Neural Network Correction for Sigma-Delta ADC Linearity
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Solution Overview
Problem
Existing analog-to-digital converters (ADCs), particularly sigma-delta ADCs, face challenges in achieving high linearity and accuracy due to non-linearities and errors introduced during design, layout, and manufacturing, which require complex calibration processes and increased hardware, leading to reduced signal-to-noise and distortion ratio (SNDR) performance.
Innovation Solution
The use of neural networks to compensate for ADC errors by learning and correcting distortions without the need for additional analog circuits, utilizing digital hardware and process information to reduce hardware complexity and improve linearity, while avoiding the introduction of parasitic errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to correct ADC errors, then linearity and accuracy are improved, but hardware complexity and area increase
Solution Approach 1:
The patent replaces traditional hardware-based calibration circuits and mechanical adjustment mechanisms with a neural network implemented in software/digital logic. The neural network processes ADC output signals to correct non-linearities and errors, eliminating the need for additional analog calibration circuits, resistors, capacitors, and other hardware components that would increase area and complexity.
Solution Approach 2:
The patent changes the approach from hardware parameter adjustment (physical circuit modifications) to digital parameter processing (neural network weight adjustments). By using a trained neural network with optimized weights and biases, the system achieves calibration without modifying hardware parameters, thereby reducing hardware complexity while maintaining measurement precision.
2Measurement precision
If multiple calibration methods are combined to correct different errors, then measurement precision is improved, but device complexity and convergence issues increase
Solution Approach 1:
The patent merges multiple calibration functions into a single unified neural network model. Instead of implementing separate calibration loops for different error types (offset, gain, non-linearity, differential non-linearity), the neural network integrates all these correction functions into one cohesive system that processes the input signal and outputs corrected values, thereby reducing complexity and eliminating convergence issues between multiple loops.
Solution Approach 2:
The neural network serves multiple calibration functions simultaneously - it corrects offset errors, gain errors, non-linearities, and differential non-linearities all in one processing stage. This multi-functional approach replaces what would traditionally require multiple specialized calibration circuits, reducing overall device complexity while maintaining comprehensive error correction capability.
3Manufacturing precision
If layout area and power are increased to correct errors, then manufacturing precision is improved, but power consumption and area increase
Solution Approach 1:
The patent creates a digital copy of the error characteristics through the neural network training process. During training, the neural network learns the error patterns of the ADC and creates an internal model (weights and biases) that replicates the inverse of these errors. This digital copy allows the system to correct manufacturing imperfections without adding physical compensation circuits that would consume additional power or occupy more area.
4Measurement precision
If design optimization time is increased to reduce errors, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent performs error correction in advance through the neural network training phase. During the training stage (which can be done offline), the neural network learns and stores the optimal correction parameters. Once trained, the network requires only simple inference operations during actual ADC operation, dramatically reducing the time needed for real-time correction and accelerating the overall design and deployment cycle.
Data Source
AI summary
Systems and methods for correction of sigma-delta analog-to-digital converters (ADCs) using neural networks are described. In an illustrative, non-limiting embodiment, a device may include: an ADC; a filter coupled to the ADC, where the filter is configured to receive an output from the ADC and to produce a filtered output; and a neural network coupled to the filter, where the neural network is configured to receive the filtered output and to produce a corrected output.