Statistical characterization of time-dependent variability defects using the maximum current fluctuation
Saraza-Canflanca, Pablo (Instituto de Microelectrónica de Sevilla)
Martin Martinez, Javier (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Castro-Lopez, Rafael (Instituto de Microelectrónica de Sevilla)
Roca, Elisenda (Instituto de Microelectrónica de Sevilla)
Rodríguez Martínez, Rosana (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)
Fernandez, Francisco V. (Instituto de Microelectrónica de Sevilla)
Nafría i Maqueda, Montserrat (Universitat Autònoma de Barcelona. Departament d'Enginyeria Electrònica)

Fecha:	2021
Resumen:	This article presents a new methodology to extract, at a given operation condition, the statistical distribution of the number of active defects that contribute to the observed device time-dependent variability, as well as their amplitude distribution. Unlike traditional approaches based on complex and time-consuming individual analysis of thousands of current traces, the proposed approach uses a simpler trace processing, since only the maximum and minimum values of the drain current during a given time interval are needed. Moreover, this extraction method can also estimate defects causing small current shifts, which can be very complex to identify by traditional means. Experimental data in a wide range of gate voltages, from near-threshold up to nominal operation conditions, are analyzed with the proposed methodology.
Ayudas:	Ministerio de Ciencia e Innovación PID2019-103869RB Ministerio de Ciencia e Innovación BES-2017-080160 Ministerio de Economía y Competitividad TEC2016-75151-C3
Derechos:	Tots els drets reservats.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió acceptada per publicar
Materia:	Bias temperature instability (BTI) ; Maximum current fluctuation (MCF) ; Random telegraph noise (RTN) ; Time-dependent variability (TDV) ; Transistor
Publicado en:	IEEE Transactions on Electron Devices, Vol. 68, issue 8 (Aug. 2021) , p. 4039-4044, ISSN 0018-9383

DOI: 10.1109/ted.2021.3086448

Postprint 8 p, 666.7 KB

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