Study the Cognitively Adaptive Photonic Metasurfaces for Self-Evolving Light–Matter Interaction

Abstract Photonic Metasurfaces have emerged as powerful platforms for sculpting electromagnetic waves at deeply subwavelength dimensions, enabling compact control over phase, amplitude, polarization, and wavefront topology. Despite these advances, most reported metasurfaces operate as static or externally driven devices, limiting their capacity to respond autonomously to fluctuating optical environments. In this paper, we explore a new conceptual paradigm, Cognitively Adaptive Photonic Metasurfaces (CAPMs), in which learning, memory, and adaptive behavior arise intrinsically from light–matter interaction. CAPMs are composed of resonant meta-atoms endowed with internal state variables that evolve in response to optical excitation, allowing the metasurface to modify its functionality based on illumination history. By integrating nonlinear optical responses, phase-change dynamics, and localized feedback, we develop a theoretical framework describing the self-evolution of optical states under spatiotemporally varying fields. This abstract reveals how memory formation, adaptive optimization, and predictive behavior can naturally emerge in metasurface architectures. The proposed concept opens a pathway toward intelligent photonic matter with potential applications in self-optimizing optical communication, adaptive imaging, neuromorphic photonics, and autonomous sensing systems.