Nch) and source/drain doping (Nsd). The optimized design parameters of the device demonstrate a tolerable magnitude of on-state current (ION), off-state current (IOFF), on-off ratio, transconductance (gm), cut-off frequency (fT) and maximum oscillation frequency (fmax), measured at 2344.9 µA/µm, 2.53 pA/µm, 927 x 106, 4.78 mS/µm, 121.5 GHz and 2469 GHz respectively.