| Autor: |
Kaur, Gurpurneet, Gill, Sandeep Singh, Rattan, Munish |
| Zdroj: |
SILICON (1876990X); May2022, Vol. 14 Issue 7, p3371-3382, 12p |
| Abstrakt: |
Today, Fin shaped Field Effect Transistors (FinFETs) are the framework of the sub-nanometer technology node. The leading semiconductor industry deploys it in low-power (LP) and high-performance (HP) applications due to its better electrostatic control and exceptional scalability. In this paper, a novel optimized and miniaturized Xmas tree-shaped FinFET was designed that provides reduced short channel effects and better analog parameters as compared to the planar Metal Oxide Semiconductor Field Effect transistor. This proposed structure was devised with the Genetic Algorithm (GA) and Whale optimization Algorithm (WOA) along with the Artificial Neural Network (ANN). The dataset used in ANN training was created through designing and simulating the Xmas tree shaped FinFET structure by varying its Fin-width (WFin) and Fin-height (HFin). Through ANN-GA and ANN-WOA optimization, the optimal value of WFin and HFin was calculated at the minimum Subthreshold Swing (SS) and off-current (IOFF) along with maximum on-current (ION). The proposed Xmas tree shaped FinFET structure was designed and simulated with the optimal value of WFin and HFin that resulted in superior performance parameters. SS, IOFF, and ION values were 63.3 mV/dec, 77.3pA, and 0.51 μA respectively; suggesting that the optimized structure has more control over undesired Short Channel Effects. The deviation of less than 5% between optimized and simulated performance parameters substantiates the effectiveness of the optimization process. It has been estimated that the novel device consumes 40% lesser area than the rectzoidal structures designed in literature. A notable improvement has been observed in the area consumption due to the usage of multiple substrates. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink