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Engineering with Computers

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12.11.2021 | Original Article

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

verfasst von: Sabitabrata Bhattacharya, Suman Lata Tripathi, Vikram Kumar Kamboj

Erschienen in: Engineering with Computers | Ausgabe 2/2023

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Abstract

An improved Chimps optimizer algorithm is proposed in this paper and is applied for the performance optimization of tunnel FET architectures for use in low power VLSI circuits. The steep subthreshold characteristics of TFET improves device performance and make it suitable for low power digital and memory applications. Classical Chimps optimizer has poor convergence and problem to stuck into local minima for high dimensional problems. This research focuses on mathematical model of divergent thinking and sensual movement of chimps in four different forms named attacker, barrier, chaser, and driver for simulation. The improved variant of Chimps optimizer has been proposed in this research and named as Imp-Chimp. To validate the efficacy and feasibility of the suggested technique, it has been examined for standard benchmarks and multidisciplinary engineering design problems to solve non-convex, non-linear, and typical engineering design problems. The suggested technique variants have been evaluated for seven standard unimodal benchmark functions, six standard multi modal benchmark functions, ten standard fixed dimension benchmark functions and engineering design problems (i. e., TFET, BTBT). The outcomes of this method have been compared with other existing optimization methods considering convergence speed as well as for searching local and global optimal solutions. The testing results show the better performance of the proposed method. The paper also demonstrates the tunnel field effect transistor (TFET) as a promising device for low power electronic circuits and an engineering problem where the Imp-Chimp optimizer can be implemented for performance improvement. The TFET is based on the carrier generation using the quantum mechanical process of the band-to-band tunneling (BTBT). TFET can meet the requirements of a device that can perform on low supply voltage with reduced leakage currents and low sub-threshold swing. TFET can be optimized to give similar performance as MOSFET, but with much lower power consumption.

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GOSC TFET

Gate on source channel tunnel field effect transistor [120]

SiGe-S-NW-TFET

SiGe source nano wire tunnel field effect transistor [121]

HGD DE DMG DL TFET

Hetero gate dielectric drain engineered dual metal gate tunnel field effect transistor [122]

HGD DW TFET

Hetero gate dielectric dual gate-metal work function tunnel field effect transistor [124]

GDO– HD–GAA-TFET

Gate drain overlapped hetero gate dielectric gate all around tunnel field effect transistor [123]

U HJ VTFET

U-shaped gate hetero junction vertical tunnel field effect transistor [126]

DMCG-CPTFET

Dual metal control gate charge plasma tunnel field effect transistor [130]

V-DMTFET

Vertical dielectric modulated tunnel field effect transistor [140]

HM-GUL-ED-TFET

Hetero material gate underlapped electrically doped tunnel field effect transistor [131]

D GAA CS NT TFET

Dual gate all around core shell nano tube tunnel field effect transistor [129]

JL-TFET

Junction less tunnel field effect transistor [132]

SD-SG TFET

Splitted drain single gate tunnel field effect transistor [135]

VS-TFET

Vertical sandwiched channel tunnel field effect transistor [127]

DE-QG-TFET

Drain engineered quadruple gate tunnel field effect transistor [128]

JLSGTFET

Junction less single gate tunnel field effect transistor [133]

GSHJ-PGP-STFET

Ge source hetero junction partial ground plane base SELBOX (selective buried oxide) tunnel field effect transistor [139]

Abbassi R, Abbassi A, Heidari AA, Mirjalili S (2019) An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models. Energy Convers Manag 179(January):362–372. https://doi.org/10.1016/j.enconman.2018.10.069CrossRef

Faris H et al (2019) An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf Fusion 48(August):67–83. https://doi.org/10.1016/j.inffus.2018.08.002CrossRef

(2000) Rapid communications. JAIDS J Acquir Immune Defic Syndr 23(5):374. https://doi.org/10.1097/00126334-200004150-00002

Wu G (2016) Across neighborhood search for numerical optimization. Inf Sci (Ny) 329(61563016):597–618. https://doi.org/10.1016/j.ins.2015.09.051CrossRefMATH

Wu G, Pedrycz W, Suganthan PN, Mallipeddi R (2015) A variable reduction strategy for evolutionary algorithms handling equality constraints. Appl Soft Comput J 37:774–786. https://doi.org/10.1016/j.asoc.2015.09.007CrossRef

Khishe M, Mosavi MR (2020) Chimp optimization algorithm. Expert Syst Appl 149:113338. https://doi.org/10.1016/j.eswa.2020.113338CrossRef

