A procedure for Alternate Test feature design and selection

Barragán, Manuel J.; Léger, Gildas

doi:10.1109/mdat.2014.2361722

Cited by 28 publications

(26 citation statements)

References 11 publications

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“…This approach has been introduced first for analog circuits [1], and then extended to RF circuits [2]. Several aspects have been researched, such as the influence of the training set [3], the use of embedded sensors to gather pertinent information [4], the exploitation of multi-Vdd test conditions [5], or the selection of appropriate indirect measurements [6][7][8][9]. A comprehensive review of works related to indirect testing can be found in [10].…”

Section: Introductionmentioning

confidence: 99%

Which metrics to use for RF indirect test strategy?

Badawi

Comte

Azaïs

et al. 2019

2019 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (

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This paper aims at opening a discussion on the quality assessment of indirect test strategies in the context of analog and RF integrated circuit testing. Many parameters may influence the prediction efficiency of the indirect test model (choice and number of indirect parameters taken into account, learning algorithm used to build the model…). In order to evaluate the quality of a given model, several metrics can be evaluated, that reflect either the average prediction error, a global reliability or a misclassification rate. But what are the most pertinent metrics to reflect the level of confidence that can be expected from the indirect test to efficiently replace a traditional test based on RF measurements? Which metrics can lead to an informed choice of an indirect test strategy for its stability and predictive power? These considerations are investigated in this paper and illustrated in a practical case study.

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Section: Introductionmentioning

confidence: 99%

Which metrics to use for RF indirect test strategy?

Badawi

Comte

Azaïs

et al. 2019

2019 16th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (

View full text Add to dashboard Cite

show abstract

“…It is quite clear that a set of alternate measurements to be used for binning purposes has to satisfy two main properties: (1) the measurements need to reflect circuit's performances variability in order to allow the binning to be performed efficiently and (2) an adequate set of measurements should not be redundant to avoid incurring in extra binning costs [31]- [33]. The first condition is achieved by means of a sensitivity analysis of candidate alternate measurements.…”

Section: B Alternate Measurements Selection Criterionmentioning

confidence: 99%

Efficient Production Binning Using Octree Tessellation in the Alternate Measurements Space

Gómez-Pau

Balado

Figueras

2016

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

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“…Indirect testing has been widely studied in the literature for many years [1][2][3][4][5][6][7][8][9][10]. Numerous aspects have been researched, such as the choice of the learning algorithm, the definition and optimization of appropriate test stimuli, the processing of complex signatures, the use of embedded sensors to gather pertinent information, the exploitation of multi-Vdd test conditions and procedures for the selection of appropriate indirect measurements.…”

Section: Introductionmentioning

confidence: 99%

Use of ensemble methods for indirect test of RF circuits: can it bring benefits?

Badawi

Azaïs

Bernard

et al. 2019

2019 IEEE Latin American Test Symposium (LATS)

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Indirect testing of analog and RF integrated circuits is a widely studied approach, which has the benefits of relaxing requirements on test equipment and reducing industrial test cost. It is based on machine-learning algorithms to train a regression model that maps an indirect and low-cost measurement space to the performance parameter space. In this work, we explore the benefit of using ensemble learning. Rather than using one single model to estimate targeted parameters, ensemble learning consists of training multiple individual regression models and combining their outputs in order to improve the predictive power. Different ensemble methods based on bagging, boosting or stacking are investigated and compared to classical individual models. Results are illustrated and discussed on three RF performances of a LNA for which we have production test data.

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A procedure for Alternate Test feature design and selection

Cited by 28 publications

References 11 publications

Which metrics to use for RF indirect test strategy?

Which metrics to use for RF indirect test strategy?

Efficient Production Binning Using Octree Tessellation in the Alternate Measurements Space

Use of ensemble methods for indirect test of RF circuits: can it bring benefits?

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