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


Sheng, Xiaoqin and Kerkhoff, Hans G. (2010) Improved method for SNR prediction in machine-learning-based test. In: 16th International Mixed-Signals, Sensors and Systems Test Workshop, IMS3TW 2010, 7-9 June 2010, La Grande Motte, France (pp. pp. 1-4).

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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.
Item Type:Conference or Workshop Item
Copyright:© 2010 IEEE
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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