Metasurface-enabled multifunctional single-frequency sensors without external power

Abstract

IoT sensors are crucial for visualizing multidimensional and multimodal information and enabling future IT applications/services such as cyber-physical spaces, digital twins, autonomous driving, smart cities and virtual/augmented reality (VR or AR). However, IoT sensors need to be battery-free to realistically manage and maintain the growing number of available sensing devices. Here, we provide a novel sensor design approach that employs metasurfaces to enable multifunctional sensing without requiring an external power source. Importantly, unlike existing metasurface-based sensors, our metasurfaces can sense multiple physical parameters even at a fixed frequency by breaking classic harmonic oscillations in the time domain, making the proposed sensors viable for usage with limited frequency resources. Moreover, we provide a method for predicting physical parameters via the machine learning-based approach of random forest regression. The sensing performance was confirmed by estimating the temperature and light intensity, and excellent determination coefficients larger than 0.96 were achieved. Our study affords new opportunities for sensing multiple physical properties without relying on an external power source or requiring multiple frequencies, which markedly simplifies and facilitates the design of next-generation wireless communication systems. Metasurface-based sensors provide a battery-free sensing solution for maintaining numerous IoT devices with little human resources. However, the conventional method exploited resonant mechanisms associated with multiple physical parameters through different frequencies, although available frequencies were strictly limited. We report the first sensor design approach using circuit-based metasurfaces that offer a higher degree of freedom to design time-varying scattering profiles associated with multiple physical properties at a single frequency. Our prototype detects light intensity and temperature with an excellent determination coefficient above 0.96 via a machine-learning technique.