Bhattacharya A, Chattopadhyay PK (2010) Solving complex economic load dispatch problems using biogeography-based optimization. Expert Syst Appl 37(5):3605–3615. https://doi.org/10.1016/j.eswa.2009.10.031CrossRef

Hemamalini S, Simon SP (2011) Dynamic economic dispatch using artificial immune system for units with valve-point effect. Int J Electr Power Energy Syst 33(4):868–874. https://doi.org/10.1016/j.ijepes.2010.12.017CrossRef

Noman N, Iba H (2008) Differential evolution for economic load dispatch problems. Electr Power Syst Res 78(8):1322–1331. https://doi.org/10.1016/j.epsr.2007.11.007CrossRef

10.

Yalcinoz T, Altun H, Uzam M (2001) Economic dispatch solution using a genetic algorithm based on arithmetic crossover. In: 2001 IEEE Porto Power Tech Proc., vol 2, no 4, pp 153–156. https://doi.org/10.1109/PTC.2001.964734

11.

Nguyen TT, Vo DN (2015) The application of one rank cuckoo search algorithm for solving economic load dispatch problems. Appl Soft Comput J 37:763–773. https://doi.org/10.1016/j.asoc.2015.09.010CrossRef

12.

Swain RK, Sahu NC, Hota PK (2012) Gravitational search algorithm for optimal economic dispatch. Proc Technol 6:411–419. https://doi.org/10.1016/j.protcy.2012.10.049CrossRef

13.

Abdelaziz AY, Ali ES, Abd Elazim SM (2016) Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems. Energy 101:506–518. https://doi.org/10.1016/j.energy.2016.02.041CrossRef

14.

Bhattacharjee K, Bhattacharya A, Dey SHN (2014) Chemical reaction optimisation for different economic dispatch problems. IET Gener Transm Distrib 8(3):530–541. https://doi.org/10.1049/iet-gtd.2013.0122CrossRef

15.

Yang X, Soheil S, Hosseini S, Hossein A (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput J 12(3):1180–1186. https://doi.org/10.1016/j.asoc.2011.09.017CrossRef

16.

Aragón VS, Esquivel SC, Coello Coello CA (2015) An immune algorithm with power redistribution for solving economic dispatch problems. Inf Sci (Ny) 295:609–632. https://doi.org/10.1016/J.INS.2014.10.026MathSciNetCrossRef

17.

Banerjee S, Maity D, Chanda CK (2015) Teaching learning based optimization for economic load dispatch problem considering valve point loading effect. Int J Electr Power Energy Syst 73:456–464. https://doi.org/10.1016/J.IJEPES.2015.05.036CrossRef

18.

Victoire TAA, Jeyakumar AE (2004) Hybrid PSO–SQP for economic dispatch with valve-point effect. Electr Power Syst Res 71(1):51–59. https://doi.org/10.1016/J.EPSR.2003.12.017CrossRef

19.

Pradhan M, Roy PK, Pal T (2018) Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system. Ain Shams Eng J 9(4):2015–2025. https://doi.org/10.1016/j.asej.2016.08.023CrossRef

20.

Yu JJQ, Li VOK (2016) A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing 171:955–965. https://doi.org/10.1016/j.neucom.2015.07.037CrossRef

21.

Kavousi-Fard A, Khosravi A (2016) An intelligent θ-modified bat algorithm to solve the non-convex economic dispatch problem considering practical constraints. Int J Electr Power Energy Syst 82:189–196. https://doi.org/10.1016/j.ijepes.2016.03.017CrossRef

22.

Whitley D (2001) An overview of evolutionary algorithms: practical issues and common pitfalls. Inf Softw Technol 43(14):817–831. https://doi.org/10.1016/S0950-5849(01)00188-4CrossRef

23.

Calvet L, De Armas J, Masip D, Juan AA (2017) Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs. Open Math 15(1):261–280. https://doi.org/10.1515/math-2017-0029MathSciNetCrossRefMATH

24.

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028CrossRef

25.

Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci (Ny) 183(1):1–15. https://doi.org/10.1016/j.ins.2011.08.006MathSciNetCrossRef

26.

Hansen P, Mladenović N, Moreno-Pérez JA (2010) Variable neighbourhood search: methods and applications. Ann Oper Res 175(1):367–407. https://doi.org/10.1007/s10479-009-0657-6MathSciNetCrossRefMATH

27.

