Deep learning estimation of complex reverberant wave fields by a programmable metasurface

Abstract

Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging, particularly in reverberating environments. The use of programmable metasurfaces provides a potential means of directing waves to optimize wireless channels on-demand, ensuring reliable operation and protecting sensitive electronic components. Here we introduce a technique that combines a deep learning network with a binary programmable metasurface to shape waves in complex reverberant electromagnetic environments, in particular ones where there is no direct line of sight. We applied this technique for wavefront reconstruction and control, and accurately determined metasurface configurations based on measured system scattering responses in a chaotic microwave cavity. The state of the metasurface that realizes desired electromagnetic wave field distribution properties was successfully determined even in cases previously unseen by the deep learning algorithm. Our technique is enabled by the reverberant nature of the cavity, and is effective with a metasurface that covers only $\sim$1.5\% of the total cavity surface area.

References in corpus (48)

• Training deep neural networks for the inverse design of nanophotonic structures
• Coherent Perfect Absorbers: Time-reversed Lasers
• Phase control algorithms for focusing light through turbid media
• Universal optimal transmission of light through disordered materials
• The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence
• Reinforcement Learning in Different Phases of Quantum Control
• Deep learning meets nanophotonics: A generalized accurate predictor for near fields and far fields of arbitrary 3D nanostructures
• Freak waves in the linear regime: A microwave study
• Transmission of natural scene images through a multimode fibre
• Quantum Metasurfaces
• Deep Complex Networks
• Universal Impedance Fluctuations in Wave Chaotic Systems
• Artificial Neural Network with Physical Dynamic Metasurface Layer for Optimal Sensing
• Focusing inside Disordered Media with the Generalized Wigner-Smith Operator
• Elucidating the Behavior of Nanophotonic Structures Through Explainable Machine Learning Algorithms
• Perfect Absorption in a Metasurface-Programmable Complex Scattering Enclosure
• Universal Statistics of the Scattering Coefficient of Chaotic Microwave Cavities
• Statistics of Impedance and Scattering Matrices in Chaotic Microwave Cavities: Single Channel Case
• Deeply Sub-Wavelength Localization with Reverberation-Coded-Aperture
• Adaptive-optics-enabled quantum communication: A technique for daytime space-to-Earth links
• Optimal Wave Fields for Micro-manipulation in Complex Scattering Environments
• Reinforcement Learning assisted Quantum Optimization
• Statistics of Impedance and Scattering Matrices of Chaotic Microwave Cavities with Multiple Ports
• Universal Properties of 2-Port Scattering, Impedance and Admittance Matrices of Wave Chaotic Systems
• Spatio-temporal wavefront shaping in a microwave cavity
• Perfect Absorption in Complex Scattering Systems with or without Hidden Symmetries
• Experimental verification of fidelity decay: From perturbative to Fermi Golden Rule regime
• A Reinforcement Learning approach for Quantum State Engineering
• The effect of short ray trajectories on the scattering statistics of wave chaotic systems
• Nonlinear Time-Reversal in a Wave Chaotic System
• Experimental Examination of the Effect of Short Ray Trajectories in Two-port Wave-Chaotic Scattering Systems
• Experimental Test of Universal Conductance Fluctuations by means of Wave-Chaotic Microwave Cavities
• Universal and non-universal properties of wave chaotic scattering systems
• A first-principles model of time-dependent variations in transmission through a fluctuating scattering environment
• Optimized Analog Information Multiplexing Across Custom-Tailored MetaCavity Configurations
• On-demand coherent perfect absorption in complex scattering systems: time delay divergence and enhanced sensitivity to perturbations
• Robust Position Sensing with Wave Fingerprints in Dynamic Complex Environments
• Wave Chaos in a Cavity of Regular Geometry with Tunable Boundaries
• Wavefront Shaping with a Tunable Metasurface: Creating Coldspots and Coherent Perfect Absorption at Arbitrary Frequencies
• Non-linear wavefront reconstruction methods for pyramid sensors using Landweber and Landweber-Kaczmarz iteration
• Focusing Waves at Arbitrary Locations in a Ray-Chaotic Enclosure Using Time-Reversed Synthetic Sonas
• Sensor Based on Extending the Concept of Fidelity to Classical Waves
• Sensing Small Changes in a Wave Chaotic Scattering System
• Quantifying Volume Changing Perturbations in a Wave Chaotic System
• Shaping Microwave Fields using Non-Linear Unsolicited Feedback: Application to Enhanced Energy Harvesting
• Classification and prediction of wave chaotic systems with machine learning techniques
• Implementing Non-Universal Features with a Random Matrix Theory Approach: Application to Space-to-Configuration Multiplexing
• Real-time Adaptive Optics with pyramid wavefront sensors: Accurate wavefront reconstruction using iterative methods