arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.10651physics.opticsphysics.app-ph
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用于多模光子卷积神经网络的波导可编程相变元表面

Programmable Phase-change Metasurfaces on Waveguides for Multimode Photonic Convolutional Neural Network

Changming Wu, Heshan Yu, Seokhyeong Lee, Ruoming Peng, Ichiro Takeuchi, Mo Li

摘要:

神经形态光子学最近已经成为一种有前途的硬件加速器,与数字电子相比,具有显着的潜在速度和能量优势,用于机器学习算法,如各种类型的神经网络。集成光子网络在执行矩阵向量乘法(MVM)的模拟计算方面特别强大,因为它们为数据传输提供了无与伦比的速度和带宽密度。在集成光子器件中使用非易失性相变材料能够为片上光学计算提供不可或缺的编程和内存计算能力。在这里,我们展示了一个多模光子计算的核心组成的一个阵列的可编程模式转换器的基础上的元表面的相变材料。可编程转换器利用相变材料Ge-Sb-Te在相变过程中的折射率变化来控制波导空间模式,模式对比度高达64级。该对比度用于表示矩阵元素,具有6位分辨率以及正值和负值,以在神经网络算法中执行MVM计算。我们展示了一个光学卷积神经网络,它可以高精度地执行图像处理和分类任务。具有较宽的工作带宽和紧凑的器件尺寸,所展示的多模光子核心是非常有前途的大规模光子处理器的高吞吐量的光神经网络。

英文摘要:

Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics, for machine learning algorithms such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on metasurface made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge-Sb-Te during phase transition to control the waveguide spatial modes with a very high precision of up 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate an optical convolutional neural network that can perform image processing and classification tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is very promising toward a large-scale photonic processor for high-throughput optical neural networks.

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。

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