Design of tunnel FET architectures for low power application using improved Chimp optimizer algorithm

Bhattacharya, Sabitabrata; Tripathi, Suman Lata; Kamboj, Vikram Kumar

doi:10.1007/s00366-021-01530-4

Cited by 12 publications

(3 citation statements)

References 135 publications

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“…A large number of existing studies have used benchmark functions f6, f7, and f8 in their research works to reduce the complexity time of performance evaluation. For example, [50][51][52][53][54] employed functions f6, f7, and f8 to evaluate the performance of their proposed models. These studies suggest that functions f5, f6, or f7 suffice for the evaluation and comparison of evolutionary computing algorithms [50,[54][55][56].…”

Section: Benchmark Functionsmentioning

confidence: 99%

“…While there are several fixed-dimension functions available in the literature, our algorithm mainly addresses multi-dimensional problems. Therefore, we have considered six standard benchmark multimodal functions, as suggested in [50][51][52][53]. The experimental results show that these functions can effectively assess performance and facilitate significance tests of the proposed algorithm in comparison to existing algorithms.…”

Section: Benchmark Functionsmentioning

confidence: 99%

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Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Ali,

Abbas,

Mahmood

et al. 2023

Mathematics

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Particle swarm optimization (PSO) is a population-based heuristic algorithm that is widely used for optimization problems. Phasor PSO (PPSO), an extension of PSO, uses the phase angle θ to create a more balanced PSO due to its increased ability to adjust the environment without parameters like the inertia weight w. The PPSO algorithm performs well for small-sized populations but needs improvements for large populations in the case of rapidly growing complex problems and dimensions. This study introduces a competitive coevolution process to enhance the capability of PPSO for global optimization problems. Competitive coevolution disintegrates the problem into multiple sub-problems, and these sub-swarms coevolve for a better solution. The best solution is selected and replaced with the current sub-swarm for the next competition. This process increases population diversity, reduces premature convergence, and increases the memory efficiency of PPSO. Simulation results using PPSO, fuzzy-dominance-based many-objective particle swarm optimization (FMPSO), and improved competitive multi-swarm PPSO (ICPPSO) are generated to assess the convergence power of the proposed algorithm. The experimental results show that ICPPSO achieves a dominating performance. The ICPPSO results for the average fitness show average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO. The Wilcoxon statistical significance test also confirms a significant difference in the performance of the ICPPSO, PPSO, and FMPSO algorithms at a 0.05 significance level.

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Section: Benchmark Functionsmentioning

confidence: 99%

Section: Benchmark Functionsmentioning

confidence: 99%

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Ali,

Abbas,

Mahmood

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…Metal-oxide-semiconductor field-effect transistors (MOS-FETs) have achieved great success in increasing the packing density and circuit speed to maintain Moore's law by employing 3D FinFET or nanosheet architectures. However, conventional MOSFETs are not suitable for ultra-low power applications since the scaling of supply voltage is hindered by the thermal kT/q limit of subthreshold swing [1]. With the key advantage of achieving steep on-off switching, TFETs have attracted much attention in the past two decades as potential * Authors to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Thin-body effects in double-gate tunnel field-effect transistors

Chien,

Thai,

Shih

2024

J. Phys. D: Appl. Phys.

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Scaling down the body thickness (Tb) of double-gate tunnel field-effect transistors (DG-TFETs) is helpful in suppressing short-channel effects (SCEs), but it may give rise to thin-body effects (TBEs). Based on 2-D device simulations, this study examines the mechanisms and influences of TBEs in DG-TFETs as Tb is scaled down. Differently from previous beliefs, the on-current degradation in thin-body DG-TFETs is not mainly caused by the volume effect, but rather by a newly defined TBE named lateralization effect. This is because the lateralization of tunneling direction significantly increases tunnel width, whereas the reduction of tunneling volume is quite limited due to narrow tunneling regions. To study the Tb-dependence of current, therefore, the vertical tunneling has to be taken into consideration. When considered as a TBE, the fringing field effect caused by reduction in Tb is not significant in degrading the on-current of thin-body DG-TFETs because the narrow tunneling regions are strongly gate-controlled. The only TBE that enhances the on-current is the coupling effect, but its role is only significant for low-bandgap bodies in which the coupling effect can efficiently promote the tunneling towards the body center. Not as previously thought that the quantum confinement effect (QCE) monotonically increased, it even decreases as Tb decreases down to sub-10 nm before turning to increase, thanks to the space sharing between proximate local quantum wells. A comprehensive understanding of the TBEs is useful for providing design insight, especially for determining the optimal Tb to maximize the on-current.

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Hybrid chimp optimization algorithm for degree reduction of ball Said–Ball curves

Dou

Wei

et al. 2023

Artif Intell Rev

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Design of tunnel FET architectures for low power application using improved Chimp optimizer algorithm

Cited by 12 publications

References 135 publications

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems

Thin-body effects in double-gate tunnel field-effect transistors

Hybrid chimp optimization algorithm for degree reduction of ball Said–Ball curves

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