Publication: Optimization of 10nm Bi-GFET Device for higher ION/IOFF ratio using Taguchi Method
No Thumbnail Available
Date
2018
Authors
Roslan A.F.
Kaharudin K.E.
Salehuddin F.
Zain A.S.M.
Ahmad I.
Faizah Z.A.N.
Hazura H.
Hanim A.R.
Idris S.K.
Zaiton A.M.
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Physics Publishing
Abstract
The simulation and statistical modeling are conducted using Silvaco TCAD tools and L9 orthogonal array (OA) of Taguchi method respectively to design a proposed layout of 10 nm gate length (L g ) Bilayer Graphene Field Effect Transistor (Bi-GFET). The investigated process parameters are halo implant dose. halo implant energy, source/drain (S/D) implant dose and source/drain (S/D) implant energy, while the noise factors are halo implant tilt angle and source/drain (S/D) implant tilt angle. The process parameters and the noise factors are optimized using the L 9 orthogonal array (OA) of Taguchi method to achieve the highest possible I ON /I OFF ratio. Utilizing both signal-to-noise ratio (SNR) and analysis of variance (ANOVA), the most dominant process parameters upon I ON /I OFF ratio are identified as S/D implant energy and S/D implant dose with 56% and 37% factor effects on SNR respectively. The largest factor effects on SNR of S/D implant energy shows that it has dominantly affected the I ON /I OFF ratio. The final results indicate that the 1.99 � 10 13 atom/cm 3 of halo implant dose. 174 keV of halo implant energy, 1.63 � 10 14 atom/cm 3 of S/D implant dose, 17 keV of S/D implant energy, 24� of halo implant tilt angle and 9� of S/D implant tilt angle are the best parameter setting in obtaining the highest I on /I off ratio of the device which is measured at 4.811 � 10 5 . � Published under licence by IOP Publishing Ltd.
Description
Analysis of variance (ANOVA); Field effect transistors; Graphene transistors; Ion implantation; Ions; Taguchi methods; Bilayer Graphene; Dominant process; Halo implants; Implant energy; L9 orthogonal arrays; Parameter setting; Process parameters; Statistical modeling; Signal to noise ratio