A Nonintrusive Machine Learning-Based Test Methodology for Millimeter-Wave Integrated Circuits

F. Cilici (1) , Manuel J. Barragan (1) , Estelle Lauga-Larroze (2) , Sylvain Bourdel (2) , G. Leger (3) , L. Vincent (4) , Salvador Mir (1)

1 TIMA-RMS - Reliable RF and Mixed-signal Systems
2 RFIC-Lab - Laboratoire de Radio-Fréquence et d'Intégration de Circuits
3 IMSE-CNM - Instituto de Microelectrónica de Sevilla
4 CIME - Centre Interuniversitaire de Micro-Electronique

In this article, we leverage the power of machine learning algorithms to propose a test methodology for millimeter-wave (mm-wave) integrated circuits. The proposed test strategy is based on identifying the main process degradation mechanisms in a particular device under test (DUT) and then designing dedicated process monitor circuits to characterize this degradation and infer the DUT performance. The resulting process monitors do not load or couple to any of the DUT nodes, and the methodology can be adapted to any mm-wave device without complex codesign. The proposed test methodology is illustrated on a set of 21 fabricated samples of a 65-GHz PA designed in STMicroelectronics 55-nm CMOS technology.

Mots clés: Index Terms-mm-wave IC test; machine learning-based test; non-intrusive test; process monitor circuits; BIST; Design-for- Test

Domaines: Micro et nanotechnologies/Microélectronique

F. Cilici, Manuel J. Barragan, Estelle Lauga-Larroze, Sylvain Bourdel, G. Leger, et al.. A Nonintrusive Machine Learning-Based Test Methodology for Millimeter-Wave Integrated Circuits. IEEE Transactions on Microwave Theory and Techniques, 2020, pp.1-1. ⟨10.1109/TMTT.2020.2991412⟩. ⟨hal-02899927⟩