Efficient selection of signatures for analog/RF alternate test

Barragán, Manuel J.; Léger, Gildas

doi:10.1109/ets.2013.6569362

Cited by 30 publications

(16 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [19], it was proposed to use Brownian Distance correlation (dCor) instead of Pearson's correlation. The distance correlation statistics is defined in [20].…”

Section: Correlation With Performancementioning

confidence: 99%

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Léger

Barragán

2016

Integration

Self Cite

View full text Add to dashboard Cite

International audienceMachine-learning indirect test relies on powerful statistical algorithms to build prediction models that relate cheap measurements to costly performance metrics. Though many works in the past have been focused on proposing different models or on ways to improve the reliability of the results, it appears that the main bottleneck of the approach is the definition of an information-rich input space. Finding the appropriate measurements that are both cheap and meaningful is a task that has not yet been automated. In this framework, feature selection is a necessary tool to explore possible candidates. In this paper a hybrid method is proposed that lay between filtering and wrapper-based methods, trying to strike the right balance between accuracy and speed for the particular case of Alternate Test

show abstract

“…In [19], it was proposed to use Brownian Distance correlation (dCor) instead of Pearson's correlation. The distance correlation statistics is defined in [20].…”

Section: Correlation With Performancementioning

confidence: 99%

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Léger

Barragán

2016

Integration

Self Cite

View full text Add to dashboard Cite

show abstract

“…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 (

View full text Add to dashboard Cite

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.

show abstract

“…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)

View full text Add to dashboard Cite

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.

show abstract

Efficient selection of signatures for analog/RF alternate test

Cited by 30 publications

References 20 publications

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Brownian distance correlation-directed search: A fast feature selection technique for alternate test

Which metrics to use for RF indirect test strategy?

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

Contact Info

Product

Resources

About