Multivariate statistical techniques for analog parametric test metrics estimation

Huang, Ke; Stratigopoulos, Haralampos-G.; Abdallah, L.; Mir, S.; Bounceur, Ahcène

doi:10.1109/dtis.2013.6527768

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The study should also be repeated for more advanced nodes, such as 28 nm FDSOI, to see if the principles are maintained in different processes, as well as for parametric circuit faults and performance testing. Finally, the use of bias control as an additional test control [61], multivariate and other statistical test techniques [64] as well as adaptive test thresholding based on known wafer or die data [46], [51] should be considered to improve test discrimination.…”

Section: Discussionmentioning

confidence: 99%

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Nel

Dualibe

Stander

2023

IEEE Open J. Circuits Syst.

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Oscillation-based testing (OBT) and Oscillation-based built-in self-testing (OBIST) circuits enable detection of catastrophic faults in analogue and RF circuits, but both are sensitive to process, voltage and temperature (PVT) variation. This paper investigates 15 OBT and OBIST feature extraction strategies, and four approaches to threshold selection, by calculating figure-of-merit (FoM) across PVT variation. This is done using a 2.4 GHz LNA in 0.35 μm CMOS as DUT. Of the 15 feature extraction approaches, the OBT approaches are found more effective, with some benefit gained from switched-state detection. Of the four approaches to threshold selection (nominal-ranged static thresholds, extreme-range static thresholds, temperature dynamic thresholds, and simple noise-filtered tone detection), dynamic thresholds resulted in the highest average FoM of 0.919, with the best FoM of 0.952, with a corresponding probability of test escape P(TE) and yield loss P(YL) of 5•10 -2 and 1.89•10 -2 respectively but requires accurate temperature measurement. Extreme static threshold selection resulted in a comparable average FoM of 0.912, but with less susceptibility to process variation and without the need for temperature measurement. Binary detection of a noisefiltered oscillating tone is found the least complex approach, with an average FoM of 0.891.

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Section: Discussionmentioning

confidence: 99%

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Nel

Dualibe

Stander

2023

IEEE Open J. Circuits Syst.

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“…This is not evident, since the test metrics have been estimated from a relatively small initial sample of circuits obtained 31:4 K. Beznia et al via Monte Carlo simulation. To illustrate, we will consider the experiment described in Huang et al [2013] for the estimation of test metrics for a built-in test technique for an RF Low Noise Amplifier (LNA). In this experiment, a highly costly Monte Carlo simulation of 10 6 circuits was first performed to obtain results that are used as a reference.…”

Section: Methodological Flow For Model Selectionmentioning

confidence: 99%

A Tool for Analog/RF BIST Evaluation Using Statistical Models of Circuit Parameters

Beznia

Bounceur

Euler

et al. 2015

ACM Trans. Des. Autom. Electron. Syst.

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Testing analog integrated circuits is expensive in terms of both test equipment and time. To reduce the cost, Design-For-Test techniques (DFT) such as Built-In Self-Test (BIST) have been developed. For a given Circuit Under Test (CUT), the choice of a suitable technique should be made at the design stage as a result of the analysis of test metrics such as test escapes and yield loss. However, it is very hard to carry out this estimation for analog/RF circuits by using fault simulation techniques. Instead, the estimation of parametric test metrics is made possible by Monte Carlo circuit-level simulations and the construction of statistical models. These models represent the output parameter space of the CUT in which the test metrics are defined. In addition, models of the input parameter space may be required to accelerate the simulations and obtain higher confidence in the DFT choices. In this work, we describe a methodological flow for the selection of most adequate statistical models and several techniques that can be used for obtaining these models. Some of these techniques have been integrated into a Computer-Aided Test (CAT) tool for the automation of the process of test metrics estimation. This estimation is illustrated for the case of a BIST solution for CMOS imager pixels that requires the use of advanced statistical modeling techniques. . 2015. A tool for analog/RF BIST evaluation using statistical models of circuit parameters.

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“…We have a data set of a Monte Carlo circuit simulation that contais 10 6 samples with the LNA performances and the BIT sensor outputs [15]. For each sample, we have as output parameters {N F, S 11 , Gain} and the test measures {T ED , T CS }.…”

Section: Test Vehiclementioning

confidence: 99%

Estimation of Analog/RF Parametric Test Metrics Based on a Multivariate Extreme Value Model

Bounceur

Mir

Euler

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Analog/RF built-in test (BIT) techniques are essential for reducing the very high costs of specification-based tests and for high-safety applications. The adoption of a BIT technique needs to be decided at the design stage, and this can be facilitated by estimating the test quality in terms of errors such as Test Escapes (TE) and Yield Loss (YL). Test quality estimation at the design stage has been traditionally very difficult for analog/RF circuits due to the lack of fault models that properly cover parametric faulty behavior. In recent years, statistical simulation has been considered in combination with learning techniques for the estimation of parametric test metrics. Extreme Value Theory (EVT) has provided a rigorous tool for the computation of parametric test metrics. However, test metrics estimation has been limited to the use of a univariate model. In this paper, we extend this approach by using a multivariate extreme value model. We illustrate this for the evaluation of a RF LNA BIT technique using a bivariate model.

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Multivariate statistical techniques for analog parametric test metrics estimation

Cited by 5 publications

References 11 publications

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

Influence of PVT Variation and Threshold Selection on OBT and OBIST Fault Detection in RFCMOS Amplifiers

A Tool for Analog/RF BIST Evaluation Using Statistical Models of Circuit Parameters

Estimation of Analog/RF Parametric Test Metrics Based on a Multivariate Extreme Value Model

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