Doʇan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: Vortex Search algorithm. Inf Sci (Ny) 293(August):125–145. https://doi.org/10.1016/j.ins.2014.08.053CrossRef

28.

Takeang C, Aurasopon A (2019) Multiple of hybrid lambda iteration and simulated annealing algorithm to solve economic dispatch problem with ramp rate limit and prohibited operating zones. J Electr Eng Technol 14(1):111–120. https://doi.org/10.1007/s42835-018-00001-zCrossRef

29.

Naama B, Bouzeboudja H, Allali A (2013) Solving the economic dispatch problem by using Tabu Search algorithm. Energy Procedia 36:694–701. https://doi.org/10.1016/j.egypro.2013.07.080CrossRef

30.

Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102CrossRef

31.

Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. SIMULATION 76(2):60–68. https://doi.org/10.1177/003754970107600201CrossRef

32.

Ghaemi M, Feizi-Derakhshi MR (2014) Forest optimization algorithm 41(15):6676-6687

33.

Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRef

34.

Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249. https://doi.org/10.1016/j.knosys.2015.07.006CrossRef

35.

Salimi H (2015) Stochastic fractal search: a powerful metaheuristic algorithm. Knowl-Based Syst 75:1–18. https://doi.org/10.1016/j.knosys.2014.07.025CrossRef

36.

Nematollahi AF, Rahiminejad A, Vahidi B (2017) A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Appl Soft Comput J 59:596–621. https://doi.org/10.1016/j.asoc.2017.06.033CrossRef

37.

Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. https://doi.org/10.1023/A:1008202821328MathSciNetCrossRefMATH

38.

C. Verma, Z. Illés, V. Stoffová and P. K. Singh Predicting Attitude of Indian Student’s Towards ICT and Mobile Technology for Real-Time: Preliminary Results IEEE Access, 8:178022-178033. doi: 10.1109/ACCESS.2020.3026934.

39.

Biswas A, Mishra KK, Tiwari S, Misra AK (2013) Physics-inspired optimization algorithms: a survey. J Optim 2013:1–16. https://doi.org/10.1155/2013/438152CrossRef

40.

Liu Y, Li R (2020) PSA: a photon search algorithm. J Inf Process Syst 16(2):478–493

41.

Erol OK, Eksin I (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005CrossRef

42.

Mosavi MR, Khishe M, Naseri MJ, Parvizi GR, Ayat M (2019) Multi-layer perceptron neural network utilizing adaptive best-mass gravitational search algorithm to classify sonar dataset. Arch Acoust 44(1):137–151. https://doi.org/10.24425/aoa.2019.126360CrossRef

43.

C. Verma, V. Stoffová, Z. Illés, S. Tanwar and N. Kumar (2020) Machine Learning-Based Student’s Native Place Identification for Real-Time IEEE Access, 8:130840-130854. doi: 10.1109/ACCESS.2020.3008830.

44.

Kumar M, Kulkarni AJ, Satapathy SC (2018) Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology. Futur Gener Comput Syst 81:252–272. https://doi.org/10.1016/j.future.2017.10.052CrossRef

45.

Ruiz-vanoye JA, Díaz-parra O, Cocón F, Soto A (2012) Meta-heuristics algorithms based on the grouping of animals by social behavior for the traveling salesman problem. Int J Comb Optim Probl Inform 3(3):104–123

46.

Khishe M, Mohammadi H (2019) Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm. Ocean Eng 181:98–108. https://doi.org/10.1016/j.oceaneng.2019.04.013CrossRef

47.

Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. https://doi.org/10.1016/j.cnsns.2012.05.010MathSciNetCrossRefMATH

48.

Mafarja MM, Mirjalili S (2019) Hybrid binary ant lion optimizer with rough set and approximate entropy reducts for feature selection. Soft Comput 23(15):6249–6265. https://doi.org/10.1007/s00500-018-3282-yCrossRef

49.

Khishe M, Safari A (2019) Classification of sonar targets using an mlp neural network trained by dragonfly algorithm. Wirel Pers Commun 108(4):2241–2260. https://doi.org/10.1007/s11277-019-06520-wCrossRef

50.

Khishe M, Mosavi MR (2019) Improved whale trainer for sonar datasets classification using neural network. Appl Acoust 154:176–192. https://doi.org/10.1016/j.apacoust.2019.05.006CrossRef

51.

Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2019.02.028CrossRef

52.

