Efficient generation of data sets for one-shot statistical calibration of RF/mm-wave circuits

International audienc

Conception en vue du test d’un amplificateur de puissance à 60 GHz

National audienc

On the use of causal feature selection in the context of machine-learning indirect test

International audienc

On the use of causal feature selection in the context of machine-learning indirect test

International audienc

On the use of causal feature selection in the context of machine-learning indirect test

International audienc

Assisted test generation strategy for non-intrusive machine learning indirect test of millimeter-wave circuits

International audienceThe functional test of millimeter-wave (mm-wave) circuitry in the production line is a challenging task that requires costly dedicated test equipment and long test times. Machine learning indirect test offers an appealing alternative to standard mm-wave functional test by replacing the direct measurement of the circuit performances by a set of indirect measurements, usually called signatures. Machine learning regression algorithms are then used to map signatures and performances. In this work, we present a generic and automated methodology for finding an appropriate set of indirect measurements and assisting the designer with the necessary Design-for-Test circuit modifications. In order to avoid complex design modifications of mm-wave circuitry, the proposed strategy is targeted at generating a set of non-intrusive indirect measurements using process variation sensors not connected to the Device Under Test (DUT). The proposed methodology is demonstrated on a 60 GHz Power Amplifier designed in STMicroelectronics 55 nm BiCMOS technology

On the use of causal feature selection in the context of machine-learning indirect test

International audienc

On the use of causal feature selection in the context of machine-learning indirect test

International audienc

Yield Recovery of mm-Wave Power Amplifiers using Variable Decoupling Cells and One-Shot Statistical Calibration

International audienc

Efficient generation of data sets for one-shot statistical calibration of RF/mm-wave circuits

International audienceMillimeter-wave circuits in current nanometric technologies are especially sensitive to process variations, which can seriously degrade the device behavior and reduce fabrication yield. To tackle this issue, conservative designs and large design margins are widely used solutions. Another approach consists in introducing variable elements, also called tuning knobs, to allow post-fabrication tuning. One-shot statistical calibration techniques take advantage of advanced machine learning regression tools to propose a set of tuning knobs values that enhance the circuit performance based on simple measurements. Training the regression models require a huge amount of data covering the device performances, the effect of the tuning knobs and the simple measurements that guide the regression. In this work, we propose an efficient method for generating such a data set that reduces noticeably the size of the required training set for an accurate calibration