All-optical machine learning using diffractive deep neural networks

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Presentation transcript:

All-optical machine learning using diffractive deep neural networks Xing Lin1,2,3*, Yair Rivenson1,2,3*, Nezih T. Yardimci1,3, Muhammed Veli1,2,3, Yi Luo1,2,3, Mona Jarrahi1,3, Aydogan Ozcan1,2,3,4† 1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. 2Bioengineering Department, University of California, Los Angeles, CA, 90095, USA. 3California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA. 4Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA. *These authors contributed equally to this work. †Corresponding author. Email: ozcan@ucla.edu http://innovate.ee.ucla.edu

Contribution We introduce an all-optical Diffractive Deep Neural Network (D2NN) Deep learning-based design of passive diffractive layers. 3D-printed D2NNs that implement classification of images and an imaging lens. Perform at the speed of light Various complex functions that computer-based neural networks can implement

Fig. 1 Diffractive Deep Neural Networks (D2NN).

Architecture

Thickness of optic material Neuron parameters phase Thickness of optic material Intensity Transmission index

Optical layer 3D model reconstruction of a D2NN layer for 3D-printing.

Classification Handwritten digits

Classification--3D-printed 𝐷 2 𝑁𝑁 Handwritten digits fashion products

Classification--3D-printed 𝐷 2 𝑁𝑁 Handwritten digits using 50 different handwritten digits selected among the images that numerical testing was successful.

Imaging 𝐷 2 𝑁𝑁 with 𝐷 2 𝑁𝑁 without 𝐷 2 𝑁𝑁

Imaging 𝐷 2 𝑁𝑁 with 𝐷 2 𝑁𝑁 without 𝐷 2 𝑁𝑁

Imaging 𝐷 2 𝑁𝑁 Imaging lens

Imaging 𝐷 2 𝑁𝑁 Imaging lens

3D-printed Imaging 𝐷 2 𝑁𝑁 Imaging lens

Classification Performance Handwritten digits Fashion products State-of-the-art CNN 99.60%-99.77% 96.7% Phase-only 𝐷 2 𝑁𝑁 5 layers 91.57% 81.3% Complex-valued 𝐷 2 𝑁𝑁 5 layers -- 86.33% Phase-only 𝐷 2 𝑁𝑁 7 layers 93.99% Complex-valued 𝐷 2 𝑁𝑁 10 layers 86.60% 3D-printed Phase-only 𝐷 2 𝑁𝑁 88% match with numerical testing 90% match with numerical testing

Multilayer optical learning networks