1. 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.

2. 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

3. Fig. 1 Diffractive Deep Neural Networks (D2NN).

4. Architecture

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

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

7. Classification
Handwritten digits

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

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

10. Imaging 𝐷 2 𝑁𝑁
with 𝐷 2 𝑁𝑁
without 𝐷 2 𝑁𝑁

11. Imaging 𝐷 2 𝑁𝑁
with 𝐷 2 𝑁𝑁
without 𝐷 2 𝑁𝑁

12. Imaging 𝐷 2 𝑁𝑁
Imaging lens

13. Imaging 𝐷 2 𝑁𝑁
Imaging lens

14. 3D-printed Imaging 𝐷 2 𝑁𝑁
Imaging lens

15. 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

16. Multilayer optical learning networks