Random Chopper Calibration in Data Converters for Noise Floor Control
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Solution Overview
Problem
Random chopping techniques in data converters face limitations due to non-idealities in chopper circuits, leading to noise floor degradation and errors in calibration, which are not effectively addressed by existing methods.
Innovation Solution
The implementation of a calibration technique using correlators and adaptive filtering, specifically the Least Mean Squares (LMS) algorithm, to correct offset and gain errors in the chopper circuit, allowing for effective removal of non-idealities and improvement of chopping performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If random chopping is used in data converters, then offset errors and even-order harmonics are reduced, but non-idealities in chopper circuits cause noise floor degradation
Solution Approach 1:
The patent applies preliminary calibration action by measuring chopper non-idealities (gain errors, offset errors, random walk errors) before they degrade the noise floor during normal operation. The calibration process pre-determines correction values that are stored and applied during conversion, preventing noise degradation rather than correcting it after the fact.
Solution Approach 2:
The patent implements feedback by using the measured non-idealities from calibration to generate correction terms that are fed back into the conversion process. The system continuously monitors performance through calibration and adjusts correction values to compensate for chopper non-idealities, creating a closed-loop system that maintains low noise floor performance.
2Measurement precision
If traditional calibration methods are used, then some errors are corrected, but digital overhead and power consumption increase
Solution Approach 1:
The patent extracts only the essential calibration information needed for noise floor correction, separating it from full-system calibration. By focusing specifically on chopper non-idealities and extracting only the relevant correction terms (gain, offset, random walk), the system achieves effective calibration with minimal digital processing overhead and reduced power consumption compared to comprehensive calibration methods.
Data Source
AI summary
Random chopping is an effective technique for data converters. Random chopping can calibrate offset errors, calibrate offset mismatch in interleaved ADCs, and dither even order harmonics. However, the non-idealities of the (analog) chopper circuit can limit its effectiveness. If left uncorrected, these non-idealities cause severe degradation in the noise floor that defeats the purpose of chopping, and the non-idealities may be substantially worse than the non-idealities that chopping is meant to fix. To address the non-idealities of the random chopper, calibration techniques can be applied, using correlators and calibrations that may already be present for the data converter. Therefore, the cost and digital overhead are negligible. Calibrating the chopper circuit can make the chopping more effective, while relaxing the design constraints imposed on the analog circuitry.