Spectral Sensor Correction Using Neural Network Calibration
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Solution Overview
Problem
Spectral sensors using interference-based filters face non-ideal conditions such as cross talk, non-linearities, and manufacturing errors, leading to inaccurate spectral representations of incident light, making correction and calibration complex and imprecise.
Innovation Solution
The integration of an artificial neural network within the spectral sensor system, utilizing a correction matrix and training with synthetic spectra to minimize errors and provide a corrected spectral output, while also considering factors like temperature and meta-data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If interference-based filters are used in spectral sensors, then controlled light wavelengths can be provided, but non-ideal conditions such as cross talk, non-linearities, and manufacturing errors occur leading to inaccurate spectral representations
Solution Approach 1:
The patent applies preliminary action by training an artificial neural network with synthetic spectral data before actual measurement. The neural network is pre-trained to recognize and correct for filter non-idealities, cross-talk, and manufacturing variations. This preliminary training enables the system to automatically compensate for these errors during actual spectral measurements without requiring manual calibration for each sensor
Solution Approach 2:
The patent introduces an artificial neural network as an intermediary between the raw sensor output and the final spectral representation. This neural network mediator processes the raw spectral data, applying learned corrections for filter non-idealities and cross-talk effects, thereby transforming inaccurate raw measurements into accurate spectral representations
2Measurement precision
If correction methods are applied to spectral sensor output, then measurement accuracy improves, but system complexity increases due to additional processing requirements
Solution Approach 1:
The patent replaces complex mechanical or manual correction systems with an artificial neural network-based computational approach. Instead of using physical calibration standards or manual adjustment mechanisms, the system uses a trained neural network that automatically performs corrections through software processing, thereby reducing mechanical complexity while maintaining high measurement precision
Solution Approach 2:
The patent changes the approach from physical/calibration-based correction parameters to learned parameters within the neural network. The neural network learns optimal correction parameters during training with synthetic data, allowing the system to adapt to different sensor characteristics and filter non-idealities without requiring complex physical adjustment mechanisms
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables precise correction of spectral outputs across various conditions, enhancing the accuracy and reliability of spectral sensor data by adaptively addressing non-idealities and complexities inherent in the sensor and filter responses.
Implementation Method 1
Interference-based filters, such as Fabry-Pérot filters, when used in conjunction with spectral sensors have been shown to be capable of providing controlled light wavelengths
Implementation Method 2
Interference-based filters, such as Fabry-Pérot filters
Data Source
AI summary
An spectral sensor system includes an array of optical sensors arranged on an integrated circuit, an interface between the plurality of optical sensors and a first processing device, a plurality of sets of optical filters configured as a layer located atop the plurality of optical sensors, with each set of optical filters including a plurality of optical filters, each optical filter configured to pass light in a different wavelength range. The spectral sensor system includes a memory configured to interface with the first processing device, the memory configured to store calibration data associated with the plurality of sets of optical sensors. The spectral sensor system further includes second processing device includes an artificial neural network configured to correct a spectral response generated by the plurality of optical sensors and an interface between the first processing device and the second processing device is configured to transmit information therebetween.