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Inspection of the Defect State Using the Mobility Spectrum Analysis Method.
Ahn, Il-Ho; Kim, Deuk Young; Yang, Woochul.
Afiliação
  • Ahn IH; Quantum-Functional Semiconductor Research Center, Dongguk University-Seoul, Seoul 04620, Korea.
  • Kim DY; Quantum-Functional Semiconductor Research Center, Dongguk University-Seoul, Seoul 04620, Korea.
  • Yang W; Division of Physics & Semiconductor Science, Dongguk University-Seoul, Seoul 04620, Korea.
Nanomaterials (Basel) ; 12(16)2022 Aug 12.
Article em En | MEDLINE | ID: mdl-36014638
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 p-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 p-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.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article