Convolutional neural networks used for random structure SPP gratings spectral response prediction
| Autor: | Tianle Qu, Liping Zhu, Zhenghua An |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Optics Letters. 48:448 |
| ISSN: | 1539-4794 0146-9592 |
| DOI: | 10.1364/ol.480210 |
| Popis: | Data-driven design approaches based on deep learning have been introduced into nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report a convolutional neural network (CNN) used to perform the prediction of surface plasmon polariton (SPP) grating output spectra, which is not limited by predefined shapes. For a random given structure, the network can output spectra with effective prediction, so that the simulation results are in excellent agreement with the network prediction results. Compared with the traditional finite-difference time-domain (FDTD) method, the CNN model proposed in this Letter has absolute advantages in speed. Previous studies often used a regular device structure to modify its parameters for prediction; the random structure design method adopted in this Letter also provides a new, to the best of knowledge, idea for device design. |
| Databáze: | OpenAIRE |
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