ADC Compensation Using Machine Learning Neural Network
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Solution Overview
Problem
Analog-to-digital converters (ADCs) suffer from errors due to non-linearity, parasitics, and component mismatches, leading to reduced signal-to-noise and distortion ratio (SNDR) performance, which is challenging to correct using traditional calibration methods that require extensive mathematical analysis and additional hardware, increasing complexity and cost.
Innovation Solution
A machine learning system, specifically a neural network, is trained to compensate for ADC errors by learning from a reference ADC, allowing it to directly correct digital outputs without the need for additional analog circuits, thereby reducing hardware complexity and improving ADC performance without increasing power or area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to correct ADC errors, then ADC performance can be improved, but hardware complexity and cost increase due to additional circuits and mathematical analysis requirements
Solution Approach 1:
The patent replaces traditional hardware-based calibration circuits with a software-based machine learning model that runs on the ADC's processor. The neural network model compensates for non-linearity errors through computational algorithms rather than additional analog or digital circuits, thereby maintaining accuracy while reducing hardware complexity.
Solution Approach 2:
The patent creates a digital copy or model of the ADC's error characteristics through machine learning training. The trained model replicates the error patterns and enables compensation without requiring physical calibration circuits, thus improving accuracy without increasing hardware complexity.
2Manufacturing precision
If layout area and power are increased to correct ADC errors, then manufacturing precision can be improved, but device area and power consumption increase
Solution Approach 1:
The patent substitutes physical layout optimizations with a software-based machine learning compensation model. Instead of increasing layout area to improve component matching and linearity, the neural network model corrects non-linearity errors computationally, achieving the same manufacturing precision goal without additional area.
Solution Approach 2:
The patent changes the approach from physical parameter optimization (layout geometry, component sizing) to software parameter optimization (neural network weights, model architecture). This allows achieving improved linearity and manufacturing precision without increasing the physical layout area.
3Measurement precision
If multiple calibration methods are combined to correct different ADC errors, then measurement precision improves, but device complexity increases due to convergence issues and mathematical analysis
Solution Approach 1:
The patent merges multiple calibration functions into a single unified machine learning model. The neural network simultaneously handles different types of errors (non-linearity, offset, gain) that would traditionally require separate calibration loops, eliminating convergence issues and reducing the overall complexity of the calibration system.
Solution Approach 2:
The patent creates a comprehensive digital model of all ADC error sources within a single machine learning framework. This unified model replaces multiple separate calibration algorithms and their associated complexity, achieving high measurement precision through one integrated compensation system rather than multiple interacting calibration loops.
Data Source
AI summary
Analog to digital conversion errors caused by non-linearities or other sources of distortion in an analog-to-digital converter are compensated for by use of a machine learning system, such as a neural network. The machine learning system is trained based on simulation or measurement data, which may utilize a reference ADC or a digital training signal representing a reference ADC that has less distortion errors than the analog-to-digital converter. The effect on the analog to digital conversion errors by Process-Voltage-Temperature parameters may be incorporated into the training of the machine learning system.