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arXiv 2409.06571quant-phcond-mat.other
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硅中铒掺杂物的自旋与晶体场哈密顿量的表征

Characterization of the spin and crystal field Hamiltonian of erbium dopants in silicon

Adrian Holzäpfel, Stephan Rinner, Kilian Sandholzer, Andreas Gritsch, Thierry Chanelière, Andreas Reiserer

AI总结:

本文通过共振荧光光谱分析了硅纳米光子波导中铒掺杂物的位置对称性,重构了自旋哈密顿量并部分拟合了晶体场哈密顿量,为基于Er:Si平台的高效纳米光子量子存储器铺平了道路。

摘要:

将相干发射器集成到低损耗光子电路中是量子网络的关键技术。在此背景下,植入铒的纳米光子硅器件是一个极具前景的硬件平台,它将先进的晶圆级纳米加工技术与光纤最小损耗波段中的相干发射相结合。最近的研究报告了硅晶格中两个不同的位置,在这些位置中铒可以被可重复地集成,并具有特别有前景的特性。在此,为了对这些位置进行深入分析,在沿不同方向施加磁场的纳米光子波导上进行了共振荧光光谱分析。通过这种方法,确定了位置对称性,重构了自旋哈密顿量,并对晶体场哈密顿量进行了部分拟合。所获得的磁相互作用的定量描述允许根据未来实验的需要优化塞曼分裂、光学分支比或微波驱动。除此之外,推导出的位置对称性限制了硅晶胞中铒掺杂物的位置。这是迈向对铒位置详细微观理解的关键一步,这可能有助于提高集成产率,从而为基于Er:Si平台的高效纳米光子量子存储器铺平道路。

英文摘要:

The integration of coherent emitters into low-loss photonic circuits is a key technology for quantum networking. In this context, nanophotonic silicon devices implanted with erbium are a promising hardware platform that combines advanced wafer-scale nanofabrication technology with coherent emission in the minimal-loss band of optical fibers. Recent studies have reported two distinct sites in the silicon lattice in which erbium can be reproducibly integrated with particularly promising properties. Here, for an in-depth analysis of these sites, resonant fluorescence spectroscopy is performed on a nanophotonic waveguide in magnetic fields applied along different orientations. In this way, the site symmetry is determined, the spin Hamiltonian is reconstructed and a partial fit of the crystal field Hamiltonian is performed. The obtained quantitative description of the magnetic interaction allows the optimization of Zeeman splittings, optical branching ratios or microwave driving to the needs of future experiments. Beyond that, the derived site symmetry constrains the location of the erbium dopant in the silicon unit cell. This is a key step towards a detailed microscopic understanding of the erbium sites, which may help to improve the integration yield, thus paving the way to efficient nanophotonic quantum memories based on the Er:Si platform.

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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