2023
DOI: 10.1088/1674-1056/ac70b5
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Atomic-scale insights of indium segregation and its suppression by GaAs insertion layer in InGaAs/AlGaAs multiple quantum wells

Abstract: This work related to the In segregation and its suppression in InGaAs/AlGaAs quantum well is investigated using high-resolution XRD and PL, combined with the state-of-the-art aberration corrected scanning transmission electron microscopy (Cs-STEM) techniques. To facility our study, we grow two multiple quantum wells (MQWs) samples, which are almost identical except that in sample B that a thin GaAs layer is inserted in each of the InGaAs well and AlGaAs barrier layer comparing to pristine InGaAs/AlGaAs MQWs (s… Show more

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Cited by 3 publications
(2 citation statements)
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“…Previous studies have adopted photoluminescence (PL) to analyze the intermixing degree of interfacial atoms by considering the change in the diffusion components rather than the exact effect of the interfacial strain and the microstructure on their optical properties [ 9 , 19 ]. Recently, we obtained the interfacial microstructure of epitaxial wafers by transmission electron microscopy and explained the effects of interfacial atomic segregation as well as interstitial atoms on the luminescence properties of QWs [ 20 , 21 , 22 , 23 ]. Therefore, the influence of strain-induced intermixing on the QW luminescence characteristic is complex and needs to be further explored.…”
Section: Introductionmentioning
confidence: 99%
“…Previous studies have adopted photoluminescence (PL) to analyze the intermixing degree of interfacial atoms by considering the change in the diffusion components rather than the exact effect of the interfacial strain and the microstructure on their optical properties [ 9 , 19 ]. Recently, we obtained the interfacial microstructure of epitaxial wafers by transmission electron microscopy and explained the effects of interfacial atomic segregation as well as interstitial atoms on the luminescence properties of QWs [ 20 , 21 , 22 , 23 ]. Therefore, the influence of strain-induced intermixing on the QW luminescence characteristic is complex and needs to be further explored.…”
Section: Introductionmentioning
confidence: 99%
“…Theoretically, considerable quantum algorithmic work is underway, such as cryptanalysis [5,6], to reduce the resources needed for implementing important classical algorithms. In recent years, a series of quantum algorithms were designed for machine learning problems in attempting to achieve potential quantum advantages, such as classification [7][8][9], clustering [10][11][12][13][14][15], linear regression [16][17][18], dimensionality reduction [19][20][21][22][23], matrix computation [24][25][26] and anomaly detection [27]. More progress on quantum machine learning algorithms can be found in Refs.…”
Section: Introductionmentioning
confidence: 99%