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

• Published in: Integration (Amsterdam) Vol. 72; pp. 13 - 20
• Main Authors: Saraza-Canflanca, P., Diaz-Fortuny, J., Castro-Lopez, R., Roca, E., Martin-Martinez, J., Rodriguez, R., Nafria, M., Fernandez, F.V.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier BV
• Subjects: Bias temperature instability; BTI; Carrier injection; Characterization; Circuit design; Circuit reliability; Circuits; CMOS; Computer simulation; Distribution functions; HCI; Hot-carrier injection; Mathematical models; Noise; Parameters; Probability distribution functions; Random telegraph noise; Reliability; RTN; Simulation; TDV; Temperature; Time dependence; Time-dependent variability; Transistors; Characterization; CMOS; Hot-carrier injection; Bias temperature instability; Simulation; BTI; TDV; Time-dependent variability; Reliability; Random telegraph noise; RTN; HCI
• ISSN: 0167-9260; 1872-7522
• DOI: 10.1016/j.vlsi.2020.02.002

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. •Complete Reliability-Aware Design flow is presented.•It includes characterization, modeling and simulation of Time-Dependent Variability.•Focus set on the automatic and robust extraction of parameters from TDV experiments.