Time-Interleaved ADC Correction Using Per-Channel Error Tagging
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Solution Overview
Problem
Time-interleaved analog-to-digital converters (TI ADCs) suffer from gain and offset errors due to variations in individual ADC units, leading to noise and distortion in the output signal, which conventional methods struggle to effectively correct without increasing cost and complexity.
Innovation Solution
Implementing a machine learning system, such as a neural network, to analyze and correct ADC unit-specific errors by identifying the responsible ADC unit and applying customized correction schemes, trained using simulation or measurement data and external timing sources, incorporating process-voltage-temperature (PVT) parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple ADC units are operated in parallel to increase effective sampling rate, then productivity is improved, but device complexity increases due to need for error correction
Solution Approach 1:
An error correction system acts as an intermediary between the parallel ADC units and the final output. This system receives digital output signals from multiple ADC units, identifies which ADC unit produced each sample using ADC unit identifiers, and applies appropriate error correction schemes to compensate for gain and offset variations. This intermediary layer enables the use of multiple ADC units without directly propagating their individual errors to the output.
Solution Approach 2:
The error correction system implements feedback by continuously monitoring the output of each ADC unit and applying corrective adjustments based on identified error patterns. The system uses ADC unit identifiers to trace each output sample back to its source ADC unit and applies feedback correction specific to that unit's characteristics, thereby compensating for manufacturing variations and maintaining high sampling rates.
2Measurement precision
If conventional error correction methods are applied to correct gain and offset errors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The error correction approach is segmented by ADC unit, with each ADC unit having its own identifier and associated correction scheme. Rather than applying a single global correction, the system divides the correction task into discrete segments corresponding to each ADC unit's specific error characteristics. This segmentation enables precise correction of gain and offset errors for each unit while maintaining organizational simplicity through modular error correction tables.
3Measurement precision
If machine learning system is implemented to correct ADC errors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The machine learning system corrects ADC errors by dynamically changing correction parameters based on the identified ADC unit and its error characteristics. Rather than using fixed correction values, the system adjusts gain and offset parameters adaptively for each ADC unit, enabling high measurement precision. The parameters are stored in lookup tables and applied based on ADC unit identifiers, maintaining implementation simplicity.
4Measurement precision
If ADC unit identifiers are tracked and applied with correction schemes, then measurement precision is improved, but loss of time occurs in identifying and correcting errors
Solution Approach 1:
ADC unit identifiers are attached to each digital output sample at the source, before any error correction processing is needed. This preliminary tagging action enables the error correction system to immediately identify the source ADC unit without requiring complex tracing or analysis during the correction phase. The identifiers are generated in advance by the ADC unit itself or its control logic, eliminating identification delays.
Solution Approach 2:
The error correction system uses copying by creating and maintaining lookup tables that store pre-computed correction schemes for each ADC unit. Rather than calculating corrections in real-time, the system copies appropriate correction parameters from these tables based on ADC unit identifiers. This copying approach significantly reduces correction time while maintaining measurement precision.
Data Source
AI summary
A system includes a time-interleaved analog-to-digital converter (TI ADC). The TI ADC includes a plurality of ADC units connected in parallel between an input terminal and an output terminal of the time-interleaved ADC. An ADC tag generator is configured to output a first identifier of a first ADC unit of the plurality of ADC when the first ADC unit is enabled. An error correction system is configured to receive a first digital signal from the output terminal of the time-interleaved ADC, receive the first identifier of the first ADC unit from the ADC tag generator, modify the first digital signal to generate a first corrected digital signal by compensating for first analog-to-digital conversion errors occurring within the first ADC unit by applying a first error correction scheme that is associated with the first ADC unit, and output the first corrected digital signal.