Mutual Empowerment of Artificial Intelligence and Metasurfaces: Intelligent Nanophotonics and Optical Intelligence
A new paper published in Opto-Electronic Sciences examines the mutual interaction between artificial intelligence and metasurfaces and its impact on nanophotonics and optical systems.
A comprehensive review article published in the journal Opto-Electronic Sciences systematically evaluates recent developments, practical applications, and future directions in the fields of intelligent nanophotonics and optical intelligence by addressing the mutual empowerment process between artificial intelligence and metasurfaces.
The Convergence of Artificial Intelligence and Metasurfaces
While artificial intelligence radically transforms data processing and sensing processes, traditional optical components remain inadequate in dynamic environments. Metasurfaces composed of subwavelength artificial nanostructures offer significant advantages by precisely controlling the amplitude, phase, and polarization of light. The independent yet complementary trajectories of these two fields form a new interdisciplinary frontier.
Two-Way Mutual Empowerment Process
With its powerful computational capabilities, artificial intelligence serves as an electromagnetic modeling engine for metasurface design, while metasurfaces offer versatile platforms for electromagnetic wave manipulation and various computational tasks. These mutual advancements bring together the themes of intelligent nanophotonics and optical intelligence, paving the way for integrated photonic information systems.
Physics-Driven Modeling and Design
AI-driven physical modeling has evolved from data-driven methods to physics-based and physics-informed approaches. These approaches reduce the computational burden of iterative electromagnetic simulations while enhancing accuracy, convergence, and robustness by integrating physical constraints into learning algorithms.
Structural Optimization Tiers
The structural optimization advances provided by artificial intelligence are organized across three progressive levels: meta-atoms, meta-arrays, and systems. This progression marks a significant shift from isolated component design to global photonic system optimization.
Optical Mathematical Computing
Optical mathematical computing utilizes optical responses designed to perform predefined mathematical operations. Metasurfaces and photonic architectures enable the solution of integral and differential equations with massive parallelism and high computational speed.
Optical Neural Networks and Future Prospects
Optical neural networks are achieving successes in areas such as object classification, privacy encryption, and image reconstruction. The future of the field is addressed from five key perspectives: physical limitations, reconfigurable materials, on-chip integration, and versatile algorithms.