Misalignment‐Robust Dynamic Cascaded Holography Accelerated by Deep Neural Networks

ABSTRACT Cascaded holographic architectures offer enhanced functionality for optical information processing, encryption, and multiplexing, but their practical implementation is severely limited by extreme sensitivity to interlayer alignment. Here, we demonstrate a dynamic cascaded holography platform using commercial phase‐only spatial light modulators by introducing a robust optimization framework that explicitly incorporates random misalignment errors into the wave propagation model. By optimizing phase patterns over diverse alignment conditions, the resulting holograms maintain stable reconstruction performance under lateral displacements of up to ∼100 pixels. Using this approach, we experimentally demonstrate robust holographic secret‐sharing, wavelength‐selective reconstruction, and high‐capacity information multiplexing with high‐fidelity image reconstruction. Furthermore, deep neural networks are employed to substantially reduce the computational overhead associated with robust phase optimization. These results establish a practical route toward scalable dynamic cascaded holography for optical encryption, holographic communication, and programmable wavefront engineering.