Acoustic Hologram Optimisation Using Automatic Differentiation

TL;DR

This paper introduces Diff-PAT, an automatic differentiation-based optimization algorithm for acoustic holograms that outperforms traditional phase-only methods, enhancing accuracy and noise-to-signal ratio in acoustic systems.

Contribution

The paper presents a novel optimization method using automatic differentiation for acoustic holograms, controlling both amplitude and phase for improved performance.

Findings

Achieved superior accuracy over conventional methods.

Demonstrated >8 dB increase in noise-to-signal ratio.

Validated with 1000 random control point sets.

Abstract

Acoustic holograms are the keystone of modern acoustics. It encodes three-dimensional acoustic fields in two dimensions, and its quality determine the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we present Diff-PAT, an acoustic hologram optimisation algorithm with automatic differentiation. We demonstrate that our method achieves superior accuracy than conventional methods. The performance of Diff-PAT was evaluated by randomly generating 1000 sets of up to 32 control points for single-sided arrays and single-axis arrays. The improved acoustic hologram can be used in wide range of applications of PATs without introducing any changes to existing systems that control the PATs. In addition, we applied Diff-PAT to acoustic metamaterial and achieved an >8 dB increase in the peak noise-to-signal ratio of acoustic hologram.