Gao, Y., Chen, W., Li, F., Zhuang, M., Yan, Y., Wang, J., Wang, X., Dong, Z., Ma, W., & Zhu, J. (2024). Meta‐Attention Deep Learning for Smart Development of Metasurface Sensors. Advanced Science, 11(42). Portico. https://doi.org/10.1002/advs.202405750
Abstract:
Optical metasurfaces with pronounced spectral characteristics are promising for sensor applications. Currently, deep learning (DL) offers a rapid manner to design various metasurfaces. However, conventional DL models are usually assumed as black boxes, which is difficult to explain how a DL model learns physical features, and they usually predict optical responses of metasurfaces in a fuzzy way. This makes them incapable of capturing critical spectral features precisely, such as high quality (Q) resonances, and hinders their use in designing metasurface sensors. Here, a transformer‐based explainable DL model named Metaformer for the high‐intelligence design, which adopts a spectrum‐splitting scheme to elevate 99% prediction accuracy through reducing 99% training parameters, is established. Based on the Metaformer, all‐dielectric metasurfaces based on quasi‐bound states in the continuum (Q‐BIC) for high‐performance metasensing are designed, and fabrication experiments are guided potently. The explainable learning relies on spectral position encoding and multi‐head attention of meta‐optics features, which overwhelms traditional black‐box models dramatically. The meta‐attention mechanism provides deep physics insights on metasurface sensors, and will inspire more powerful DL design applications on other optical devices.
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Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - Career Development Award
Grant Reference no. : C210112019
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity (MTC) Individual Research Grant
Grant Reference no. : M21K2c0116
This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity (MTC) Individual Research Grant
Grant Reference no. : M22K2c0088
This research / project is supported by the National Research Foundation, Singapore - Quantum Engineering Programme 2.0
Grant Reference no. : NRF2021-QEP2-03-P09
This research is supported by core funding from: Agency for Science, Technology and Research DELTA-Q 2.0
Grant Reference no. : C230917001