论文标题
机器学习可以通过硅 - 纳米图案数字化材料实现超紧凑的集成光子学
Machine Learning enables Ultra-Compact Integrated Photonics through Silicon-Nanopattern Digital Metamaterials
论文作者
论文摘要
在这项工作中，我们演示了三种超连接的集成量音设备，这些设备是通过机器学习算法以及有限差分时间域（FDTD）建模设计的。通过将设计域数字化到“二进制像素”中，这些数字超材料也很容易制造。通过显示各种设备（Beamsplitter和波导弯曲），我们展示了方法的通用性。在面积足迹小于$ {λ_0}^2 $的情况下，我们的设计是迄今为止报告的最小的。我们的方法将机器学习与数字化材料结合在一起，以实现超紧凑的可制造设备，从而为新的“ Photonics Moore定律”提供动力。
In this work, we demonstrate three ultra-compact integrated-photonics devices, which are designed via a machine-learning algorithm coupled with finite-difference time-domain (FDTD) modeling. Through digitizing the design domain into "binary pixels" these digital metamaterials are readily manufacturable as well. By showing a variety of devices (beamsplitters and waveguide bends), we showcase the generality of our approach. With an area footprint smaller than ${λ_0}^2$, our designs are amongst the smallest reported to-date. Our method combines machine learning with digital metamaterials to enable ultra-compact, manufacturable devices, which could power a new "Photonics Moore's Law."