Hashim FA, Houssein EH, Mabrouk MS, Al-atabany W (2019) Henry gas solubility optimization: a novel physics-based algorithm. Futur Gener Comput Syst 101:646–667. https://doi.org/10.1016/j.future.2019.07.015CrossRef

53.

Wang GG, Guo L, Gandomi AH, Hao GS, Wang H (2014) Chaotic Krill herd algorithm. Inf Sci (Ny) 274(January):17–34. https://doi.org/10.1016/j.ins.2014.02.123MathSciNetCrossRef

54.

Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theor Artif Intell 28(4):673–687. https://doi.org/10.1080/0952813X.2015.1042530CrossRef

55.

Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput J 36:315–333. https://doi.org/10.1016/j.asoc.2015.07.028CrossRef

56.

Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513. https://doi.org/10.1007/s00521-015-1870-7CrossRef

57.

Li MD, Zhao H, Weng XW, Han T (2016) A novel nature-inspired algorithm for optimization: Virus colony search. Adv Eng Softw 92:65–88. https://doi.org/10.1016/j.advengsoft.2015.11.004CrossRef

58.

Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47. https://doi.org/10.1016/j.advengsoft.2017.01.004CrossRef

59.

Deb S, Gao XZ, Tammi K, Kalita K, Mahanta P (2020) Recent studies on chicken swarm optimization algorithm: a review (2014–2018). Artif Intell Rev 53(3):1737–1765. https://doi.org/10.1007/s10462-019-09718-3CrossRef

60.

Singh N, Singh SB (2017) A novel hybrid GWO-SCA approach for optimization problems. Eng Sci Technol Int J 20(6):1586–1601. https://doi.org/10.1016/j.jestch.2017.11.001CrossRef

61.

Huang KW, Wu ZX (2018) CPO: a crow particle optimization algorithm. Int J Comput Intell Syst 12(1):426–435. https://doi.org/10.2991/ijcis.2018.125905658CrossRef

62.

Aala-Kalananda VKR, Komanapalli VLN (2021) A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems. Appl Soft Comput 99:106903. https://doi.org/10.1016/j.asoc.2020.106903CrossRef

63.

Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014CrossRef

64.

Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl Based Syst 150(March):175–197. https://doi.org/10.1016/j.knosys.2018.03.011CrossRef

65.

Hu K, Jiang H, Ji CG, Pan Z (2020) A modified butterfly optimization algorithm: an adaptive algorithm for global optimization and the support vector machine. Expert Syst. https://doi.org/10.1111/exsy.12642CrossRef

66.

Kumar V, Kaur A (2020) Binary spotted hyena optimizer and its application to feature selection. J Ambient Intell Humaniz Comput 11(7):2625–2645. https://doi.org/10.1007/s12652-019-01324-zCrossRef

67.

Krishna AB, Saxena S, Kamboj VK (2021) A novel statistical approach to numerical and multidisciplinary design optimization problems using pattern search inspired Harris Hawks optimizer. Springer, LondonCrossRef

68.

Kamboj VK, Nandi A, Bhadoria A, Sehgal S (2020) An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput J 89:106018. https://doi.org/10.1016/j.asoc.2019.106018CrossRef

69.

Zamani H, Nadimi-shahraki MH (2020) Enhancement of Bernstain-search differential evolution algorithm to solve constrained engineering problems. Int J Comput Sci Eng 9(6):386–396

70.

Meng Z, Li G, Wang X, Sait SM, Yıldız AR (2020) A comparative study of metaheuristic algorithms for reliability-based design optimization problems. Arch Comput Methods Eng. https://doi.org/10.1007/s11831-020-09443-zCrossRef

71.

Xu J, Zhang J (2014) Exploration-exploitation tradeoffs in metaheuristics: survey and analysis. In: Proc.33rd Chinese control conf. CCC 2014, pp 8633–8638. https://doi.org/10.1109/ChiCC.2014.6896450

72.

Yang XS, Deb S, Fong S (2014) Metaheuristic algorithms: optimal balance of intensification and diversification. Appl Math Inf Sci 8(3):977–983. https://doi.org/10.12785/amis/080306CrossRef

73.

Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9(3):727–745. https://doi.org/10.1007/s11047-009-9175-3MathSciNetCrossRefMATH

74.

Emary E, Zawbaa HM, Hassanien AE (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381. https://doi.org/10.1016/j.neucom.2015.06.083CrossRef

75.

