Actively Reconfigurable Segmented Spatial Sound Modulators

High-quality acousto-holographic patterns and images, integral to applications like 3D displays, acoustophoresis, and midair haptics, require precise distribution of ultrasound waves to achieve. Essential tools for this task are spatial sound modulators (SSMs), which control constituent elements to enable dynamic distribution of sound pressure. However, current ultrasonic SSMs face limitations due to high costs and the intricate actuation of numerous small, closely spaced units. This study introduces "segmented SSMs," novel devices that combine traditional acoustic metasurface pixel units into custom-shaped segmented elements. These segmented SSMs reduce actuation costs and complexity while retaining pressure distribution quality. This approach includes a custom phase agglomeration algorithm (PAA), that offers a hierarchy of potential segmentation solutions for user selection. An SSM fabrication method is detailed using off-the-shelf 3D printers and bespoke control electronics, completing an end-to-end methodology from conception to realization. This approach is validated with two prototype SSM devices that focus sound waves and levitate polystyrene beads using dynamic segmented elements. Further enhancements to the technique are explored through hybrid SSM devices with both static and dynamic elements. The pipeline facilitates efficient SSM construction across diverse applications and invites the inception of future devices with varying sizes, uses, and actuation mechanisms. Segmented spatial sound modulators (SSMs) are innovative devices that combine traditional acoustic metasurface units into custom-shaped segmented elements, reducing actuation cost and complexity while maintaining high-quality sound pressure distributions. The end-to-end methodology employs a custom phase agglomeration algorithm and uses off-the-shelf 3D printers and bespoke control electronics to ensure efficient SSM construction for applications like 3D displays and acoustic levitation.image