Superresolution Metrology via Singular Light and Deep Learning
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Solution Overview
Problem
Existing metrology techniques are limited by the Abbe limit, which restricts the resolution of geometrical feature measurements due to diffraction, and require post-processing application of mathematical priors and positivity constraints, reducing measurement precision.
Innovation Solution
The method involves projecting a singular light distribution onto a physical object, detecting light interacted with the geometrical feature, and using deep learning or machine learning to classify and quantify intrinsic geometrical parameters directly from the detected light, bypassing image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional imaging paradigm with post-processing is used, then measurement process is simple, but measurement precision deteriorates due to application of mathematical priors after acquisition
Solution Approach 1:
The patent embeds the mathematical prior directly into the acquisition process by designing a specialized optical system that projects singular light distributions and captures data consistent with the parametric shape model from the beginning, rather than applying the prior as a post-processing step. This preliminary integration of the shape assumption into the measurement setup enables high-precision retrieval of geometrical parameters without the degradation that occurs when priors are applied after acquisition.
2Manufacturing precision
If Abbe limit-based diffraction optics are used, then optical system is simple, but resolution deteriorates and cannot measure features below diffraction limit
Solution Approach 1:
The patent changes the fundamental parameters of the optical measurement system by using singular light distributions (such as optical vortices or conical diffraction patterns) instead of conventional Gaussian beams, and by capturing the full complex field (amplitude and phase) rather than only intensity. These parameter changes enable the system to encode geometrical information at scales below the diffraction limit, achieving super-resolution measurement of nanoscale features.
Solution Approach 2:
The patent introduces an intermediary mathematical model (parametric shape assumption) that acts as a bridge between the optical measurement data and the geometrical parameters of interest. This model embeds prior knowledge about the expected shape of nanoscale features, allowing the system to extract precise dimensional information even when the features are smaller than the diffraction limit, effectively using the model as a mediator to overcome the optical resolution barrier.
3Measurement precision
If more observations are taken to improve precision, then measurement precision improves, but time consumption increases
Solution Approach 1:
The patent performs preliminary encoding of the parametric shape model into the optical measurement process itself, so that each measurement contains maximally useful information about the geometrical parameters. By designing the acquisition to directly probe the parameters of interest through singular light distributions and embedded mathematical priors, the system achieves high precision with minimal observations, avoiding the time-consuming requirement of collecting large numbers of measurements.
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
This approach enables measurements with resolutions greater than the diffraction limit, improving precision and reducing the number of observations required, while avoiding reconstruction artifacts and enhancing computational efficiency.
Implementation Method 1
The Abbe limit arises due to diffraction by a defining aperture of the optical system used to illuminate or collect light from a sample
Implementation Method 2
an objective lens of numerical aperture NA, focused at a sample plane z=0 by a lens of focal length f
Data Source
AI summary
Methods for determining a value of an intrinsic geometrical parameter of a geometrical feature characterizing a physical object, and for classifying a scene into at least one geometrical shape, each geometrical shape modeling a luminous object. A singular light distribution characterized by a first wavelength and a position of singularity is projected onto the physical object. Light excited by the singular light distribution that has interacted with the geometrical feature and that impinges upon a detector is detected and a return energy distribution is identified and quantified at one or more positions. A deep learning or neural network layer may be employed, using the detected light as direct input of the neural network layer, adapted to classify the scene, as a plurality of shapes, static or dynamic, the shapes being part of a set of shapes predetermined or acquired by learning.