Nakamura RYM, Pereira LAM, Costa KA, Rodrigues D, Papa JP, Yang XS (2012) BBA: a binary bat algorithm for feature selection. In: Brazilian symp. comput. graph. image process, pp 291–297. https://doi.org/10.1109/SIBGRAPI.2012.47

76.

Nakamura RYM, Pereira LAM, Rodrigues D, Costa KAP, Papa JP, Yang XS (2013) Binary bat algorithm for feature selection. Swarm Intell Bio-Inspir Comput 2010:225–237. https://doi.org/10.1016/B978-0-12-405163-8.00009-0CrossRef

77.

Kennedy J, Eberhart RC (1997) Discrete binary version of the particle swarm algorithm. In: Proc. IEEE int. conf. syst. man cybern., vol 5, pp 4104–4108. https://doi.org/10.1109/icsmc.1997.637339

78.

Dhiman G, Kaur A (2019) A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization, vol 816, no. January. Springer, Singapore

79.

Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82. https://doi.org/10.1109/4235.585893CrossRef

80.

Mirjalili S, Wang GG, Coelho LS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435. https://doi.org/10.1007/s00521-014-1629-6CrossRef

81.

Rashedi E, Nezamabadi-pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci (Ny) 179(13):2232–2248. https://doi.org/10.1016/j.ins.2009.03.004CrossRefMATH

82.

Glover F (1989) Tabu search—part I. Orsa J Comput 1(3):190–206CrossRefMATH

83.

Digalakis JG, Margaritis KG (2001) On benchmarking functions for genetic algorithms. Int J Comput Math 77(4):481–506. https://doi.org/10.1080/00207160108805080MathSciNetCrossRefMATH

84.

Rahkar Farshi T (2020) Battle Royale optimization algorithm. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05004-4CrossRef

85.

Hans R, Kaur H (2020) Opposition-based enhanced grey wolf optimization algorithm for feature selection in breast density classification. Int J Mach Learn Comput 10(3):458–464. https://doi.org/10.18178/ijmlc.2020.10.3.957CrossRef

86.

Bhullar AK, Kaur R, Sondhi S (2020) Enhanced crow search algorithm for AVR optimization, vol 24, no 16. Springer, Berlin

87.

Qais MH, Hasanien HM, Alghuwainem S (2020) Transient search optimization: a new meta-heuristic optimization algorithm. Appl Intell 50(11):3926–3941. https://doi.org/10.1007/s10489-020-01727-yCrossRef

88.

Mamidala JK, Vishnoi R, Pandey P (2016) Tunnel field-effect transistors (TFET): Modelling and Simulation. Wiley, ChichesterCrossRef

89.

Boucart K, Ionescu AM (2007) Double-gate tunnel FET with high-κ gate dielectric. IEEE Trans Electron Devices 54(7):1725–1733. https://doi.org/10.1109/TED.2007.899389CrossRef

90.

Wang PF et al (2004) Complementary tunneling transistor for low power application. Solid State Electron 48(12):2281–2286. https://doi.org/10.1016/j.sse.2004.04.006CrossRef

91.

Choi WY, Park BG, Lee JD, Liu TJK (2007) Tunneling field-effect transistors (TFETs) with subthreshold swing (SS) less than 60 mV/dec. IEEE Electron Device Lett 28(8):743–745. https://doi.org/10.1109/LED.2007.901273CrossRef

92.

Koswatta SO, Lundstrom MS, Nikonov DE (2009) Performance comparison between p-i-n tunneling transistors and conventional MOSFETs. IEEE Trans Electron Devices 56(3):456–465. https://doi.org/10.1109/TED.2008.2011934CrossRef

93.

Avci UE, Rios R, Kuhn K, Young IA (2011) Comparison of performance, switching energy and process variations for the TFET and MOSFET in logic. In: Dig.Tech.Pap.—Symp.VLSI Technol., no. 2009, pp 124–125

94.

Trivedi AR, Datta S, Mukhopadhyay S (2014) Application of silicon-germanium source tunnel-FET to enable ultralow power cellular neural network-based associative memory. IEEE Trans Electron Devices 61(11):3707–3715. https://doi.org/10.1109/TED.2014.2357777CrossRef

95.

Trivedi AR, Amir MF, Mukhopadhyay S (2014) Ultra-low power electronics with Si/Ge tunnel FET. In: Design, automation & test in Europe conference & exhibition (DATE), pp1–6. https://doi.org/10.7873/DATE.2014.244

96.

