Statistical Characterization of Time-Dependent Variability Defects Using the Maximum Current Fluctuation

Saraza-Canflanca, P.; Martín-Martínez, J.; Castro-López, R.; Roca, E.; Rodrı́guez, R.; Fernandez, F.V.; Nafría, M.

doi:10.1109/ted.2021.3086448

Cited by 10 publications

(19 citation statements)

References 19 publications

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“…The statistical distribution of time constants is difficult to determine experimentally. To simplify tests, attempts were made to focus on either capture [11], [24] or emission [3].…”

Section: B the Concept Of Effective Charged Traps (Ect)mentioning

confidence: 99%

“…In Section III.B, we will estimate the percentage of traps that are charged. The envelopes have been used to estimate the worst impact of RTN [11], [24], [25]. To optimize circuit design, however, one needs to model the probability of ∆Vth at any level between > Manuscript ID: TED-2022-02-0287-R < 3 the two envelopes, which is the objective of this work.…”

Section: B the Concept Of Effective Charged Traps (Ect)mentioning

confidence: 99%

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An Integral Methodology for Predicting Long-Term RTN

Tok

Mehedi

Zhang

et al. 2022

IEEE Trans. Electron Devices

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Random Telegraph Noise (RTN) adversely impacts circuit performance and this impact increases for smaller devices and lower operation voltage. To optimize circuit design, many efforts have been made to model RTN. RTN is highly stochastic, with significant device-to-device variations. Early works often characterize individual traps first and then group them together to extract their statistical distributions. This bottom-up approach suffers from limitations in the number of traps it is possible to measure, especially for the capture and emission time constants, calling the reliability of extracted distributions into question. Several compact models have been proposed, but their ability to predict long term RTN is not verified. Many early works measured RTN only for tens of seconds, although a longer time window increases RTN by capturing slower traps. The aim of this work is to propose an integral methodology for modelling RTN and, for the first time, to verify its capability of predicting the long term RTN. Instead of characterizing properties of individual traps/devices, the RTN of multiple devices were integrated to form one dataset for extracting their statistical properties. This allows using the concept of effective charged traps (ECT) and transforms the need for time constant distribution to obtaining the kinetics of ECT, making long term RTN prediction similar to predicting ageing. The proposed methodology opens the way for assessing RTN impact within a window of 10 years by efficiently evaluating the probability of a device parameter at a given level.

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“…The statistical distribution of time constants is difficult to determine experimentally. To simplify tests, attempts were made to focus on either capture [11], [24] or emission [3].…”

Section: B the Concept Of Effective Charged Traps (Ect)mentioning

confidence: 99%

Section: B the Concept Of Effective Charged Traps (Ect)mentioning

confidence: 99%

An Integral Methodology for Predicting Long-Term RTN

Tok

Mehedi

Zhang

et al. 2022

IEEE Trans. Electron Devices

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“…In the following, a different metric will be used to quantify the impact of the biasing history on the measurable RTN activity. This is the Maximum Current Fluctuation (MCF), which requires a very simple processing of the current traces measured in the lab and is very useful to evaluate the impact of RTN in them [19].…”

Section: A Impact On Measurementsmentioning

confidence: 99%

“…12(c). The cdf of the MCF at any time instant can be related to the distribution of the current shift amplitudes and to the distribution of the number of active defects up to that point [19]. Fig.…”

Section: A Impact On Measurementsmentioning

confidence: 99%

“…In this subsection, the impact on the extracted RTN parameters, obtained using the extraction method in [19], will be studied. That method relates the MCF at any time instant with the number of active defects (i.e., defects that suffer at least one trapping/detrapping event up to that time) and the amplitude shifts induced by such defects, as well as, the For the amplitude shifts, a two-lognormal distribution is assumed [19]:…”

Section: B Impact On Parameter Extractionmentioning

confidence: 99%

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On the Impact of the Biasing History on the Characterization of Random Telegraph Noise

Saraza-Canflanca

Castro-López

Roca

et al. 2022

IEEE Trans. Instrum. Meas.

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Random Telegraph Noise is a time-dependent variability phenomenon that has gained increased attention during the last years, especially in deeply-scaled technologies. In particular, there is a wide variety of works presenting different techniques designed to analyze current traces in scaled FET devices displaying Random Telegraph Noise, and others focused on modeling the phenomenon using the parameters extracted through such techniques. However, very little attention has been paid to the effects that the biasing conditions of the transistors prior to the measurements may have on the extraction of the parameters that characterize this phenomenon. This paper investigates how these biasing conditions actually impact the extracted results. Specifically, it is demonstrated that the results obtained when Random Telegraph Noise is measured immediately after the device is biased may lead to an overestimation of the Random Telegraph Noise impact with respect to situations in which the device has been previously biased for some time. This fact is, first, presented from a theoretical point of view, and, after, demonstrated experimentally through measurements obtained from a CMOS-transistor array.

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Challenges and solutions to the defect-centric modeling and circuit simulation of time-dependent variability

Martín-Martínez

Diaz-Fortuny

Saraza-Canflanca

et al. 2023

2023 IEEE International Reliability Physics Symposium (IRPS)

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Time-Dependent Variability (TDV) phenomena represent a serious concern for device and circuit reliability. To address the TDV impact at circuit level, Reliability-Aware Design (RAD) tools can be used by circuit designers to achieve more reliable circuits. However, this is not a straightforward task, since the development of RAD tools comprises several steps such as the characterization, modeling and simulation of TDV phenomena. Furthermore, in deeply-scaled CMOS technologies, TDV reveals a stochastic nature that can complicate those steps. In this invited paper, we review some of the main challenges that appear in each step of the flow towards the development of RAD tools, providing our solutions to them.

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Statistical Characterization of Time-Dependent Variability Defects Using the Maximum Current Fluctuation

Cited by 10 publications

References 19 publications

An Integral Methodology for Predicting Long-Term RTN

An Integral Methodology for Predicting Long-Term RTN

On the Impact of the Biasing History on the Characterization of Random Telegraph Noise

Challenges and solutions to the defect-centric modeling and circuit simulation of time-dependent variability

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