Multi-frequency acoustic hologram generation with a physics-enhanced deep neural network

Abstract

Here, a physics-enhanced multi-frequency acoustic hologram deep neural network (PhysNet_MFAH) method is proposed for designing multi-frequency acoustic holograms, which is built by incorporating multiple physical models that represent the physical processes of acoustic waves propagation for a set of design frequencies into a deep neural network. It is demonstrated that one needs only to feed a set of frequency-specific target patterns into the network, the proposed PhysNet_MFAH method can automatically, accurately, and rapidly generate a high-quality multi-frequency acoustic hologram for holographic rendering of different target acoustic fields in the same or distinct regions of the target plane when driven at different frequencies. Remarkably, it is also demonstrated that the proposed PhysNet_MFAH method can achieve a higher quality of the reconstructed acoustic intensity fields than the existing optimization methods IASA and DS for designing multi-frequency acoustic holograms at a relatively fast-computational speed. Furthermore, the performance dependencies of the proposed PhysNet_MFAH method on different design parameters are established, which provide insight into the performance of the reconstructed acoustic intensity fields when subject to different design conditions of the proposed PhysNet_MFAH method. We believe that the proposed PhysNet_MFAH method can facilitate many potential applications of acoustic holograms, ranging from dynamic particle manipulation to volumetric display.

Keywords: Acoustic field reconstruction; Acoustic hologram; Deep learning; Frequency multiplexing; Wave propagation.