A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models

.In modern nanometer-scale CMOS technologies, time-zero and time-dependent variability (TDV) effects, the latter coming from aging mechanisms like Bias Temperature Instability (BTI), Hot Carrier Injection (HCI) or Random Telegraph Noise (RTN), have re-emerged as a serious threat affecting the perfor...

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Autores: Díaz-Fortuny, Javier, Martín-Martínez, Javier, Rodríguez, Rosana, Castro-López, Rafael, Roca, Elisenda, Fernández, Francisco V., Nafría, Montserrat
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/189751
Acceso en línea:http://hdl.handle.net/10261/189751
Access Level:acceso abierto
Palabra clave:Parameters
HCI
BTI
CMOS
Extraction
Aging
Defects
RTN
Method
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spelling A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact modelsDíaz-Fortuny, JavierMartín-Martínez, JavierRodríguez, RosanaCastro-López, RafaelRoca, ElisendaFernández, Francisco V.Nafría, MontserratParametersHCIBTICMOSExtractionAgingDefectsRTNMethod.In modern nanometer-scale CMOS technologies, time-zero and time-dependent variability (TDV) effects, the latter coming from aging mechanisms like Bias Temperature Instability (BTI), Hot Carrier Injection (HCI) or Random Telegraph Noise (RTN), have re-emerged as a serious threat affecting the performance of analog and digital integrated circuits. Variability induced by the aging phenomena can lead circuits to a progressive malfunction or failure. In order to understand the effects of the mentioned variability sources, a precise and sound statistical characterization and modeling of these effects should be done. Typically, transistor TDV characterization entails long, and typically prohibitive, testing times, as well as huge amounts of data, which are complex to post-process. In order to face these limitations, this work presents a new method to statistically characterize the emission times and threshold voltage shifts (ΔVth) related to oxide defects in nanometer CMOS transistors during aging tests. At the same time, the aging testing methodology significantly reduces testing times by parallelizing the stress. The method identifies the Vth drops associated to oxide trap emissions during BTI and HCI aging recovery traces while removing RTN and background noise contributions, to avoid artifacts during data analysis.Peer ReviewedPergamon PressConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2019201920192019info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/189751reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésSíinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1897512026-05-22T06:33:51Z
dc.title.none.fl_str_mv A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
title A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
spellingShingle A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
Díaz-Fortuny, Javier
Parameters
HCI
BTI
CMOS
Extraction
Aging
Defects
RTN
Method
title_short A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
title_full A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
title_fullStr A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
title_full_unstemmed A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
title_sort A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models
dc.creator.none.fl_str_mv Díaz-Fortuny, Javier
Martín-Martínez, Javier
Rodríguez, Rosana
Castro-López, Rafael
Roca, Elisenda
Fernández, Francisco V.
Nafría, Montserrat
author Díaz-Fortuny, Javier
author_facet Díaz-Fortuny, Javier
Martín-Martínez, Javier
Rodríguez, Rosana
Castro-López, Rafael
Roca, Elisenda
Fernández, Francisco V.
Nafría, Montserrat
author_role author
author2 Martín-Martínez, Javier
Rodríguez, Rosana
Castro-López, Rafael
Roca, Elisenda
Fernández, Francisco V.
Nafría, Montserrat
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Parameters
HCI
BTI
CMOS
Extraction
Aging
Defects
RTN
Method
topic Parameters
HCI
BTI
CMOS
Extraction
Aging
Defects
RTN
Method
description .In modern nanometer-scale CMOS technologies, time-zero and time-dependent variability (TDV) effects, the latter coming from aging mechanisms like Bias Temperature Instability (BTI), Hot Carrier Injection (HCI) or Random Telegraph Noise (RTN), have re-emerged as a serious threat affecting the performance of analog and digital integrated circuits. Variability induced by the aging phenomena can lead circuits to a progressive malfunction or failure. In order to understand the effects of the mentioned variability sources, a precise and sound statistical characterization and modeling of these effects should be done. Typically, transistor TDV characterization entails long, and typically prohibitive, testing times, as well as huge amounts of data, which are complex to post-process. In order to face these limitations, this work presents a new method to statistically characterize the emission times and threshold voltage shifts (ΔVth) related to oxide defects in nanometer CMOS transistors during aging tests. At the same time, the aging testing methodology significantly reduces testing times by parallelizing the stress. The method identifies the Vth drops associated to oxide trap emissions during BTI and HCI aging recovery traces while removing RTN and background noise contributions, to avoid artifacts during data analysis.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/189751
url http://hdl.handle.net/10261/189751
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Pergamon Press
publisher.none.fl_str_mv Pergamon Press
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422969054298112
score 15,811543

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