RHEED transients during InAs quantum dot growth by MBE

Shimomura, K.; Shirasaka, T.; Tex, David M.; Yamada, Fumihiko; Kamiya, Itaru

doi:10.1116/1.3694019

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…So, we assigned the "low" label to densities below 1 × 10 10 cm −2 , the "middle" label to densities ranging from 1 × 10 10 cm −2 to 4 × 10 10 cm −2 . As the density exceeds 4 × 10 10 cm −2 , the spot features become more rounded and show higher brightness, we labeled densities exceeding 4 × 10 10 cm −2 as "high" (see Supplementary Information for RHEED characteristics of QDs with different labels, S1) [31][32][33] . Fig.…”

Section: Sample Structure and Data Labelingmentioning

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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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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Section: Sample Structure and Data Labelingmentioning

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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“…In MBE InAs/GaAs(001), the dynamic evolution of an ensemble of QDs was generally monitored in situ via either RHEED [247][248][249][250][251], the photoluminescence optical properties [252], or XRD [253]. From these experimental observations, a timescale of at least a few seconds for InAs QD growth was extracted; this was regarded as quite fast in comparison with the deposition rate (which is usually 0.1 ML/s).…”

Section: Experimental Observations Of the Timescale Of Inas Qd Growthmentioning

confidence: 99%

“…In Section 3.6, it was mentioned that the timescale for QD growth completion was estimated by techniques such as RHEED [247][248][249][250][251], photoluminescence [252], and XRD [253]. A more direct and accurate measurement method for extracting the timescale is based on snapshots obtained in neighboring regions on the growth surface using STM and ATM.…”

Section: -28mentioning

confidence: 99%

Self-assembly of InAs quantum dots on GaAs(001) by molecular beam epitaxy

Jin

2015

Front. Phys.

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Currently, the nature of self-assembly of three-dimensional epitaxial islands or quantum dots (QDs) in a lattice-mismatched heteroepitaxial growth system, such as InAs/GaAs(001) and Ge/Si(001) as fabricated by molecular beam epitaxy (MBE), is still puzzling. The purpose of this article is to discuss how the self-assembly of InAs QDs in MBE InAs/GaAs(001) should be properly understood in atomic scale. First, the conventional kinetic theories that have traditionally been used to interpret QD self-assembly in heteroepitaxial growth with a significant lattice mismatch are reviewed briefly by examining the literature of the past two decades. Second, based on their own experimental data, the authors point out that InAs QD self-assembly can proceed in distinctly different kinetic ways depending on the growth conditions and so cannot be framed within a universal kinetic theory, and, furthermore, that the process may be transient, or the time required for a QD to grow to maturity may be significantly short, which is obviously inconsistent with conventional kinetic theories. Third, the authors point out that, in all of these conventional theories, two well-established experimental observations have been overlooked: i) A large number of “floating” indium atoms are present on the growing surface in MBE InAs/GaAs(001); ii) an elastically strained InAs film on the GaAs(001) substrate should be mechanically unstable. These two well-established experimental facts may be highly relevant and should be taken into account in interpreting InAs QD formation. Finally, the authors speculate that the formation of an InAs QD is more likely to be a collective event involving a large number of both indium and arsenic atoms simultaneously or, alternatively, a morphological/structural transformation in which a single atomic InAs sheet is transformed into a three-dimensional InAs island, accompanied by the rehybridization from the sp 2-bonded to sp 3-bonded atomic configuration of both indium and arsenic elements in the heteroepitaxial growth system.

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