Weichao Kong, Jun Chen, Zengxin Huang, Dengfeng Kuang. Bidirectional cascaded deep neural networks with a pretrained autoencoder for dielectric metasurfaces[J]. Photonics Research, 2021, 9(8): 1607
- Photonics Research
- Vol. 9, Issue 8, 1607 (2021)
Fig. 1. Generation process of database. (a) Association of 100 meta-atoms. (b) Sample and its generation method. (c) Structure of the selected random meta-atom. (d) Transmission spectrum (phase and amplitude) of the meta-atom. (e) | E x | 2
Fig. 2. Comparison of four kinds of frequently used material combinations in visible light.
Fig. 3. Structure of CDNN for metasurfaces. (a) Forward and backward networks for prediction of transmission spectrum and structure of meta-atoms. (b)–(d) Structures of the simulator, autoencoder, and translator, respectively.
Fig. 4. Evaluation of the simulator. (a) Training and test loss functions along with epochs. (b) Loss functions of different depth. (c) Counts of MAE for whole test sets.
Fig. 5. Four samples for the simulator. Inset is corresponding meta-atom. (a) MAE of 0.0104. (b) MAE of 0.0327. (c) MAE of 0.0356. (d) MAE of 0.0550.
Fig. 6. Visualization of forward network. (a) Meta-atom and its structure parameters. (b) Feature maps extracted from the first and second convolutional layers. (c) Feature maps extracted from the third and fourth convolutional layers. (d) Activation thermal maps.
Fig. 7. Evaluation of backward networks. (a) Restored images and (b) original images. (c) Counts of MAE for whole test sets.
Fig. 8. Four samples with typical losses for CDNN. (a) MAE of 0.0317. (b) MAE of 0.0520. (c) MAE of 0.0784. (d) MAE of 0.1621.
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