Multitask Diffractive Deep Neural Networks Enabled by Joint Distance–Frequency Encoding

ABSTRACT Diffractive deep neural networks (D 2 NNs) offer a promising route toward high‐speed wave‐based computing, but most existing architectures are restricted to a single fixed function, whereas multitask implementations often require additional hardware complexity. Here, a multitask D 2 NN architecture based on joint distance and frequency encoding is proposed and demonstrated. The interlayer distance is introduced as a physically reconfigurable task‐encoding dimension and is combined with a dual‐frequency phase‐independent metasurface to enable task multiplexing within a single passive hardware system. Scalar diffraction simulations on the corresponding testing datasets yielded accuracies of 99.7% for 9‐class orbital angular momentum (OAM) classification, 86.4% for 9‐class direction recognition, 98.8% for 4‐category programmable logic‐gate operations, and 95.4% for 4‐class MNIST digit recognition (Digits 0–3). Full‐wave electromagnetic simulations further support the physical feasibility of the designed metasurface implementation, whereas microwave measurements on selected representative inputs provide proof‐of‐concept evidence of task‐dependent energy routing and distance‐enabled task switching. This joint‐encoding strategy provides a compact and resource‐efficient route for multifunctional intelligent wave‐based computing systems.