@Article{Lin2018,
author = {Lin, Xing and Rivenson, Yair and Yardimci, Nezih T and Veli, Muhammed and Luo, Yi and Jarrahi, Mona and Ozcan, Aydogan}, 
title = {All-optical machine learning using diffractive deep neural networks}, 
journal = {Science}, 
volume = {15}, 
number = {}, 
pages = {eaat8084}, 
year = {2018}, 
abstract = {\<p\>Deep learning has been transforming our ability to execute advanced inference tasks using computers. We introduce a physical mechanism to perform machine learning by demonstrating an all-optical Diffractive Deep Neural Network (D\<sup\>2\</sup\>NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We create 3D-printed D\<sup\>2\</sup\>NNs that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. Our 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 perform unique tasks using D\<sup\>2\</sup\>NNs.\</p\>}, 
location = {}, 
keywords = {}}

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