Programmable diffractive deep neural networks enabled by integrated rewritable metasurfaces
Year: 2025
Type: article
Abstract: Photonic neural networks capable of rapid programming are indispensable to realize many functionalities. Phase change technology can provide nonvolatile programmability in photonic neural networks. Integrating the direct laser writing technique with phase change material (PCM) can potentially enable programming and in-memory computing for on-chip photonic neural networks. Sb2Se3 is a newly introduced ultralow-loss phase change material with a large refractive index contrast over the telecommunication transmission band. Compact, low-loss, rewritable, and nonvolatile on-chip phase-change metasurfaces can be created by using direct laser writing on a Sb2Se3 thin film. Here, by cascading multiple layers of on-chip phase-change metasurfaces, an ultra-compact on-chip programmable diffractive deep neural network is theoretically demonstrated at the wavelength of 1.55 μm and benchmarked on two machine learning tasks of pattern recognition and MNIST (Modified National Institute of Standards and Technology) handwritten digits classification, and accuracies comparable to the state of the art are achieved.
Source: Scientific Reports
Author: Sanaz Zarei
Institution: Sharif University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4415113210