片上可重写相变超表面用于可编程衍射深度神经网络
On-chip rewritable phase-change metasurface for programmable diffractive deep neural networks
AI总结：
本文提出一种基于Sb2Se3相变材料、通过直接激光写入构建的片上可重写相变超表面，级联多层实现可编程衍射深度神经网络，在1.55微米波长下完成模式识别和MNIST分类，精度达先进水平且功耗低。
摘要：
能够快速编程的光子神经网络对于实现许多功能是必不可少的。相变技术可以在光子神经网络中提供非易失性可编程性。将直接激光写入技术与相变材料（PCM）相结合，有望实现片上光子神经网络的编程和存内计算。Sb2Se3是一种新引入的超低损耗相变材料，在电信传输波段具有较大的折射率对比度。通过在Sb2Se3薄膜上使用直接激光写入，可以创建紧凑、低损耗、可重写且非易失性的片上相变超表面。这里，通过级联多层片上相变超表面，在1.55微米波长下展示了一种超紧凑的片上可编程衍射深度神经网络，并在模式识别和MNIST（美国国家标准与技术研究院修改版）手写数字分类两个机器学习任务上进行了基准测试，取得了与最先进技术相当的准确率。由于Sb2Se3的超低损耗及其非易失性（无需持续供电即可维持其编程状态），所提出的片上可编程衍射深度神经网络在功耗方面也具有优势。
英文摘要：
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 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 demonstrated at the wavelength of 1.55um 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. The proposed on-chip programmable diffractive deep neural network is also advantageous in terms of power consumption because of the ultralow-loss of the Sb2Se3 and its nonvolatility which requires no constant power supply to maintain its programmed state.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
