Ontology type: schema:ScholarlyArticle
2021-11-12
AUTHORSSabitabrata Bhattacharya, Suman Lata Tripathi, Vikram Kumar Kamboj
ABSTRACTAn 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. More... »
PAGES1-44
http://scigraph.springernature.com/pub.10.1007/s00366-021-01530-4
DOIhttp://dx.doi.org/10.1007/s00366-021-01530-4
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1142552104
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Mathematical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0102",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Applied Mathematics",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India",
"id": "http://www.grid.ac/institutes/grid.449005.c",
"name": [
"School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India"
],
"type": "Organization"
},
"familyName": "Bhattacharya",
"givenName": "Sabitabrata",
"id": "sg:person.014533202655.60",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014533202655.60"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India",
"id": "http://www.grid.ac/institutes/grid.449005.c",
"name": [
"School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India"
],
"type": "Organization"
},
"familyName": "Tripathi",
"givenName": "Suman Lata",
"id": "sg:person.015315200145.38",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015315200145.38"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India",
"id": "http://www.grid.ac/institutes/grid.449005.c",
"name": [
"School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India"
],
"type": "Organization"
},
"familyName": "Kamboj",
"givenName": "Vikram Kumar",
"id": "sg:person.013051727071.26",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013051727071.26"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s11047-009-9175-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1003181811",
"https://doi.org/10.1007/s11047-009-9175-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-020-05475-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1134291780",
"https://doi.org/10.1007/s00521-020-05475-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12652-019-01324-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1115647691",
"https://doi.org/10.1007/s12652-019-01324-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00339-016-0510-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1008173216",
"https://doi.org/10.1007/s00339-016-0510-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10462-019-09718-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1115215492",
"https://doi.org/10.1007/s10462-019-09718-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s42835-018-00001-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1111155965",
"https://doi.org/10.1007/s42835-018-00001-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-981-13-1592-3_47",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110579876",
"https://doi.org/10.1007/978-981-13-1592-3_47"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10479-009-0657-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036051273",
"https://doi.org/10.1007/s10479-009-0657-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.2991/ijcis.2018.125905658",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1112771552",
"https://doi.org/10.2991/ijcis.2018.125905658"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00500-019-04640-w",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123792972",
"https://doi.org/10.1007/s00500-019-04640-w"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-015-1870-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037907456",
"https://doi.org/10.1007/s00521-015-1870-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11664-020-08145-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1126894222",
"https://doi.org/10.1007/s11664-020-08145-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10825-018-1222-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105909540",
"https://doi.org/10.1007/s10825-018-1222-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-020-05004-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1128162982",
"https://doi.org/10.1007/s00521-020-05004-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11831-020-09443-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1128065079",
"https://doi.org/10.1007/s11831-020-09443-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00339-018-2264-3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1110133829",
"https://doi.org/10.1007/s00339-018-2264-3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11277-019-06520-w",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1114045671",
"https://doi.org/10.1007/s11277-019-06520-w"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10489-020-01727-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1129036842",
"https://doi.org/10.1007/s10489-020-01727-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00500-018-3282-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1104496082",
"https://doi.org/10.1007/s00500-018-3282-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1023/a:1008202821328",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012950914",
"https://doi.org/10.1023/a:1008202821328"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12633-020-00672-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1130752604",
"https://doi.org/10.1007/s12633-020-00672-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00521-014-1629-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026880794",
"https://doi.org/10.1007/s00521-014-1629-6"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s12633-019-00330-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1123471287",
"https://doi.org/10.1007/s12633-019-00330-2"
],
"type": "CreativeWork"
}
],
"datePublished": "2021-11-12",
"datePublishedReg": "2021-11-12",
"description": "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.",
"genre": "article",
"id": "sg:pub.10.1007/s00366-021-01530-4",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1041785",
"issn": [
"0177-0667",
"1435-5663"
],
"name": "Engineering with Computers",
"publisher": "Springer Nature",
"type": "Periodical"
}
],
"keywords": [
"engineering design problems",
"tunnel field-effect transistor",
"benchmark functions",
"design problem",
"high-dimensional problems",
"typical engineering design problems",
"Tunnel FET",
"global optimal solution",
"quantum mechanical process",
"optimizer algorithm",
"low sub-threshold swing",
"low-power electronic circuits",
"Reduced Leakage Current",
"low power digital",
"sub-threshold swing",
"low supply voltage",
"dimensional problems",
"low power VLSI circuits",
"power electronic circuits",
"low power applications",
"mathematical model",
"steep subthreshold characteristics",
"optimization method",
"field-effect transistors",
"optimal solution",
"low power consumption",
"engineering problems",
"convergence speed",
"poor convergence",
"unimodal benchmark functions",
"power digital",
"power applications",
"local minima",
"supply voltage",
"leakage current",
"device performance",
"effect transistors",
"band tunneling",
"subthreshold characteristics",
"memory applications",
"performance optimization",
"optimizer",
"promising device",
"carrier generation",
"power consumption",
"electronic circuits",
"mechanical processes",
"VLSI circuits",
"improved variant",
"testing results",
"problem",
"FET",
"algorithm",
"standard benchmarks",
"circuit",
"better performance",
"performance improvement",
"devices",
"convergence",
"performance",
"similar performance",
"MOSFETs",
"transistors",
"optimization",
"voltage",
"function",
"applications",
"simulations",
"tunneling",
"solution",
"current",
"speed",
"method",
"swing",
"chaser",
"technique variants",
"design",
"model",
"digital",
"feasibility",
"minimum",
"benchmarks",
"consumption",
"generation",
"requirements",
"characteristics",
"technique",
"process",
"band",
"form",
"different forms",
"attacker",
"improvement",
"variants",
"results",
"research",
"drivers",
"use",
"barriers",
"movement",
"thinking",
"chimps",
"divergent thinking",
"efficacy",
"outcomes",
"paper"
],
"name": "Design of tunnel FET architectures for low power application using improved Chimp optimizer algorithm",
"pagination": "1-44",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1142552104"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00366-021-01530-4"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00366-021-01530-4",
"https://app.dimensions.ai/details/publication/pub.1142552104"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:38",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_880.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00366-021-01530-4"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01530-4'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01530-4'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01530-4'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01530-4'
This table displays all metadata directly associated to this object as RDF triples.
264 TRIPLES
22 PREDICATES
152 URIs
121 LITERALS
4 BLANK NODES