Advanced Photonics Nexus, Volume. 3, Issue 2, 026007(2024)
Multimode diffractive optical neural network
Fig. 1. Multimode DONN and EAM. (a) Multimode DONN. As an example, the width of the multimode waveguide is
Fig. 2. Training process and application demonstration of the multimode DONN composed of the structures in the library. When the task is defined, the training data are loaded into a variety of the potential multimode DONN structures composed of the input, output, and metalines in the library, as shown by the dotted lines. The performance of each potential multimode DONN is evaluated using the port-to-port transmission matrix and the best one is selected. Live or test data will be loaded in.
Fig. 3. The classification task of the Iris plants dataset. (a) Multimode DONN structure. The category corresponding to the output port receiving the highest power is judged as a classification result. PD, photodetector. (b) A set of Setosa class data is simulated by var-FDTD. (c) The confusion matrix of the test dataset. (d) Fundamental mode amplitudes for the three output ports of the test dataset. The gray and yellow bars mark the dataset presented in (b) and the three misclassified datasets, respectively.
Fig. 4. One-bit binary adder. (a) Multimode DONN and discriminant structure. (b) Var-FDTD simulation of four input cases. (c) The power of the four output ports normalized to the input port power. Ports 1 to 4 indicate the marked ports, as shown by the dashed gray lines.
Fig. 5. Previous DONN layout. (a) Every three identical Si etching slots form a group in the metaline, which is laid in a lateral open Si slab.
Fig. 6. The optical fields calculated by the DAM and EAM are compared. (a) The amplitude of the optical field in the lateral open device obtained by var-FDTD. (b) RMSE of DAM or EAM varies with the propagation distance. The gray narrow strip areas are the metalines. (c) The amplitude of the optical field in the multimode device is obtained by var-FDTD simulation. (d)–(i) Comparison of the optical fields calculated by the DAM (EAM) or var-FDTD in front of the first and second metalines, and at the end.
Fig. 7. Average and variance of mode coupling matrices categorized by the number of Si etching slots. The 4096 metalines obtained in Sec. 3.1 of the main text are classified based on the number of etching slots, ranging from 0 to 12. The quantity of the metalines in each group is listed above the images. The top image in each group displays the average amplitude of the elements in the mode coupling matrices, while the bottom image shows the variance. The numbers in the horizontal direction are the input mode numbers, and the numbers in the vertical direction are the output mode numbers.
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Run Sun, Tingzhao Fu, Yuyao Huang, Wencan Liu, Zhenmin Du, Hongwei Chen. Multimode diffractive optical neural network[J]. Advanced Photonics Nexus, 2024, 3(2): 026007
Category: Research Articles
Received: Nov. 9, 2023
Accepted: Feb. 19, 2024
Published Online: Mar. 11, 2024
The Author Email: Fu Tingzhao (futingzhao@nudt.edu.cn), Chen Hongwei (chenhw@tsinghua.edu.cn)