{"id":"https://openalex.org/W4416010224","doi":"https://doi.org/10.48550/arxiv.2505.03764","title":"Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures","display_name":"Ultra-Low-Power Spiking Neurons in 7 nm FinFET Technology: A Comparative Analysis of Leaky Integrate-and-Fire, Morris-Lecar, and Axon-Hillock Architectures","publication_year":2025,"publication_date":"2025-04-21","ids":{"openalex":"https://openalex.org/W4416010224","doi":"https://doi.org/10.48550/arxiv.2505.03764"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2505.03764","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03764","pdf_url":"https://arxiv.org/pdf/2505.03764","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2505.03764","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120299325","display_name":"Logan Larsh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Larsh, Logan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107546993","display_name":"Raiyan Siddique","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siddique, Raiyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5120299326","display_name":"Sarah Sharif Yaser Mike Banad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Banad, Sarah Sharif Yaser Mike","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8176000118255615,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8176000118255615,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.09009999781847,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.035999998450279236,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.878600001335144},{"id":"https://openalex.org/keywords/subthreshold-conduction","display_name":"Subthreshold conduction","score":0.8686000108718872},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.7353000044822693},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.6057000160217285},{"id":"https://openalex.org/keywords/spice","display_name":"Spice","score":0.5928999781608582},{"id":"https://openalex.org/keywords/cmos","display_name":"CMOS","score":0.5031999945640564},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.5001000165939331},{"id":"https://openalex.org/keywords/electronic-circuit","display_name":"Electronic circuit","score":0.48240000009536743},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.45239999890327454},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44780001044273376}],"concepts":[{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.878600001335144},{"id":"https://openalex.org/C156465305","wikidata":"https://www.wikidata.org/wiki/Q1658601","display_name":"Subthreshold conduction","level":4,"score":0.8686000108718872},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.7353000044822693},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.6057000160217285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6055999994277954},{"id":"https://openalex.org/C2780077345","wikidata":"https://www.wikidata.org/wiki/Q16891888","display_name":"Spice","level":2,"score":0.5928999781608582},{"id":"https://openalex.org/C46362747","wikidata":"https://www.wikidata.org/wiki/Q173431","display_name":"CMOS","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.5001000165939331},{"id":"https://openalex.org/C134146338","wikidata":"https://www.wikidata.org/wiki/Q1815901","display_name":"Electronic circuit","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.47679999470710754},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.45239999890327454},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44780001044273376},{"id":"https://openalex.org/C2777042071","wikidata":"https://www.wikidata.org/wiki/Q6509304","display_name":"Leakage (economics)","level":2,"score":0.4399000108242035},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C117551214","wikidata":"https://www.wikidata.org/wiki/Q6692774","display_name":"Low-power electronics","level":4,"score":0.38350000977516174},{"id":"https://openalex.org/C7366592","wikidata":"https://www.wikidata.org/wiki/Q1255620","display_name":"Dram","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C195370968","wikidata":"https://www.wikidata.org/wiki/Q1754002","display_name":"Threshold voltage","level":4,"score":0.35989999771118164},{"id":"https://openalex.org/C195221683","wikidata":"https://www.wikidata.org/wiki/Q5000649","display_name":"Bursting","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.34689998626708984},{"id":"https://openalex.org/C530198007","wikidata":"https://www.wikidata.org/wiki/Q80831","display_name":"Integrated