Nonvolatile PCM-driven photonic computing using programmable sub-wavelength digital metasurfaces

We present a programmable nonvolatile convolutional core based on sub-wavelength phase-change digital meta-surfaces, designed to enable energy-efficient and scalable optical computing. Utilizing sub-wavelength Sb₂Se₃ cylindrical arrays, the kernel achieves enhanced weight distinguishability, reduced insertion loss, and fine-tuned multi-level reconfigurability, addressing the requirements of optical neural networks (ONNs). Experimental results further demonstrate a weight range from 0.1 to 0.93 with over six levels per unit, supported by a zero static power and digital architecture. We validate the system's 2-bit digital reconfigurability and post-trimming functionality using laser direct-writing techniques. This novel, to the best of our knowledge, integration of phase change materials and digital architectures represents a promising pathway for high-accuracy optical computing applications, such as neural network-based image recognition.