Silicon on Insulator C-VTFET Based Design of low Complexity Sparse Quadrature Mirror Filter Using Differential Search Algorithm

• Published in: SILICON 2022-11, Vol.14 (17), p.11545-11560
• Main Authors: Singh, Hitendra, Dwivedi, Atul Kumar, Nagaria, Deepak
• Format: Article
• Language: English
• Subjects: Algorithms; Chemistry; Chemistry and Materials Science; Circuit design; Complexity; Digital electronics; Electric potential; Environmental Chemistry; Evolutionary algorithms; Filter design (mathematics); Inorganic Chemistry; Ion currents; Lasers; Materials Science; Optical Devices; Optics; Optimization; Optimization techniques; Original Paper; Photonics; Polymer Sciences; Power management; Quadratures; Search algorithms; Search methods; Silicon dioxide; Staking; Voltage
• ISSN: 1876-990X; 1876-9918
• DOI: 10.1007/s12633-022-01858-6

In this paper, a 60 nm Complementary-Vertical TFET (C-VTFET) is designed using silicon on insulation technology is implemented for low power quadrature mirror filter design for different search algorithm using silvaco TCAD simulation. Various advantages of SOI have been incorporated for the realization of low-voltage with low power (LVLP) for VLSI design digital circuits. To prevent the losses of the Lattice mismatch structure, gate staking of high k -dielectric (HfO 2 ) material with SiO 2 was employed using equivalent oxide thickness method. Various engineering method incorporated to optimized the Drain-Voltage characteristics. N-Type and P-Type VTFET is considered using the mixed mode technique. The SiGe layer is employed to enhance the tunneling method of by reducing the bandgap from 1.1 eV to 0.7 eV. The highest I ON current and minimum I OFF current is reported for the proposed device is I (3.62 × 10 −4 A/μm) and (1.58 × 10 −18 A/μm) respectively. The ON/OFF current ratio out recorded as ~10 13 respectively. The reconstruction quadrature mirror filter architecture in this manuscript is computationally efficient and virtually flawless. Sparsity among the coefficients is introduced to reduce the number of multipliers and adders required to construct prototype the filter H 0 (Z), resulting in a reduction in computational complexity. A well-known population-based evolutionary optimization differential search algorithm is used to optimized the objective function. However, Levy’s differential search method is also advocated due to sluggish exploitation of traditional differential search algorithm. The suggested algorithm’s effectiveness is evaluated by comparing it to other evolutionary optimization techniques. The results demonstrate that the proposed technique outperforms previously published evolutionary optimization algorithms. The above analysis validated the proposed device are well incorporated for the execution to design low power filter design.