circuit","level":2,"score":0.34119999408721924},{"id":"https://openalex.org/C172385210","wikidata":"https://www.wikidata.org/wiki/Q5339","display_name":"Transistor","level":3,"score":0.3400999903678894},{"id":"https://openalex.org/C131017901","wikidata":"https://www.wikidata.org/wiki/Q170451","display_name":"Logic gate","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C150072547","wikidata":"https://www.wikidata.org/wiki/Q212923","display_name":"Memristor","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C190560348","wikidata":"https://www.wikidata.org/wiki/Q3245116","display_name":"Circuit design","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C182019814","wikidata":"https://www.wikidata.org/wiki/Q1143830","display_name":"Resistive random-access memory","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C118993495","wikidata":"https://www.wikidata.org/wiki/Q5042828","display_name":"Electrical efficiency","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C186565885","wikidata":"https://www.wikidata.org/wiki/Q1651163","display_name":"Biological neuron model","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C135402231","wikidata":"https://www.wikidata.org/wiki/Q898440","display_name":"Dissipation","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26969999074935913},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.2556999921798706}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2505.03764","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03764","pdf_url":"https://arxiv.org/pdf/2505.03764","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2505.03764","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2505.03764","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2505.03764","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2505.03764","pdf_url":"https://arxiv.org/pdf/2505.03764","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Neuromorphic":[0],"computing":[1],"aims":[2],"to":[3,80,95,129,193],"replicate":[4],"the":[5,52,68,72,89,161],"brain's":[6],"remarkable":[7],"energy":[8,57,85,155],"efficiency":[9,156],"and":[10,37,60,107,131,157,202],"parallel":[11],"processing":[12],"capabilities":[13],"for":[14,119,175,184],"large-scale":[15],"artificial":[16],"intelligence":[17],"applications.":[18],"In":[19],"this":[20],"work,":[21],"we":[22,50],"present":[23],"a":[24,41,115,182],"comprehensive":[25],"comparative":[26],"study":[27],"of":[28,54,163,198],"three":[29],"spiking":[30,55,186],"neuron":[31,177,187],"circuit":[32],"architectures-Leaky-Integrate-and-Fire":[33],"(LIF),":[34],"Morris-Lecar":[35],"(ML),":[36],"Axon-Hillock":[38],"(AH)-implemented":[39],"in":[40,93,167,189],"7":[42,149],"nm":[43,143,150],"FinFET":[44],"technology.":[45],"Through":[46],"extensive":[47],"SPICE":[48],"simulations,":[49],"explore":[51],"optimization":[53],"frequency,":[56],"per":[58],"spike,":[59],"static":[61,126],"power":[62],"consumption.":[63],"Our":[64],"results":[65],"show":[66],"that":[67,148],"AH":[69,132],"design":[70,173],"achieves":[71],"highest":[73],"throughput,":[74],"demonstrating":[75],"multi-gigahertz":[76],"firing":[77],"rates":[78],"(up":[79],"3":[81],"GHz)":[82],"with":[83,138],"attojoule":[84],"costs.":[86],"By":[87,171],"contrast,":[88],"ML":[90,130],"architecture":[91],"excels":[92],"subthreshold":[94,165,169],"near-threshold":[96],"regimes,":[97],"offering":[98],"robust":[99],"low-power":[100],"operation":[101,201],"(as":[102],"low":[103],"as":[104],"0.385":[105],"aJ/spike)":[106],"biological":[108],"bursting":[109],"behavior.":[110],"Although":[111],"LIF":[112],"benefits":[113],"from":[114],"decoupled":[116],"current":[117],"mirror":[118],"high-frequency":[120],"operation,":[121],"it":[122],"exhibits":[123],"slightly":[124],"higher":[125],"leakage":[127,166],"compared":[128],"at":[133,160],"elevated":[134],"supply":[135],"voltages.":[136],"Comparisons":[137],"previous":[139],"node":[140],"implementations":[141],"(22":[142],"planar,":[144],"28":[145],"nm)":[146],"reveal":[147],"FinFETs":[151],"can":[152],"drastically":[153],"boost":[154],"speed":[158],"albeit":[159],"cost":[162],"increased":[164],"deep":[168],"regions.":[170],"quantifying":[172],"trade-offs":[174],"each":[176],"architecture,":[178],"our":[179],"work":[180],"provides":[181],"roadmap":[183],"optimizing":[185],"circuits":[188],"advanced":[190],"nanoscale":[191],"technologies":[192],"deliver":[194],"neuromorphic":[195],"hardware":[196],"capable":[197],"both":[199],"ultra-low-power":[200],"high":[203],"computational":[204],"throughput.":[205]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}