Background Calibration of Random Chopper Non-Idealities in ADCs
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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 techniques are used in data converters, then offset errors and even order harmonics are calibrated, but non-idealities in chopper circuits cause noise floor degradation
Solution Approach 1:
The patent applies preliminary calibration action by injecting a calibration signal through the random chopper before normal operation, measuring the non-idealities (offset and gain errors), and storing correction values. This preliminary measurement and correction process eliminates the harmful noise floor degradation caused by chopper non-idealities while preserving the calibration benefits.
2Measurement precision
If chopper circuits are used to calibrate offset errors, then offset calibration is achieved, but non-idealities cause errors in calibration
Solution Approach 1:
The patent implements feedback by measuring the actual output of the random chopper during calibration, comparing it with expected values, and using the difference (error) to update correction values. This closed-loop feedback process compensates for chopper non-idealities and ensures both accurate offset calibration and reliable calibration results.
3Object-generated harmful factors
If random chopping is applied to data converters, then even order harmonics are reduced, but non-idealities limit the effectiveness of chopping
Solution Approach 1:
The patent changes the parameters of the calibration process by injecting a known calibration signal with specific amplitude and frequency characteristics through the random chopper. By measuring the actual response and comparing it with the known input, the system characterizes the chopper's non-idealities and applies correction values, thereby maintaining chopping effectiveness despite non-idealities.
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.