Low areal densities of InAs quantum dots on GaAs(1 0 0) prepared by molecular beam epitaxy

Verma, Akshay; Bopp, Frederik; Finley, J. J.; Jonas, Björn; Zrenner, A.; Reuter, D.

doi:10.1016/j.jcrysgro.2022.126715

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Self-assembled quantum dots (QDs) have attracted great interest due to their applications in various optoelectronic devices 1 . The so-called Stranski-Krastanow (SK) mode in molecular beam epitaxy (MBE) is widely used for growing these high-quality QDs 2 .…”

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confidence: 99%

“…The so-called Stranski-Krastanow (SK) mode in molecular beam epitaxy (MBE) is widely used for growing these high-quality QDs 2 . For specific applications, such as QD lasers, high QD densities are required; while lowdensity QDs are necessary for other applications, such as single photon sources 1 . However, the outcomes of any QD growth process are a complex function of a large number of variables including the substrate temperature, III/V ratio, and growth rate, etc.…”

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confidence: 99%

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Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Shen,

Zhan,

Xin

et al. 2024

Nat Commun

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The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5 × 1010 cm−2 down to 3.8 × 108 cm−2 or up to 1.4 × 1011 cm−2. Compared to traditional methods, our approach can dramatically expedite the optimization process and improve the reproducibility of MBE. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

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confidence: 99%

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confidence: 99%

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Shen,

Zhan,

Xin

et al. 2024

Nat Commun

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Simulation and Analysis of the Optical Characteristics of Cylindrical Micropillars with InAs/GaAs Quantum Dots

Bobrov

Blokhin²,

Maleev³

et al. 2022

Jetp Lett.

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The optical characteristics of vertical cylindrical micropillars with AlGaAs distributed Bragg reflectors and InAs/GaAs quantum dots, which are designed for the fabrication of single-photon sources, have been studied. The effect of parameters such as the inclination angle of sidewalls, partial oxidation of AlGaAs layers, and deviation of quantum dots from the central axis of a micropillar on the Purcell factor and the radiation extraction efficiency has been numerically simulated by the finite-difference time-domain method. The allowable ranges of the listed parameters have been determined for cylindrical vertical 920-nm micropillars. The comparison of the calculations performed with the refined refractive indices of the used materials at cryogenic temperatures with the measured characteristics of the fabricated micropillar structures has confirmed the adequacy of the used models.

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Statistical Analysis of the Spatial Distribution of Mbe Grown Inas Quantum Dots on Gaas(100)

Auler,

Zolatanosha,

Reuter

2024

Preprint

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Low areal densities of InAs quantum dots on GaAs(1 0 0) prepared by molecular beam epitaxy

Cited by 3 publications

References 14 publications

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots

Simulation and Analysis of the Optical Characteristics of Cylindrical Micropillars with InAs/GaAs Quantum Dots

Statistical Analysis of the Spatial Distribution of Mbe Grown Inas Quantum Dots on Gaas(100)

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