A device for adc calibration using lms algorithm

Abstract

The invention discloses an LMS (Least Mean Squares) algorithm for calibrating ADC (Analog to Digital Converter). The LMS algorithm is characterized by comprising an ADC to be calibrated, a frequency demultiplier, a low-speed high-precision ADC, an LMS adaptive filter and a subtractor, wherein a mimic input signal vin is input to the input end of the ADC to be calibrated, and is input to the input end of the low-speed high-precision ADC after being subjected to down-conversion of the frequency demultiplier, the input end of the LMS adaptive filter is connected with the output end of the ADC to be calibrated, the output of the LMS adaptive filter is connected with one input end of the subtractor after being subjected to down-conversion of the frequency demultiplier, the other input end of the subtractor is connected with the output end of the low-speed high-precision ADC, and the output end of the subtractor is connected with the control end of the LMS adaptive filter. The LMS algorithm has the advantages of high convergence rate, high calibration precision and the like.

Description

technical field [0001] The present invention relates to an analog-to-digital converter, an adaptive filtering algorithm, digital signal processing and digital calibration technology, in particular to a device for performing ADC calibration using an LMS algorithm. The algorithm is an adaptive The least mean square algorithm (abbreviation: LMS algorithm) can be used for digital calibration of an analog-to-digital converter (abbreviation: ADC), and can well calibrate the linear and nonlinear errors of the ADC. Background technique [0002] With the development of digital signal processing technology and modern wireless communication industry, analog-to-digital converters have been developed more and more widely, and are gradually developing in the direction of high precision, high speed and low power consumption. However, when the accuracy of the ADC reaches more than 10 bits, various errors such as capacitance mismatch, input parasitic capacitance of the operational amplifier,...

AI Technical Summary

Problems solved by technology

[0004] Most of the traditional LMS algorithms used for ADC calibration use the method of updating the tap weight coefficient with a fixed step size, so that there is a big contradiction in the convergence speed and convergence accuracy: that is, when the step size is large, the convergence speed is faster, but the misalignment is relatively large. Large; when the step size is small, the misalignment is small, but the convergence speed is slow

In addition, because the traditional LMS algorithm does not add a disturbance amount, when the iteration increment is too large, it is easy to cause the tap weight coefficient to oscillate around the optimal value when updating, which slows down the speed of system convergence.

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