Optical nanocavities formed by defects in a two-dimensional photonic crystal (PC) slab can simultaneously realize a very small modal volume and an ultrahigh quality factor (Q). Therefore, such nanocavities are expected to be useful for the enhancement of light–matter interaction and slowdown of light in devices. In the past, it was difficult to design a PC hole pattern that makes sufficient use of the high degree of structural freedom of this type of optical nanocavity, but very recently, an iterative optimization method based on machine learning was proposed that efficiently explores a wide parameter space. Here, we fabricate and characterize an L3 nanocavity that was designed by using this method and has a theoretical Q value of 29 × 10⁶ and a modal volume of 0.7 cubic wavelength in the material. The highest unloaded Q value of the fabricated cavities is 4.3 × 10⁶; this value significantly exceeds those reported previously for an L3 cavity, i.e., ≈2.1 × 10⁶. The experimental result shows that the iterative optimization method based on machine learning is effective in improving cavity Q values.

中文翻译：

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通过迭代机器学习方法设计的L3纳米腔的制备和表征

由二维光子晶体（PC）平板中的缺陷形成的光学纳米腔可以同时实现非常小的模态体积和超高品质因数（Q）。因此，预期此类纳米腔可用于增强光与物质的相互作用以及减慢设备中的光。过去，很难设计出能够充分利用这种类型的光学纳米腔的高度结构自由度的PC孔图案，但是最近，人们提出了一种基于机器学习的迭代优化方法，该方法可以有效地探索广泛的应用领域。参数空间。在这里，我们制造并表征了使用这种方法设计的L3纳米腔，其理论Q值为29×10 ⁶材料中的模式体积为0.7立方波长。所制造型腔的最高空载Q值为4.3×10 ⁶ ; 该值大大超过了先前针对L3腔所报告的值，即≈2.1×10 ⁶。实验结果表明，基于机器学习的迭代优化方法在提高腔体Q值方面是有效的。