Report
All-Optical Machine Learning Using Diffractive Deep Neural Networks
| العنوان: | All-Optical Machine Learning Using Diffractive Deep Neural Networks |
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| المؤلفون: | Lin, Xing, Rivenson, Yair, Yardimci, Nezih T., Veli, Muhammed, Jarrahi, Mona, Ozcan, Aydogan |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning, Physics - Computational Physics, Physics - Optics |
| الوصف: | 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. Comment: 20 pages, 4 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1126/science.aat8084 |
| URL الوصول: | http://arxiv.org/abs/1804.08711 |
| رقم الانضمام: | edsarx.1804.08711 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1126/science.aat8084 |
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