
doi: 10.1109/ats.2010.56
In the conventional ADC production test method, a high-quality analogue sine wave is applied to the Analogue-to-Digital Converter (ADC), which is expensive to generate. Nowadays, an increasing number of ADCs are integrated into a system-on-chip (SoC) platform design, which usually contains a digital embedded processor. In such a platform, a digital pulse wave is obviously less expensive to generate than an accurate analogue sine wave. As a result, the usage of a digital pulse wave has been investigated to test ADCs as the test stimulus. In this paper, the ability of a digital adaptive pulse wave for ADC testing is presented via the measurement results. Instead of the conventional FFT analysis, a time-domain analysis is exploited for post-processing, from which a signature result can be obtained. This signature can distinguish between faulty devices and the fault-free devices. It is also used in the machine-learning-based test method to predict the dynamic specifications of the ADC. The experimental results of a 12-bit 80 M/s pipelined ADC are shown to evaluate the sensitivity and accuracy of using a pulse wave to test an ADC.
METIS-277474, Measurement, EWI-19178, Test, ADC, IR-75343, machine-learning-based, pulse wave, signature
METIS-277474, Measurement, EWI-19178, Test, ADC, IR-75343, machine-learning-based, pulse wave, signature
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