All-Optical Machine Learning Using Diffractive Deep Neural Networks
This work addresses the need for high-speed, all-optical computing for applications like image analysis and object classification, representing a novel paradigm rather than an incremental improvement.
The paper introduced an all-optical Diffractive Deep Neural Network (D2NN) architecture that tackles the problem of implementing machine learning functions optically, achieving experimental success in handwritten digit classification and lens imaging at terahertz spectrum with 3D-printed devices.
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions 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.