Mechanically Programmable Diffractive Neural Networks Based on Pancharatnam‐Berry Phase

Abstract A mechanically programmable diffractive neural network based on Pancharatnam‐Berry (PB) phase metasurfaces, consisting of rotatable PB phase meta‐atoms as fundamental building blocks is presented. By precisely controlling the rotation angle of each programmable meta‐atom in a mechanical way, modulations of the PB phase distribution are achieved, and flexible programmability of the network to adapt to diverse tasks is enabled. The system seamlessly integrates the low‐power consumption advantage of passive networks with the flexibility of programmable networks, achieving orders‐of‐magnitude reduction in energy consumption while maintaining optimal performance balance. Experimental results demonstrate the system's mechanical programmability with high‐precision classification ability (100% test accuracy) in multi‐task operations, including zodiac sign recognition and handwritten digit classification. The proposed MP‐DNN operates without an external energy supply during the matrix computations and consumes only minimal power during task switching, thus offering an energy‐efficient and low‐power solution for reconfigurable diffractive neural networks.