Improved method for SNR prediction in machine-learning-based test

Xiaoqin Sheng, Hans G. Kerkhoff

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    1 Citation (Scopus)
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    Abstract

    This paper applies an improved method for testing the signal-to-noise ratio (SNR) of Analogue-to-Digital Converters (ADC). In previous work, a noisy and nonlinear pulse signal is exploited as the input stimulus to obtain the signature results of ADC. By applying a machine-learning-based approach, the dynamic parameters can be predicted by using the signature results. However, it can only estimate the SNR accurately within a certain range. In order to overcome this limitation, an improved method based on work is applied in this work. It is validated on the Labview model of a 12-bit 80 Ms/s pipelined ADC with a pulse- wave input signal of 3 LSB noise and 7-bit nonlinear rising and falling edges.
    Original languageUndefined
    Title of host publication16th International Mixed-Signals, Sensors and Systems Test Workshop, IMS3TW 2010
    Place of PublicationScottsdale, AZ, USA
    PublisherIEEE Computer Society
    Pages1-4
    Number of pages4
    ISBN (Print)978-1-4244-7792-0
    DOIs
    Publication statusPublished - 7 Jun 2010
    Event16th IEEE International Mixed-Signals, Sensors and Systems Test Workshop, IMS3TW 2010 - La Grande Motte, France
    Duration: 7 Jun 20109 Jun 2010
    Conference number: 16

    Workshop

    Workshop16th IEEE International Mixed-Signals, Sensors and Systems Test Workshop, IMS3TW 2010
    Abbreviated titleIMS3TW
    CountryFrance
    CityLa Grande Motte
    Period7/06/109/06/10

    Keywords

    • METIS-277473
    • ADC
    • IR-75545
    • pulse wave
    • EWI-19177
    • machine-learning-based
    • Test
    • SNR
    • double-ADC

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