Machine-Learning ADC Calibration for Nonlinear Analog Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing analog-to-digital conversion technologies require idealized analog circuitry and rigid digital interfaces, making them impractical or impossible to build and limiting their applicability due to complex and nonlinear behavior of analog circuits, which constrains their use to well-understood environments.
Innovation Solution
A machine-learning-enabled approach that includes a physical parallel array analog-to-digital converter (PA ADC) with a machine-learning unit for calibration, using techniques such as generative adversarial networks and neural networks to map digital features into a digital representation of the analog input, allowing for flexible and adaptive signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If idealized analog circuitry is used to implement mathematical equations for analog-to-digital conversion, then measurement precision is improved, but device complexity and ease of manufacture worsen due to impractical or impossible construction requirements
Solution Approach 1:
The patent uses a trained neural network model (a digital copy of the ideal conversion function) to replicate the behavior of idealized analog circuitry. The neural network, trained on data from ideal mathematical models, copies their precise conversion characteristics while being implementable with practical, non-ideal analog components and digital processing.
Solution Approach 2:
The patent replaces complex analog circuit implementations with a hybrid approach: simple analog front-end circuitry followed by digital neural network processing. This substitutes the need for complex analog mathematics implementation with digital computation that can achieve the same measurement precision without requiring impractical circuit designs.
2Ease of manufacture
If analog circuits are designed to be simple enough to model, then ease of manufacture is improved, but adaptability worsens due to constraints in handling complex and nonlinear behavior
Solution Approach 1:
The patent makes the system adaptive through a trained neural network that can dynamically adjust to different input signal characteristics. The neural network model, once trained, can handle complex and nonlinear analog signals that would be impossible to model with simple analog circuits, while the physical analog front-end remains simple and manufacturable.
Solution Approach 2:
The patent creates a universal solution where a single trained neural network model can process various types of analog signals (different frequencies, amplitudes, and waveforms) that would require different specialized analog circuit designs. The simple analog front-end combined with the versatile neural network achieves multi-functionality without increasing manufacturing complexity.
3Measurement precision
If exhaustive modeling and characterization of analog circuits is performed, then measurement precision is improved, but loss of time and productivity worsen during system development and calibration
Solution Approach 1:
The patent performs the exhaustive modeling and characterization work in advance during the neural network training phase. By pre-training the neural network on comprehensive data from ideal mathematical models and simulated circuit behavior, the system achieves high measurement precision without requiring time-consuming calibration when deployed. The complex analysis work is done once during training, not repeatedly during operation.
Solution Approach 2:
The trained neural network becomes a self-sufficient model that automatically compensates for non-idealities in the physical analog circuitry. Once trained, the neural network self-corrects for deviations from ideal behavior without requiring external calibration or adjustment, eliminating the need for time-consuming field calibration procedures.
Data Source
AI summary
A method for calibrating a machine-learning unit includes generating an analog calibration signal from an input sequence; generating a digital calibration signal by taking digital samples representing the value of the analog calibration signal at a predetermined sample rate; and applying the analog calibration signal as an input to a physical parallel array analog-to-digital converter (PA ADC) to produce a digital response. The method further includes producing an output by the machine-learning unit, at least in part based on the digital response; modifying a parameter of the machine-learning unit to reduce an error between the output and the digital calibration signal; and determining that the error is not less than a predetermined threshold.