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On the limits of machine learning-based test: a calibrated mixed-signal system case study

Author(s): M. Barragan, G. Leger, A. Gines, E. Peralias, A. Rueda

Doc. Source: Design Automation and Test in Europe (DATE 2017)

Publisher: IEEE

Doi : 10.23919/DATE.2017.7926962

Testing analog, mixed-signal and RF circuits rep- resents the main cost component for testing complex SoCs. A promising solution to alleviate this cost is the machine learning- based test strategy. These test techniques are an indirect test approach that replaces costly specification measurements by simpler signatures. Machine learning algorithms are used to map these signatures to the performance parameters. Although this approach has a number of undoubtable advantages, it also opens new issues that have to be addressed before it can be widely adopted by the industry. In this paper we present a machine learning-based test for a complex mixed-signal system –i.e. a state-of-the-art pipeline ADC– that includes digital calibration. This paper shows how the introduction of digital calibration for the ADC has a serious impact in the proposed test as calibration completely decorrelates signatures from the target specification in the presence of local mismatch.