Saripalli V, Datta S, Narayanan V, Kulkarni JP (2011) Variation-tolerant ultra low-power heterojunction tunnel FET SRAM design. In: Proc.2011 IEEE/ACM Int. symp. nanoscale archit. NANOARCH 2011, vol 1, pp 45–52. https://doi.org/10.1109/NANOARCH.2011.5941482

97.

Gupta N, Makosiej A, Vladimirescu A, Amara A, Anghel C (2016) 3T-TFET bitcell based TFET-CMOS hybrid SRAM design for ultra-low power applications. In: Proc. 2016 des. autom. test eur. conf. exhib., pp 361–366. https://doi.org/10.3850/9783981537079_0462

98.

Ahmad S, Ahmad SA, Muqeem M, Alam N, Hasan M (2019) TFET-based robust 7T SRAM cell for low power application. IEEE Trans Electron Devices 66(9):3834–3840. https://doi.org/10.1109/TED.2019.2931567CrossRef

99.

Trivedi AR, Carlo S, Mukhopadhyay S (2013) Exploring tunnel-FET for ultra low power analog applications. In: Proceedings of the 50th annual design automation conference on—DAC ’13, no. V, p 1. https://doi.org/10.1145/2463209.2488868

100.

Ahmad S, Alam N, Hasan M (2018) Robust TFET SRAM cell for ultra-low power IoT applications. AEU Int J Electron Commun 89:70–76. https://doi.org/10.1016/j.aeue.2018.03.029CrossRef

101.

Bhuwalka KK, Schulze J, Eisele I (2005) Scaling the vertical tunnel FET with tunnel bandgap modulation and gate workfunction engineering. IEEE Trans Electron Devices 52(5):909–917. https://doi.org/10.1109/TED.2005.846318CrossRef

102.

Bhuwalka KK, Schulze J, Eisele I (2005) A simulation approach to optimize the electrical parameters of a vertical tunnel FET. IEEE Trans Electron Devices 52(7):1541–1547. https://doi.org/10.1109/TED.2005.850618CrossRef

103.

Boucart K, Ionescu AM (2007) Length scaling of the double gate tunnel FET with a high-K gate dielectric. Solid State Electron 51(11–12):1500–1507. https://doi.org/10.1016/j.sse.2007.09.014CrossRef

104.

Narang R, Saxena M, Gupta M (2019) Exploring the applicability of well optimized dielectric pocket tunnel transistor for future low power applications. Superlattices Microstruct 126:8–16. https://doi.org/10.1016/j.spmi.2018.12.005CrossRef

105.

Mayer F et al (2008) Impact of SOI, Si1-xGexOI and GeOI substrates on CMOS compatible tunnel FET performance. In: Tech. Dig.—Int. Electron Devices Meet. IEDM, vol 4, pp 9–13. https://doi.org/10.1109/IEDM.2008.4796641

106.

Arun Samuel TS, Balamurugan NB, Bhuvaneswari S, Sharmila D, Padmapriya K (2014) Analytical modelling and simulation of single-gate SOI TFET for low-power applications. Int J Electron 101(6):779–788. https://doi.org/10.1080/00207217.2013.796544CrossRef

107.

Singh AK, Tripathy MR, Baral K, Singh PK, Jit S (2020) Ferroelectric gate heterojunction TFET on selective buried oxide (SELBOX) substrate for distortionless and low power applications. In: 2020 4th IEEE electron devices technology & manufacturing conference (EDTM), pp 1–4. https://doi.org/10.1109/EDTM47692.2020.9117858

108.

Singh AK, Tripathy MR, Singh PK, Baral K, Chander S, Jit S (2020) Deep insight into DC/RF and linearity parameters of a novel back gated ferroelectric TFET on SELBOX substrate for ultra low power applications. SILICON. https://doi.org/10.1007/s12633-020-00672-2CrossRef

109.

Choi WY, Lee W (2010) Hetero-gate-dielectric tunneling field-effect transistors. IEEE Trans Electron Devices 57(9):2317–2319. https://doi.org/10.1109/TED.2010.2052167CrossRef

110.

Asra R, Shrivastava M, Murali KVRM, Pandey RK, Gossner H, Rao VR (2011) A tunnel FET for V_DD scaling below 0.6 V with a CMOS-comparable performance. IEEE Trans Electron Devices 58(7):1855–1863. https://doi.org/10.1109/TED.2011.2140322CrossRef

111.

