This paper is a contribution to the Physical Review Applied collection titled Phononics and Metamaterials.

A neuromorphic metasurface embodies mechanical intelligence by realizing physical neural architectures. It exploits guided wave scattering to conduct computations in an analog manner. Through multiple tuned waveguides, the neuromorphic system recognizes the features of an input signal and self-identifies its classification label. The computational input is introduced to the system through mechanical excitations at one edge, generating elastic waves that traverse multiple layers of resonant metasurfaces. These metasurfaces possess a tunable phase akin to trainable parameters in deep learning algorithms. While early efforts have been promising, the well-established constraints on wave propagation in finite media limit such systems to single-task realizations. In this work, we devise a dual classifier neuromorphic metasurface and demonstrate its effectiveness in carrying out two completely independent classification problems that are concurrently carried out in parallel, thus addressing a major bottleneck in physical computing systems. Parallelization is achieved through smart multiplexing of the carrier computational frequency, enabled by prescribed temporal modulations of the embedded waveguides. The presented theory and results pave the way for new paradigms in wave-based computing systems, which have been elusive thus far.

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• Analog computation
• Neuromorphic computing
• Time crystals
• Acoustic metamaterials
• Artificial neural networks
• Metasurfaces
• Multiplexers
• Deep learning