Title :
Improved method for SNR prediction in machine-learning-based test
Author :
Sheng, Xiaoqin ; Kerkhoff, Hans G.
Author_Institution :
CTIT-TDT Group, Univ. of Twente, Enschede, Netherlands
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.
Keywords :
analogue-digital conversion; learning (artificial intelligence); Labview model; SNR prediction; analogue-to-digital converter; machine-learning-based test; pulse-wave input signal; signal-to-noise ratio; Circuit noise; Circuit testing; Mars; Multimedia systems; RF signals; Radio frequency; Signal generators; Signal to noise ratio; System testing; Training data; ADC; SNR; double-ADC; machine-learning-based; pulse wave; test;
Conference_Titel :
Mixed-Signals, Sensors and Systems Test Workshop (IMS3TW), 2010 IEEE 16th International
Conference_Location :
La Grande Motte
Print_ISBN :
978-1-4244-7792-0
Electronic_ISBN :
978-1-4244-7791-3
DOI :
10.1109/IMS3TW.2010.5503007
