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The use of ensemble methods for indirect test of RF circuits

Hassan El Badawi 1 Florence Azaïs 1 Serge Bernard 2 Mariane Comte 1 Vincent Kerzérho 2 François Lefevre 3
1 TEST - TEST
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 SmartIES - Smart Integrated Electronic Systems
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : 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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Submitted on : Friday, March 27, 2020 - 1:47:50 PM
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  • HAL Id : lirmm-02375900, version 1

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Hassan El Badawi, Florence Azaïs, Serge Bernard, Mariane Comte, Vincent Kerzérho, et al.. The use of ensemble methods for indirect test of RF circuits. 13ème Colloque National Du GDR SoC², Jun 2019, Montpellier, France. ⟨lirmm-02375900⟩

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