An efficient background calibration technique for analog-to-digital converters based on neural network

Abstract

This paper introduces a background digital calibration algorithm based on neural network, which can adaptively calibrate multiple non-ideal factors in a single-channel ADC, such as gain error, mismatch, offset and harmonic distortion. It enables an efficient background calibration through a simple feed forward neural network and LM gradient descent algorithm. The simulation results show that in the case of a signal input close to the Nyquist frequency, for a 14-bit 500 MS/s prototype ADC, only about 40,000 data needed, the ENOB of the ADC can be increased from 7.81 to 13.06 and the SFDR from 49.7 dB to 106.8 dB assisted by a lower speed reference ADC.