Machine Learning Spectral Resolution Enhancement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional spectrometers face a tradeoff between spectral resolution and throughput, where achieving higher resolution typically sacrifices spectral range and throughput, making it difficult to resolve finer details without increasing costs or using more complex designs.
Innovation Solution
The use of deep-learning techniques to generate high-resolution spectral data from low-resolution data collected by conventional spectrometers, by training a machine-learning model on paired low and high-resolution data sets to predict finer details without the need for higher resolution hardware, allowing for extended wavelength range and high light throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a narrower slit is used to improve spectral resolution, then resolution is improved, but light throughput is reduced
Solution Approach 1:
The patent replaces the mechanical slit aperture system with a machine learning-based image processing system. Instead of physically narrowing the slit to improve resolution, the system uses a trained neural network to enhance spectral resolution from images captured with a wider, fixed slit. This substitution of mechanical adjustment with computational processing resolves the contradiction by achieving high resolution without sacrificing light throughput.
Solution Approach 2:
The patent creates a computational copy or representation of high-resolution spectral data from low-resolution input images. The machine learning model learns to reconstruct and enhance spectral features from blurred input images, generating virtual high-resolution spectra that would otherwise require physical high-resolution hardware. This copying approach allows the system to achieve high resolution without the physical constraints that reduce throughput.
2Measurement precision
If a higher resolution spectrometer is used to resolve finer spectral details, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex high-resolution optical mechanical systems with a simpler fixed-aperture spectrometer combined with machine learning processing. Instead of designing and building complex optical paths, gratings, and slit mechanisms to achieve high resolution, the system uses a simple fixed slit and computational algorithms to achieve the same measurement precision, thereby reducing device complexity.
Solution Approach 2:
The patent employs a relatively simple, inexpensive fixed-aperture spectrometer that can be easily manufactured and replaced, rather than investing in expensive, complex high-resolution spectrometers. The machine learning model acts as a software component that compensates for the hardware simplicity, providing high resolution at lower cost and reduced complexity.
3Measurement precision
If physical spectrometer design parameters are changed to improve resolution, then measurement precision is improved, but throughput and spectral range are compromised
Solution Approach 1:
The patent substitutes physical optical design changes with machine learning-based signal processing. Instead of modifying optical components to achieve high resolution while maintaining versatility, the system uses a fixed optical system with a trained neural network that can process spectra across broad ranges and maintain high resolution through computational enhancement, thereby preserving adaptability and throughput.
Solution Approach 2:
The patent changes the fundamental parameter of how resolution is achieved from physical optical parameters (slit width, grating dispersion) to computational parameters (machine learning model trained on spectral characteristics). This parameter transformation allows the system to maintain spectral range and throughput while achieving high resolution through software-based enhancement rather than hardware constraints.
Data Source
AI summary
A data set is stored in memory circuitry that is indicative of a state of a semiconductor fabrication process or of semiconductor structure fabricated thereby. Features in the data set are discernable to an extent limited by a data resolution. A machine-learning model comprising parameters having respective values assigned thereto as constrained by a model training process is also stored in the memory circuitry. Processor circuitry communicatively coupled to the memory circuitry generates an output data set from the data set in accordance with the machine-learning model such that features in the output data set are discernable to an extent limited by an output data resolution that is finer than the data resolution of the data set.