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Indirect test of RF circuits using ensemble methods

Hassan El Badawi 1 Florence Azaïs 1 Serge Bernard 2 Mariane Comte 1 Vincent Kerzérho 2 François Lefèvre 3 
1 TEST - Test and dEpendability of microelectronic integrated SysTems
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 : The adoption of indirect test for analog and RF integrated circuits (ICs) can tackle the rising costs of the classical industrial testing of these circuits, hence relaxing the requirements on test equipment. Based on machine-learning techniques, the concept of indirect test is to create a mapping between an indirect and low-cost measurement and the performance of the circuit by training a regression model. In this work, we explore the potential benefit of using ensemble learning. Instead of using a single regression model to predict the performance, the use of ensemble learning consists of combining multiple regression models to enhance the model's generalization. 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 : Thursday, November 12, 2020 - 1:54:13 PM
Last modification on : Friday, August 5, 2022 - 3:03:29 PM
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  • HAL Id : lirmm-03001530, version 1


Hassan El Badawi, Florence Azaïs, Serge Bernard, Mariane Comte, Vincent Kerzérho, et al.. Indirect test of RF circuits using ensemble methods. ETS 2020 - 25th IEEE European Test Symposium, May 2020, Tallinn, Estonia. ⟨lirmm-03001530⟩



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