Neural Network ADC Compensation for Nonlinearity and PVT Drift
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Solution Overview
Problem
Analog-to-digital converters (ADCs) suffer from errors due to non-linearity and physical imperfections, leading to reduced signal-to-noise and distortion ratio (SNDR) performance, which is costly and time-consuming to correct through traditional calibration methods that require extensive mathematical analysis and additional hardware.
Innovation Solution
A machine learning system, such as a neural network, is trained to compensate for ADC errors by learning from a filtered output to produce a corrected digital signal, reducing the need for additional analog circuits and mathematical models, and incorporating process, voltage, and temperature (PVT) variations to improve ADC performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional calibration methods are used to correct ADC errors, then manufacturing precision is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/calibration-based error correction systems with a machine learning system. Instead of using complex analog circuits and calibration hardware to correct ADC non-linearity errors, the invention trains a neural network to learn the error characteristics and compensate for them digitally, thereby reducing hardware complexity while maintaining manufacturing precision
Solution Approach 2:
The patent changes the approach from fixed calibration parameters to adaptive machine learning parameters. The system uses trained neural network weights and biases that can dynamically adjust to compensate for ADC errors across different operating conditions, replacing static calibration tables with intelligent parameter adaptation
2Manufacturing precision
If traditional calibration methods are used to correct ADC errors, then manufacturing precision is improved, but loss of time increases due to tedious optimization processes
Solution Approach 1:
The patent applies preliminary action by training the machine learning system in advance during the manufacturing process. The neural network is pre-trained on a dataset that captures ADC error characteristics, so that when the ADC is deployed, the error compensation is already built-in and requires no additional calibration time during operation
Solution Approach 2:
The patent replaces time-consuming manual calibration procedures with an automated machine learning training process. The system uses algorithms to automatically learn error patterns and generate compensation parameters, eliminating the need for tedious manual optimization and significantly reducing calibration time
3Manufacturing precision
If multiple calibration methods are combined to correct different ADC errors, then manufacturing precision is improved, but device complexity increases due to convergence issues and additional hardware
Solution Approach 1:
The patent merges multiple error correction functions into a single unified machine learning model. Instead of implementing separate calibration loops for different error types (offset, gain, non-linearity), the neural network learns to compensate for all these errors simultaneously through a unified training process, thereby reducing device complexity while maintaining comprehensive error correction
Solution Approach 2:
The patent creates a universal error compensation system that handles multiple types of ADC errors with a single machine learning model. The trained neural network can compensate for offset errors, gain errors, non-linearity, and other distortion effects across different operating conditions, replacing multiple specialized calibration systems with one multi-functional solution
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 utilizes a filtered output of the analog-to-digital converter that has less distortion errors than the unfiltered output of 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.