论文标题
深度学习启用了通用光学组件的复杂传输矩阵的设计
Deep learning enabled design of complex transmission matrices for universal optical components
论文作者
论文摘要
在基于光子学的量子，神经形态和模拟处理中的最新突破指出了需要对完全可编程的纳米光子设备进行新方案的必要性。基于干涉仪网格的通用光学元素是许多新技术的基础，但是与有限的芯片房地产相比，这是以总体足迹非常大的成本实现的，从而限制了这种方法的可扩展性。在这里，我们使用多端口多模型波导的复杂传输矩阵来考虑用于低损耗可编程元素的超校准平台。我们提出了一种深度学习的反网络方法，以使用弱散射扰动的模式来设计任意传输矩阵。与传统技术相比，该技术可以以四个阶减少设备的占用量的多端子设备中的强度和相位来控制强度和相位，从而为大型集成通用网络打开了大门。
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 multi-port 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 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.
