公开(公告)号：US20220366253A1

公开(公告)日：2022-11-17

申请号：US17843720

申请日：2022-06-17

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yair Rivenson ,  Xing Lin ,  Deniz Mengu ,  Yi Luo

IPC: G06N3/08 ,  G02B5/18 ,  G02B27/42 ,  G06K9/62 ,  G06N3/04 ,  G06V10/94 ,  G06V10/147

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.

公开(公告)号：US20220253685A1

公开(公告)日：2022-08-11

申请号：US17629346

申请日：2020-09-09

Applicant: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA

Inventor： Aydogan Ozcan ,  Yi Luo ,  Deniz Mengu ,  Yair Rivenson

IPC: G06N3/067 ,  G06N3/08

Abstract: A broadband diffractive optical neural network simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to all-optically perform a specific task learned using network learning. The optical neural network design was verified by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tunable, single passband as well as dual passband spectral filters, and (2) spatially-controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep learning-based design, broadband diffractive optical neural networks help engineer light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning. The optical neural network may be implemented as a reflective optical neural network.