Meta‐Attention Deep Learning for Smart Development of Metasurface Sensors

Abstract

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.

Keywords

• bound states in the continuum
• explainable deep learning
• metasensors
• metasurface
• transformer