Superresolution metrology methods based on singular distributions and deep learning
US11694453B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 30, 2018 |
| Grant date | Jul 4, 2023 |
| Priority date | — |
| Expiry date | Oct 19, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/82
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.