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.

International audienceThis paper presents the application of the state space approach to analyze stability and robustness of multiloop linear low dropout (LDO) regulators. Because of the increasing complexity of the LDO architecture, the stability study consisting of an open-loop ac analysis is more and more difficult to apply. In this paper, we demonstrate how a state matrix decomposition of a system allows the stability analysis in closed loop to be performed where the open- loop ac analysis failed. Based on this technique, a methodology of design, a time response criterion, and a Monte Carlo analysis are proposed. The efficiency of this approach is illustrated comparing the classical open-loop ac study with the state matrix decomposition analysis of a complex innovative architecture LDO. The results are verified experimentally

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