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

Auteur(s) : F. Cilici, M. Barragan, E. Lauga-Larroze, S. Bourdel, G. Leger, L. Vincent, S. Mir

Journal : IEEE Transactions on Microwave Theory and Techniques

Pages : 1-1

Doi : 10.1109/TMTT.2020.2991412

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.