Deep Learning Enabled Design of Complex Transmission Matrices for Universal Optical Components
Deep Learning Enabled Design of Complex Transmission Matrices for Universal Optical Components
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DOI：
10.1021/acsphotonics.0c01481
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发表时间：
2021-01-05
期刊：
影响因子：
7
通讯作者：
Muskens, Otto L.
中科院分区：
文献类型：
--
作者：
Dinsdale, Nicholas J.;Wiecha, Peter R.;Muskens, Otto L.
最近在光子学为基础的量子，神经形态，和模拟处理的突破，指出了完全可编程的纳米光子器件的新方案的需要。基于干涉仪网格的通用光学元件是这些新技术中的许多技术的基础，然而，这是以与有限的芯片真实的资产相比非常大的总体占用面积为代价实现的，从而限制了该方法的可扩展性。在这里，我们考虑使用多端口多模波导的复杂传输矩阵的低损耗可编程元件的超紧凑平台。我们提出了一种深度学习逆网络方法，使用弱散射扰动模式来设计任意传输矩阵。所展示的技术允许在一个多端口设备的强度和相位的控制在一个四个订单减少设备的足迹相比，传统的技术，从而打开了大门，大规模的集成通用网络。
Recent breakthroughs in photonics-based quantum, neuromorphic, and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are underpinning many of these new technologies, however, this is achieved at the cost of an overall footprint that is very large compared to the limited chip real estate, restricting the scalability of this approach. Here, we consider an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multiport multimode waveguide. We propose a deep learning inverse network approach to design arbitrary transmission matrices using patterns of weakly scattering perturbations. The demonstrated technique allows control over both the intensity and the phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus, opening the door for large-scale integrated universal networks.