Machine-Learning ADC Calibration for Non-Ideal Analog Circuits
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Solution Overview
Problem
Existing analog-to-digital conversion technologies face challenges in practical implementation due to the need for idealized analog circuitry and rigid digital interfaces, which are often impractical or impossible to build.
Innovation Solution
A method for calibrating a machine-learning unit that involves generating an analog calibration signal, converting it to a digital signal, and applying it to a physical parallel array analog-to-digital converter (PA ADC) to produce a digital response, which is then used to modify the machine-learning unit's parameters to reduce errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If idealized analog circuitry is used to achieve accurate analog-to-digital conversion, then measurement precision is improved, but ease of manufacture deteriorates because such circuitry is impractical or impossible to build
Solution Approach 1:
The patent creates a virtual copy of the ideal analog-to-digital converter using software simulation. The virtual ADC replicates the functionality of ideal circuitry through computational models, allowing accurate conversion without requiring physically impossible analog components. The virtual converter is trained to match the behavior of ideal circuitry while being implementable in software.
Solution Approach 2:
The patent replaces the mechanical/analog circuit system with a software-based system. Instead of using physical analog circuitry that cannot be built, the invention uses digital signal processing and machine learning algorithms to perform the analog-to-digital conversion function, substituting software for hardware that is impossible to manufacture.
2Reliability
If rigid digital interfaces are used to maintain system stability, then reliability is improved, but adaptability deteriorates because the interfaces cannot accommodate real-world signal variations
Solution Approach 1:
The patent introduces dynamic adaptability through machine learning models that can adjust their parameters and behavior based on the characteristics of incoming signals. The virtual ADC adapts to different signal types, noise levels, and operating conditions while maintaining stable and reliable conversion performance. This dynamic adjustment allows the system to handle real-world signal variations without sacrificing stability.
Solution Approach 2:
The patent changes the parameters of the conversion system based on input signal characteristics. The virtual ADC modifies its internal parameters, such as quantization levels, filtering characteristics, and reconstruction algorithms, to optimize performance for different signal conditions. This parameter adaptation enables the system to maintain reliability across varying operational environments.
3Manufacturing precision
If machine-learning units are calibrated using traditional methods, then manufacturing precision is improved, but productivity deteriorates due to the complexity of calibration procedures
Solution Approach 1:
The patent enables the machine-learning unit to perform self-calibration by automatically adjusting its parameters based on training data and performance feedback. The virtual ADC calibrates itself through iterative learning processes, eliminating the need for complex external calibration procedures. This self-service capability maintains high manufacturing precision while significantly improving calibration productivity.
Solution Approach 2:
The patent implements feedback mechanisms where the calibration process uses the output of the virtual ADC to continuously refine its parameters. By comparing actual conversion results with expected values and adjusting the model accordingly, the system achieves high calibration accuracy through automated feedback loops, reducing the need for manual intervention and complex 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.