重新评估硅中的铁镓重组活动

T. T. Le, Zhuangyi Zhou, Alan Chen, Zhongshu Yang, F. Rougieux, D. Macdonald, A. Liu
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摘要

在这项工作中,我们利用注入相关寿命光谱(IDLS)对硅中的铁镓(FeGa)重组参数进行了全面的重新评估。我们使用了掺镓硅片(电阻率各不相同),通过离子注入和分布,在硅片主体中精确地加入了有意污染的铁。通过测量在已知会导致铁-镓解离和结合的条件下有效少数载流子寿命的变化,证实了这些硅晶片中存在间隙铁和铁-镓,以及它们对寿命的限制作用。深层瞬态光谱也证实了铁的存在。为确保对硅中的 FeGa 缺陷进行准确的 IDLS 分析,我们采用了寿命线性化方案,以有效过滤其他缺陷的干扰。误差分析用于找到最适合实验数据的缺陷参数组合,并确定与 IDLS 最佳拟合结果相关的不确定性范围。通过肖克利-雷德-霍尔统计法对实验 IDLS 的最佳拟合,得出了硅中 FeGa 缺陷的电子俘获截面 σn=2.3×10-14cm2 、空穴俘获截面 σp=1.1×10-14cm2 和陷阱能级 Et=EV+0.2-0.01+0.02eV。通过实验测量 Fei 和 FeGa 寿命曲线的交叉点,也验证了所提取的缺陷参数。
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Reassessing iron–gallium recombination activity in silicon
In this work, we present a comprehensive re-evaluation of the iron–gallium (FeGa) recombination parameters in silicon using injection-dependent lifetime spectroscopy (IDLS). Ga-doped silicon wafers (of varying resistivities) with precise concentrations of intentional iron contamination in the silicon wafer bulk, through ion implantation and distribution, were used. The presence of interstitial Fei and FeGa, and their lifetime-limiting effects in these silicon wafers, were confirmed through measuring the effective minority carrier lifetime changes during the conditions that are known to cause FeGa dissociation and association. The presence of Fe was also confirmed by deep-level transient spectroscopy. To ensure accurate IDLS analysis of the FeGa defect in silicon, a lifetime linearization scheme was employed to effectively filter out interference by other defects. Error analysis was employed to find the combination of defect parameters that best fit the experimental data and to ascertain the range of uncertainty associated with the IDLS best-fit results. The optimal fitting of the experimental IDLS by Shockley–Read–Hall statistics produced an electron capture cross section σn=2.3×10−14cm2, hole capture cross section σp=1.1×10−14cm2, and a trap energy level Et=EV+0.2−0.01+0.02eV for the FeGa defect in silicon. The extracted defect parameters are also verified by experimentally measuring the crossover point of Fei and FeGa lifetime curves.
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