Xing Hua Lin

US Patent:

20220366253, Nov 17, 2022

Filed:

Jun 17, 2022

Appl. No.:

17/843720

Inventors:

- Oakland CA, US
Yair Rivenson - Los Angeles CA, US
Xing Lin - Los Angeles CA, US
Deniz Mengu - Los Angeles CA, US
Yi Luo - Los Angeles CA, US

Assignee:

THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA

International Classification:

G06N 3/08
G02B 5/18
G02B 27/42
G06K 9/62
G06N 3/04
G06V 10/94
G06V 10/147

Abstract:

An all-optical Diffractive Deep Neural Network (DNN) 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 D-printed DNNs 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 DNNs. In alternative embodiments, the all-optical DNN is used as a front-end in conjunction with a trained, digital neural network back-end.