Method for predicting and quantifying binary charge redistribution type successive approximation analog-to-digital converter

Abstract

The invention relates to a method for predicting and quantifying a binary charge redistribution type successive approximation analog-to-digital converter, and belongs to the technical field of analog-to-digital conversion. According to the method provided by the invention, a high P bit code of a previous quantification result is directly loaded in the quantification result of the present high P bit based on a common mode voltage reset structure capacitor array DAC, a prediction result is judged according to twice switching results before and after the switching of a redundant capacitor, the comparison of the residual low bits can be carried out in the case of prediction correctness, the capacitance of a lower polar plate of the high P bit is reset in the case of a prediction error, and the traditional quantification from the most significant bit to the least significant bit (MSB_First) is carried out again. By adoption of the method provided by the invention, the switching frequency of a digital-to-analog converter module and the comparison times of a comparator are maximally reduced, the average quantification power consumption of the low and medium frequency portions of signals are greatly reduced, and a low power consumption design is realized; and the method provided by the invention can be used as a feature parameter extraction means of electrocardiogram (ECG), electroencephalogram (EEG) and other biological medical signals so as to extract extreme values and frequency characteristics of target signals under extremely low power consumption.

Description

technical field [0001] The invention belongs to the technical field of analog-to-digital conversion, and in particular relates to an analog-to-digital converter prediction technology and its application in extracting characteristic parameters of biomedical signals. Background technique [0002] Analog signals in real life often have the characteristics of long-term small fluctuations, periodic changes, different bands, peak points and valley points, such as bioelectrical signals, sensor detection signals, etc. In the frequency spectrum, the mid-low frequency components of such signals are often very large. The MSB-First algorithm of the traditional successive approximation analog-to-digital converter first compares, then gives the prediction interval, and continuously narrows the range of the prediction interval to obtain the final quantization result. Its disadvantage is that even if the signal amplitude of the two samples before and after the change is very small, each qu...

AI Technical Summary

Problems solved by technology

But it has a defect that the quantization power consumption of the higher frequency part of the signal is large, and the maximum number of comparisons can reach 2N+1 times, which limits its speed, and the power consumption is significantly increased, where N is the bit of the analog-to-digital converter number

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