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使用迁移率谱分析方法检测缺陷状态。

Inspection of the Defect State Using the Mobility Spectrum Analysis Method.

作者信息

Ahn Il-Ho, Kim Deuk Young, Yang Woochul

机构信息

Quantum-Functional Semiconductor Research Center, Dongguk University-Seoul, Seoul 04620, Korea.

Division of Physics & Semiconductor Science, Dongguk University-Seoul, Seoul 04620, Korea.

出版信息

Nanomaterials (Basel). 2022 Aug 12;12(16):2773. doi: 10.3390/nano12162773.

Abstract

Mobility spectrum analysis (MSA) is a method that enables the carrier density (and mobility) separation of the majority and minority carriers in multicarrier semiconductors, respectively. In this paper, we use the -GaAs layer in order to demonstrate that the MSA can perform unique facilities for the defect analysis by using its resolvable features for the carriers. Using two proven methods, we reveal that the defect state can be anticipated at the characteristic temperature Tdeep, in which the ratio (RNn/Nh) that is associated with the density of the minority carrier Nn, to the density of the majority carrier Nh, exceeds 50%. (1) Using a -GaAs Schottky diode in a reverse bias regime, the position of the deep level transient spectroscopy (DLTS) peak is shown directly as the defect signal. (2) Furthermore, by examining the current-voltage-temperature (I-V-T) characteristics in the forward bias regime, this peak position has been indirectly revealed as the generation-recombination center. The DLTS signals are dominant around the Tdeep, according to the window rate, and it has been shown that the peak variation range is consistent with the temperature range of the temperature-dependent generation-recombination peak. The Tdeep is also consistent with the temperature-dependent thermionic emission peak position. By having only RNn/Nh through the MSA, it is possible to intuitively determine the existence and the peak position of the DLTS signal, and the majority carrier's density enables a more accurate extraction of the deep trap density in the DLTS analysis.

摘要

迁移率谱分析(MSA)是一种能够分别实现多载流子半导体中多数载流子和少数载流子的载流子密度(以及迁移率)分离的方法。在本文中,我们使用 -GaAs 层来证明 MSA 可以通过利用其对载流子的可分辨特征为缺陷分析提供独特的便利。通过两种已被证实的方法,我们揭示了在特征温度 Tdeep 下可以预测缺陷状态,在该温度下,与少数载流子 Nn 的密度相关的比率(RNn/Nh)与多数载流子 Nh 的密度之比超过 50%。(1)在反向偏置状态下使用 -GaAs 肖特基二极管,深能级瞬态谱(DLTS)峰的位置直接显示为缺陷信号。(2)此外,通过研究正向偏置状态下的电流 - 电压 - 温度(I - V - T)特性,该峰位置已被间接揭示为产生 - 复合中心。根据窗口速率,DLTS 信号在 Tdeep 附近占主导,并且已经表明峰变化范围与温度相关的产生 - 复合峰的温度范围一致。Tdeep 也与温度相关的热电子发射峰位置一致。通过仅通过 MSA 获得 RNn/Nh,就可以直观地确定 DLTS 信号的存在和峰位置,并且多数载流子的密度能够在 DLTS 分析中更准确地提取深陷阱密度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/14e7/9412662/3e9fff280edc/nanomaterials-12-02773-g001.jpg

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