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Modification of process parameters of the Fin shaped Field Effect Transistor (FinFET) is the field of research which has drawn attention after it has been inferred that these transistors diminish the leakage effects, which occur in planar transistors. The research paper presents a novel approach in which Artificial Neural Network (ANN) and Genetic Algorithm (GA) have been combined to optimize the structure of 14 nm Dual Gate Material Dual Gate Dielectric Material Heterojunction (DGMDGDM-Hetero) Silicon On Insulator (SOI) FinFET. The dataset mandatory for the training of ANN has been obtained through designing and simulating the DGMDGDM-Hetero SOI FinFET structure by varying the Si 1-x Ge x  height ( HSiGe ) and mole fraction ( MFSiGe ) in TCAD simulator. Through GA optimization, the optimal value of  HSiGe  and  MFSiGe  for which minimum Subthreshold Swing (SS), off-current ( Id,off ), Drain-Induced-Barrier-Lowering (DIBL) along with maximum on-current ( Id,on ) and  Id,on – Id,off  ratio achieved have been discovered. The considered FinFET structure was designed and simulated with optimal value of  HSiGe  and  MFSiGe  which resulted in ultimate best performance parameters. DIBL and leakage of 15.8 mV/V and 1.37 × 10 −17 A respectively, suggesting that DGMDGDM-Hetero SOI FinFET has more control over undesired Short Channel Effects (SCEs). Only 1.4% difference in the value of ANN-GA optimized and TCAD simulated performance parameters substantiate the effectiveness of optimization process.

Optimization of Dual-K Gate Dielectric and Dual Gate Heterojunction SOI FinFET at 14 nm Gate Length