Multispectral Image Sensor Filter Optimization via Machine Learning
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Solution Overview
Problem
Existing multispectral image sensors face challenges in achieving real-time performance due to inconsistent spectrum acquisition times, affecting imaging precision and efficiency, particularly in filter switching and push-broom methods.
Innovation Solution
A manufacturing method for multispectral image sensors involves obtaining feature parameter sets for filters corresponding to different wavelengths, inputting these into a feature vector conversion model, and selecting the method with the highest detection accuracy to optimize filter arrangement and manufacturing process, enabling simultaneous acquisition of multiple spectrums during imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If filter switching method is used to manufacture multispectral image sensor, then different spectral filters can be implemented, but real-time performance deteriorates because different spectrums are acquired at different moments
Solution Approach 1:
The sensor is divided into multiple pixel units, each equipped with different spectral filters (e.g., red, green, blue, infrared). This segmentation allows different spectral bands to be captured simultaneously by different pixels rather than sequentially switching filters, thereby maintaining real-time performance while achieving spectral diversity.
Solution Approach 2:
The patent transitions from temporal multiplexing (switching filters over time) to spatial multiplexing (arranging different filters across different spatial locations/pixels). This dimensional change from time to space enables simultaneous acquisition of multiple spectra without temporal delays.
2Adaptability or versatility
If push-broom method is used, then spectral imaging capability is achieved, but imaging precision deteriorates due to inconsistent spectrum acquisition times
Solution Approach 1:
The imaging sensor is segmented into multiple pixel units with different spectral filters, allowing simultaneous capture of multiple spectral bands at the same moment. This eliminates the temporal inconsistency inherent in push-broom methods where spectra are acquired sequentially, thereby improving imaging precision while maintaining spectral imaging capability.
3Measurement precision
If filter arrangement is optimized for detection accuracy, then detection precision improves, but device complexity increases due to multiple manufacturing method evaluations
Solution Approach 1:
The patent employs machine learning models to evaluate and optimize filter arrangement parameters (such as filter types, positions, and configurations) by calculating detection accuracy for different arrangements. This automated parameter optimization achieves high detection precision while managing manufacturing complexity through computational methods rather than manual trial-and-error.
Solution Approach 2:
The patent uses machine learning models and simulations to create virtual copies of different filter arrangements and evaluate their performance before actual manufacturing. This allows optimization of detection accuracy through computational modeling, reducing the need for multiple physical prototypes and complex manufacturing iterations.
Data Source
AI summary
A method for manufacturing an image sensor, includes: obtaining feature parameter sets of filters corresponding to different wavelengths in a filter unit set, where the image sensor includes at least one filter unit set, the image sensor is manufactured in one of candidate manufacturing methods, and the feature parameter sets correspond to the candidate manufacturing methods; inputting the feature parameter sets of the filters and a preset parameter set into a feature vector conversion model, to obtain feature vectors based on detection targets; inputting the feature vectors into a detection model corresponding to an application scenario type associated with the image sensor, and calculating a corresponding detection accuracy; and selecting a feature parameter set of a filter unit set corresponding to a highest detection accuracy corresponding to the candidate manufacturing methods, and using a candidate manufacturing method corresponding to the selected feature parameter set as an initial manufacturing method of the image sensor.