Devices and methods employing optical-based machine learning using diffractive deep neural networks
US12086717B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 12, 2023 |
| Grant date | Sep 10, 2024 |
| Priority date | — |
| Expiry date | May 12, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG03H2240/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An all-optical Diffractive Deep Neural Network (D2NN) architecture learns to implement various functions or tasks after deep learning-based design of the passive diffractive or reflective substrate layers that work collectively to perform the desired function or task. This architecture was successfully confirmed experimentally by creating 3D-printed D2NNs that learned to implement handwritten classifications and lens function at the terahertz spectrum. This all-optical deep learning framework can perform, at the speed of light, various complex functions and tasks that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs. In alternative embodiments, the all-optical D2NN is used as a front-end in conjunction with a trained, digital neural network back-end.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.