A robust and automated methodology for the analysis of Time-Dependent Variability at transistor level

In the past few years, Time-Dependent Variability has become a subject of growing concern in CMOS technologies. In particular, phenomena such as Bias Temperature Instability, Hot-Carrier Injection and Random Telegraph Noise can largely affect circuit reliability. It becomes therefore imperative to d...

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Bibliographic Details
Authors: Saraza-Canflanca, Pablo|||0000-0003-2155-8305, Diaz-Fortuny, Javier|||0000-0002-8186-071X, Castro-Lopez, Rafael|||0000-0002-6247-3124, Roca, Elisenda|||0000-0001-6260-6495, Martin Martinez, Javier|||0000-0001-5938-5898, Rodríguez Martínez, Rosana|||0000-0002-4565-6703, Nafria, Montserrat|||0000-0002-9549-2890, Fernandez, Francisco V.|||0000-0001-8682-2280
Format: article
Publication Date:2020
Country:España
Institution:Universitat Autònoma de Barcelona
Repository:Dipòsit Digital de Documents de la UAB
Language:English
OAI Identifier:oai:ddd.uab.cat:321438
Online Access:https://ddd.uab.cat/record/321438
https://dx.doi.org/urn:doi:10.1016/j.vlsi.2020.02.002
Access Level:Open access
Keyword:Bias temperature instability
BTI
Characterization
CMOS
HCI
Hot-carrier injection
Random telegraph noise
Reliability
RTN
Simulation
TDV
Time-dependent variability
Description
Summary:In the past few years, Time-Dependent Variability has become a subject of growing concern in CMOS technologies. In particular, phenomena such as Bias Temperature Instability, Hot-Carrier Injection and Random Telegraph Noise can largely affect circuit reliability. It becomes therefore imperative to develop reliability-aware design tools to mitigate their impact on circuits. To this end, these phenomena must be first accurately characterized and modeled. And, since all these phenomena reveal a stochastic nature for deeply-scaled integration technologies, they must be characterized massively on devices to extract the probability distribution functions associated to their characteristic parameters. In this work, a complete methodology to characterize these phenomena experimentally, and then extract the necessary parameters to construct a Time-Dependent Variability model, is presented. This model can be used by a reliability simulator.