Analyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.

Acoustic hyperlenses have recently attracted much attention for promising applications in various fields. Yet the experimental realization of an acoustic hyperlens working in a real three-dimensional (3D) world is still lacking. Here, we theoretically propose and experimentally demonstrate a 3D acoustic hyperlens capable of producing super-resolution imaging for broadband airborne sound. A simple nonresonant metamaterial is designed as a practical implementation that simultaneously ensures tessellation of the curved surface and deep-subwavelength resolution. We analyze the dispersion relationship of the designed metamaterial that converts the evanescent waves into radially propagating modes based on positive extreme anisotropy. The effectiveness of our mechanism is demonstrated both numerically and experimentally via the production of 3D magnifying super-resolution imaging of small objects containing subwavelength patterns within a broad frequency range. We envision the realization of a 3D acoustic hyperlens to offer possibilities for the design of acoustic super-resolution imaging devices and their application in diverse scenarios ranging from medical ultrasound imaging to noninvasive evaluation.