Paras N, Chauhan SS (2019) Insights into the DC, RF/analog and linearity performance of vertical tunneling based TFET for low-power applications. Microelectron Eng 216:111043. https://doi.org/10.1016/j.mee.2019.111043CrossRef

112.

Tripathy MR et al (2020) Device and circuit-level assessment of GaSb/Si heterojunction vertical tunnel-FET for low-power applications. IEEE Trans Electron Devices 67(3):1285–1292. https://doi.org/10.1109/TED.2020.2964428CrossRef

113.

Dutta R, Sarkar SK (2019) Analytical modeling and simulation-based optimization of broken gate TFET structure for low power applications. IEEE Trans Electron Devices 66(8):3513–3520. https://doi.org/10.1109/TED.2019.2925109CrossRef

114.

Beohar A, Yadav N, Shah AP, Vishvakarma SK (2018) Analog/RF characteristics of a 3D-Cyl underlap GAA-TFET based on a Ge source using fringing-field engineering for low-power applications. J Comput Electron 17(4):1650–1657. https://doi.org/10.1007/s10825-018-1222-9CrossRef

115.

Mookerjea S, Mohata D, Mayer T, Narayanan V, Datta S (2010) Temperature-dependent I–V characteristics of a vertical In 0.53Ga0.47 tunnel FET. IEEE Electron Device Lett 31(6):564–566. https://doi.org/10.1109/LED.2010.2045631CrossRef

116.

Avci UE, Morris DH, Young IA (2015) Tunnel field-effect transistors: prospects and challenges. IEEE J Electron Devices Soc 3(3):88–95. https://doi.org/10.1109/JEDS.2015.2390591CrossRef

117.

Dewey G, Chu-Kung B, Kotlyar R, Metz M, Mukherjee N, Radosavljevic M (2012) III–V field effect transistors for future ultra-low power applications. In: 2012 symposium on VLSI Technology (VLSIT), vol 14, no 2011, pp 45–46. https://doi.org/10.1109/VLSIT.2012.6242453

118.

Fiori G, Iannaccone G (2009) Ultralow-voltage bilayer graphene tunnel FET. IEEE Electron Device Lett 30(10):1096–1098. https://doi.org/10.1109/LED.2009.2028248CrossRef

119.

Wang W et al (2016) Investigation of light doping and hetero gate dielectric carbon nanotube tunneling field-effect transistor for improved device and circuit-level performance. Semicond Sci Technol 31(3):035002. https://doi.org/10.1088/0268-1242/31/3/035002CrossRef

120.

Mitra SK, Bhowmick B (2019) Impact of interface traps on performance of gate-on-source/channel SOI TFET. Microelectron Reliab 94:1–12. https://doi.org/10.1016/j.microrel.2019.01.004CrossRef

121.

Patel J, Sharma D, Yadav S, Lemtur A, Suman P (2019) Performance improvement of nano wire TFET by hetero-dielectric and hetero-material: at device and circuit level. Microelectronics J 85(February):72–82. https://doi.org/10.1016/j.mejo.2019.02.004CrossRef

122.

Yadav DS, Verma A, Sharma D, Tirkey S, Raad BR (2017) Comparative investigation of novel hetero gate dielectric and drain engineered charge plasma TFET for improved DC and RF performance. Superlattices Microstruct 111:123–133. https://doi.org/10.1016/j.spmi.2017.06.016CrossRef

123.

Madan J, Chaujar R (2016) Gate drain-overlapped-asymmetric gate dielectric-GAA-TFET: a solution for suppressed ambipolarity and enhanced ON state behavior. Appl Phys A. https://doi.org/10.1007/s00339-016-0510-0CrossRef

124.

Raad B, Nigam K, Sharma D, Kondekar P (2016) Dielectric and work function engineered TFET for ambipolar suppression and RF performance enhancement. Electron Lett 52(9):770–772. https://doi.org/10.1049/el.2015.4348CrossRef

125.

Bagga N, Dasgupta S (2017) Surface potential and drain current analytical model of gate all around triple metal TFET. IEEE Trans Electron Devices 64(2):606–613. https://doi.org/10.1109/TED.2016.2642165CrossRef

126.

Shih PC, Hou WC, Li JY (2017) A U-gate InGaAs/GaAsSb heterojunction TFET of tunneling normal to the gate with separate control over ON- and OFF-state current. IEEE Electron Device Lett 38(12):1751–1754. https://doi.org/10.1109/LED.2017.2759303CrossRef

127.

