On the Use of Redundancy to Reduce Prediction Error in Alternate Analog/RF Test

Ayari, Haithem; Azaïs, Florence; Bernard, Serge; Comte, Mariane; Kerzérho, Vincent; Potin, Olivier; Renovell, Michel

doi:10.1109/ims3tw.2012.17

Cited by 5 publications

(14 citation statements)

References 20 publications

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“…In our previous work [9], experiments performed on a Power Amplifier (PA) fabricated by NXP Semiconductors for which we have production test data from 10,000 devices have revealed that the large majority of devices can indeed be accurately predicted using the alternate test strategy, but rather large prediction errors are observed for some circuits. Although the number of devices affected by such large prediction error is very small, i.e.…”

Section: Previous Work and Limitationsmentioning

confidence: 99%

“…Alternate testing has been widely studied in the literature for many years [1][2][3][4][5][6][7][8][9][10]. Many 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 multi-Vdd test conditions and procedures for the selection of appropriate indirect measurements.…”

Section: Introductionmentioning

confidence: 99%

“…To cope with this issue, an interesting approach is to use a two-tier test scheme in which devices for which the alternate test decision may be prone to error are identified and directed towards a different tier where further testing may apply [7]. Different solutions have been explored for the identification of suspect devices based either on kernel density estimation [8] or on the use of redundancy [9,10]. In this paper, we extend our previous work based on redundancy, and we explore various options for implementing model redundancy with the objective to strengthen the confidence in alternate test predictions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

New implementions of predictive alternate analog/RF test with augmented model redundancy

Ayari

Azais

Bernard

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

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This paper discusses new implementations of the predictive alternate test strategy that exploit model redundancy in order to improve test confidence. The key idea is to build during the training phase, not only one regression model for each specification as in the classical implementation, but several regression models. This redundancy is then used during the testing phase to identify suspect predictions and remove the corresponding devices from the alternate test flow. In this paper, we explore various options for implementing model redundancy, based on the use of different indirect measurement combinations and/or different partitions of the training set. The proposed implementations are evaluated on a real case study for which we have production test data from 10,000 devices.

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Section: Previous Work and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New implementions of predictive alternate analog/RF test with augmented model redundancy

Ayari

Azais

Bernard

et al. 2014

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2014

View full text Add to dashboard Cite

show abstract

“…Despite the clear advantages of employing such approach and a number of convincing attempts to prove its efficiency [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21], the deployment of the indirect test strategy in industry is limited. A solution to enhance the confidence in the test method is to implement a strengthened strategy based on prediction model redundancy [21]. According to [21], the combination of the predictions of three models with strong correlations between IMs and specifications, allows chasing the rare misjudged circuits.…”

Section: Introductionmentioning

confidence: 99%

Enhancing confidence in indirect analog/RF testing against the lack of correlation between regular parameters and indirect measurements

Ayari¹,

Azaïs²,

Bernard³

et al. 2014

Microelectronics Journal

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The greedy specification testing remains mandatory for analog and Radio Frequency (RF) integrated circuits because of the accuracy of the sorting based on these measurements. Unfortunately, to be implemented, this kind of testing method often incurs very high costs (expensive instruments, long test time...). A common approach, in the literature, is the so-called indirect/alternate test strategy. This strategy consists in deriving targeted specifications from low-cost Indirect Measurements (IMs). During the industrial test phase, the estimation of regular specifications using IMs is based on a correlation model that has been built previously, during a training phase. Despite the substantial test cost reduction offered by this strategy, its deployment in industry is limited, mainly because of a lack of confidence in the accuracy of estimations made by the correlation model. A solution to increase the confidence in the estimation of specifications using the indirect approach is to implement redundancy in the prediction phase. In this paper, we demonstrate that the redundancy implementation brings more than identifying rare misjudged circuits from a high-correlated model. Indeed redundancy massively increases the accuracy despite of the lack of accurate models that have been assumed in previous implementations of redundant indirect testing. This approach is illustrated on a real case study for which we have experimental measurements on a set of 10,000 devices.

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“…To cope with this issue, an interesting proposal was introduced in [3] that suggests to use a two-tier test scheme in which devices for which the alternate test decision may be prone to error are identified and directed towards a different tier where further testing may apply. Following this approach, we have introduced the use of redundancy as a mean to identify suspect predictions in the context of predictive alternate testing [4,5]. In this work, we extend this strategy by exploring various options for model redundancy implementation with the objective to strengthen the confidence in alternate test predictions.…”

Section: Introductionmentioning

confidence: 99%

Implementing model redundancy in predictive alternate test to improve test confidence

Ayari

Azaïs

Bernard

et al. 2013

2013 18th Ieee European Test Symposium (Ets)

Self Cite

View full text Add to dashboard Cite

This work investigates new implementations of the predictive alternate test strategy that exploit model redundancy in order to improve test confidence. The key idea is to build during the training phase, not only one regression model for each specification as in the classical implementation, but several regression models. We explore various options for implementing model redundancy, based on the use of different indirect measurement combinations and/or different partitions of the training set.

show abstract

On the Use of Redundancy to Reduce Prediction Error in Alternate Analog/RF Test

Cited by 5 publications

References 20 publications

New implementions of predictive alternate analog/RF test with augmented model redundancy

New implementions of predictive alternate analog/RF test with augmented model redundancy

Enhancing confidence in indirect analog/RF testing against the lack of correlation between regular parameters and indirect measurements

Implementing model redundancy in predictive alternate test to improve test confidence

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