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arXiv 1702.00260physics.opticscs.LG
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基于机器学习的强光约束紧凑光子结构设计

Machine learning based compact photonic structure design for strong light confinement

TED University(TED大学);TOBB University of Economics and Technology(TOBB经济与技术大学)

Mirbek Turduev, Çağrı Latifoğlu, İbrahim Halil Giden, Y. Sinan Hanay

摘要:

提出了一种基于机器学习的光子结构设计方法。特别是,我们专注于强光限制,允许设计一个有效的自由空间到波导耦合器,这是由硅板覆盖在二氧化硅衬底的顶部。学习算法是使用按位平方Si单元实现的,并且整个优化的器件具有$\boldsymbol{2 \, μm \times 1\, μm}$的占用空间,这是有史以来数值实现的最小尺寸。为了研究硅板厚度对优化后的光子晶体结构的亚波长聚焦和强耦合特性的影响,进行了三维时域数值计算。计算了板厚为$\boldsymbol{280nm}$时半高宽和耦合效率的最佳值分别为$\boldsymbol{0.158 λ}$和$\boldsymbol{-1.87\,dB}$。与传统的对应物相比,优化的透镜和耦合器设计易于通过光刻技术制造,非常紧凑,并且可以在电信波长下工作。所提出的研究结果表明,机器学习可以在各种潜在的应用,如偏振分割,光束操纵和光学互连的有效光子设计是有益的。

英文摘要:

We present a novel approach based on machine learning for designing photonic structures. In particular, we focus on strong light confinement that allows the design of an efficient free-space-to-waveguide coupler which is made of Si- slab overlying on the top of silica substrate. The learning algorithm is implemented using bitwise square Si- cells and the whole optimized device has a footprint of $\boldsymbol{2 \, μm \times 1\, μm}$, which is the smallest size ever achieved numerically. To find the effect of Si- slab thickness on the sub-wavelength focusing and strong coupling characteristics of optimized photonic structure, we carried out three-dimensional time-domain numerical calculations. Corresponding optimum values of full width at half maximum and coupling efficiency were calculated as $\boldsymbol{0.158 λ}$ and $\boldsymbol{-1.87\,dB}$ with slab thickness of $\boldsymbol{280nm}$. Compared to the conventional counterparts, the optimized lens and coupler designs are easy-to-fabricate via optical lithography techniques, quite compact, and can operate at telecommunication wavelengths. The outcomes of the presented study show that machine learning can be beneficial for efficient photonic designs in various potential applications such as polarization-division, beam manipulation and optical interconnects.

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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