Kim JH, Kim S, Park B (2019) Double-gate TFET with vertical channel sandwiched by lightly doped Si. IEEE Trans Electron Devices 66(4):1656–1661. https://doi.org/10.1109/TED.2019.2899206CrossRef

128.

Uddin Shaikh MR, Loan SA (2019) Drain-engineered TFET with fully suppressed ambipolarity for high-frequency application. IEEE Trans Electron Devices 66(4):1628–1634. https://doi.org/10.1109/TED.2019.2896674CrossRef

129.

Kumar N, Mushtaq U, Amin SI, Anand S (2019) Design and performance analysis of dual-gate all around core-shell nanotube TFET. Superlattices Microstruct 125:356–364. https://doi.org/10.1016/j.spmi.2018.09.012CrossRef

130.

Nigam K, Pandey S, Kondekar PN, Sharma D, Kumar Parte P (2017) A barrier controlled charge plasma-based TFET with gate engineering for ambipolar suppression and rf/linearity performance improvement. IEEE Trans Electron Devices 64(6):2751–2757. https://doi.org/10.1109/TED.2017.2693679CrossRef

131.

Yadav S, Sharma D, Chandan BV, Aslam M, Soni D, Sharma N (2018) A novel hetero-material gate-underlap electrically doped TFET for improving DC/RF and ambipolar behaviour. Superlattices Microstruct 117:9–17. https://doi.org/10.1016/j.spmi.2018.02.005CrossRef

132.

Devi WV, Bhowmick B (2019) Optimisation of pocket doped junctionless TFET and its application in digital inverter. Micro Nano Lett 14(1):69–73. https://doi.org/10.1049/mnl.2018.5086CrossRef

133.

Tripathi SL, Sinha SK, Patel GS (2020) Low-power efficient p+ Si0.7Ge0.3 pocket junctionless SGTFET with varying operating conditions. J Electron Mater 49(7):4291–4299. https://doi.org/10.1007/s11664-020-08145-3CrossRef

134.

Kumar N, Raman A (2019) Performance assessment of the charge-plasma-based cylindrical GAA vertical nanowire TFET with impact of interface trap charges. IEEE Trans Electron Devices 66(10):4453–4460. https://doi.org/10.1109/TED.2019.2935342CrossRef

135.

Bhattacharjee D, Goswami B, Dash DK, Bhattacharya A, Sarkar SK (2019) Analytical modelling and simulation of drain doping engineered splitted drain structured TFET and its improved performance in subduing ambipolar effect. IET Circuits Devices Syst 13(6):888–895. https://doi.org/10.1049/iet-cds.2018.5261CrossRef

136.

Vanlalawpuia K, Bhowmick B (2019) Investigation of a Ge-source vertical TFET with delta-doped layer. IEEE Trans Electron Devices 66(10):4439–4445. https://doi.org/10.1109/TED.2019.2933313CrossRef

137.

Ahn DH, Yoon SH, Kato K, Fukui T, Takenaka M, Takagi S (2019) Effects of ZrO₂/Al₂O₃ gate-stack on the performance of planar-type InGaAs TFET. IEEE Trans Electron Devices 66(4):1862–1867. https://doi.org/10.1109/TED.2019.2897821CrossRef

138.

Ghosh P, Bhowmick B (2018) Low-frequency noise analysis of heterojunction SELBOX TFET. Appl Phys A Mater Sci Process 124(12):838. https://doi.org/10.1007/s00339-018-2264-3CrossRef

139.

Singh AK, Tripathy MR, Chander S, Baral K, Singh PK, Jit S (2019) Simulation study and comparative analysis of some TFET structures with a novel partial-ground-plane (PGP) based TFET on SELBOX structure. SILICON 12(10):2345–2354. https://doi.org/10.1007/s12633-019-00330-2CrossRef

140.

Verma M, Tirkey S, Yadav S, Sharma D, Yadav DS (2017) Performance assessment of a novel vertical dielectrically modulated TFET-based biosensor. IEEE Trans Electron Devices 64(9):3841–3848. https://doi.org/10.1109/TED.2017.2732820CrossRef

Titel: Design of tunnel FET architectures for low power application using improved Chimp optimizer algorithm
verfasst von: Sabitabrata Bhattacharya
Suman Lata Tripathi
Vikram Kumar Kamboj
Publikationsdatum: 12.11.2021
Verlag: Springer London
Erschienen in: Engineering with Computers / Ausgabe 2/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-021-01530-4

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