Deep‐learning‐based polarization‐dependent switching metasurface in dual‐band for optical communication

Abstract To address the critical limitations of conventional band‐switching technologies – such as their slow speed, high energy consumption, and mechanical instability – this research introduces a novel deep‐learning‐driven framework for the intelligent inverse design of polarization‐multiplexed metasurfaces. This approach represents a paradigm shift from traditional methods by enabling single‐step, computational discovery of metasurface designs that directly encode two distinct optical functions within a single flat device. At the heart of our framework is a custom‐designed deep neural network that seamlessly integrates parallel convolutional layers for robust feature extraction with cascaded regression modules for high‐precision prediction. This hybrid architecture allows us to engineer sub‐wavelength meta‐atoms to achieve desired optical responses rigorously. As a groundbreaking demonstration, we designed and optimized a metasurface that achieves dynamic band switching solely through polarization modulation: it generates a targeted transmission peak in the O‐band (1,260–1,360 nm) under y ‐polarization and an independent peak in the C‐band (1,530–1,565 nm) under x ‐polarization. This mechanism eliminates the need for moving parts. The resulting device exhibits a switching efficiency orders of magnitude greater than its mechanical counterparts, while simultaneously offering enhanced stability, lower power consumption, and inherent adaptability for reconfigurable optical networks. Our work not only validates a specific device but also establishes a robust and generalizable design paradigm, underscoring the transformative potential of uniting deep learning with metasurfaces to achieve ultra‐fast, intelligent, and efficient photonic systems for next‐generation optical communications.