Chip‐Compatible Optical Field Manipulation for a Single Mode Fiber Using Ultrathin 3D‐Nanoprinted Diffractive Neural Surfaces

ABSTRACT Hybrid optical computing seeks to merge the versatility of free‐space diffractive systems with the compactness and stability of integrated photonics, thereby providing a promising hardware platform for high‐bandwidth, low‐latency optical interconnects in GPU‐accelerated and data‐intensive computing systems. Yet, this integration remains fundamentally limited by the mismatch between guided optical modes and free‐space architectures, which typically require complex alignment and mode adaptation. Here, we introduce miniaturized vertical diffractive surfaces (MVDSs)—a new class of chip‐compatible diffractive neural surfaces that unify diffractive neural network principles with integrated photonic platforms. By combining physics‐driven neural network design with ultrafast three‐dimensional Galvo‐dithering and scanning two‐photon nanolithography (3D GDS‐TPN), we fabricate wavelength‐scale diffractive elements featuring vertical optical geometries, subwavelength alignment precision, and high structural scalability. These MVDSs perform diverse optical transformations—including focusing, beam steering, orbital angular momentum (OAM) generation—with diffraction efficiencies up to 91%. This work establishes a design and fabrication paradigm that bridges free‐space diffractive optics and guided‐wave photonics, enabling hybrid optical neural architectures where light itself performs customizable wavefront transformation and beam control at the chip scale.