Compensation of Converter System Based on Neural Network

In this paper, a digital processor is presented for full calibration of pipeline ADCs. The main idea is to find an inverse model of ADC errors by using small number of the measured codes. This approach does not change internal parts of the ADC and most known errors are compensated simultaneously by digital post-processing of the output bits. Some function approximation algorithms are tested and their performances are evaluated. To verify the algorithms, a 12-bit pipelined ADC based on 1.5-bit per stage architecture is simulated with 1%-2% non-ideal factors in the SIMULINK with a 20 MHz sinusoidal input and a 100 MS/s sampling frequency. The selected algorithm has been implemented on a Virtex-4 LX25 FPGA from Xilinx. The designed processor improves the SNDR from 45 to 69 dB and increases the SFDR from 45.5 to 90 dB. The calibration processor also improves the integral nonlinearity of the ADC.

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“…Neural networks have been used for ADC calibration in [9,10]. In [9], a self-calibrating ADC employing a T-Model neural network is described.…”

Section: Neural Networkmentioning

confidence: 99%

“…In [9], a self-calibrating ADC employing a T-Model neural network is described. Errors of the converter are corrected by a simple error back propagation algorithm.…”

Section: Neural Networkmentioning

confidence: 99%