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

F. Cilici (1) , Manuel J. Barragan (1) , Salvador Mir (1) , Estelle Lauga-Larroze (2) , Sylvain Bourdel (2)

1 TIMA - Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés
2 IMEP-LAHC - Institut de Microélectronique, Electromagnétisme et Photonique - Laboratoire d'Hyperfréquences et Caractérisation

The 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.

Mots clés: analog

Domaines: Micro et nanotechnologies/Microélectronique

F. Cilici, Manuel J. Barragan, Salvador Mir, Estelle Lauga-Larroze, Sylvain Bourdel. Assisted test generation strategy for non-intrusive machine learning indirect test of millimeter-wave circuits. 23rd IEEE European Test Symposium (ETS'2018), May 2018, Bremen, Germany. pp.1-6, ⟨10.1109/ETS.2018.8400689⟩. ⟨hal-01922313⟩