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9 September 2019 Diffractive optical neural networks designed by deep learning (Conference Presentation)
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Abstract
We introduce a physical mechanism to perform machine learning by demonstrating a Diffractive Deep Neural Network architecture that can all-optically implement various functions following the deep learning-based design of passive layers that work collectively. We created 3D-printed diffractive networks that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. This passive diffractive network 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 diffractive networks.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Aydogan Ozcan "Diffractive optical neural networks designed by deep learning (Conference Presentation)", Proc. SPIE 11080, Metamaterials, Metadevices, and Metasystems 2019, 110802J (9 September 2019); https://doi.org/10.1117/12.2525245
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