Growth of type I superlattice III-V heterostructure in horizontal nanowires enclosed in a silicon oxide template

Brugnolotto, Enrico; Scherrer, Markus; Schmid, Heinz; Georgiev, Vihar P.; Sousa, Marilyne

doi:10.1016/j.jcrysgro.2022.127015

Cited by 4 publications

(12 citation statements)

References 22 publications

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“…This is achieved by setting a growth condition favoring slow growth along the main ⟨1 1 1⟩ direction and fast growth in the ⟨1 1 0⟩ directions. 28 Under these conditions, efficient step flow occurs on the {1 1 1} facets. In such a configuration, the first wire to extend into the widening template section will spill over the spacers separating it from the neighboring wires and find itself supported on the other wire's {1 1 1} surface.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Figure schematically illustrates the recipes employed during the growth of the two studied samples. The first three growth steps are common to all samples: a baking native oxide desorption step takes place in an As-rich atmosphere, followed by a short InGaAs nucleation step and a further InP growth step designed to stabilize a single {1 1 1} B facet as the growth front . V/III ratios and In/(In+Ga) fractions for every material are reported in Table .…”

Section: Methodsmentioning

confidence: 99%

“…The first three growth steps are common to all samples: a baking native oxide desorption step takes place in an As-rich atmosphere, followed by a short InGaAs nucleation step and a further InP growth step designed to stabilize a single {1 1 1} B facet as the growth front. 28 V/III ratios and In/(In +Ga) fractions for every material are reported in Table 1. An InP-lattice-matched composition (In 0.53 Ga 0.47 As) was targeted for the InGaAs segments.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

“…To improve heterointerface definition, a purge or hold step is included after each of the layers. 28 A long InP segment separates the two looped segments in the recipe for sample 1 to highlight any changes in growth rates given by the proximity to the template opening. The superlattice of sample 2 incorporates InAs, InGaAs, and GaAs quantum wells.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

“…Typically, III–V growth on Si {0 0 1} shows a multifaceted growth front, which can lead to compositional and thickness variations , due to differences in growth rates of different crystalline planes. Multifaceted growth can be suppressed by growing on Si (1 1 0) along a ⟨1 1 1⟩ direction, , and single-facet heterointerface formation was also described in homoepitaxy on InP (1 1 0) along a ⟨1 1 0⟩ direction, resulting in well-defined quantum well sections. , …”

Section: Introductionmentioning

confidence: 99%

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In-Plane III–V Nanowires on Si(1 1 0) with Quantum Wells by Selective Epitaxy in Templates

Brugnolotto,

Schmid,

Georgiev

et al. 2023

Crystal Growth & Design

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Heterointerfaces and quantum wells were realized in III–V nanowires monolithically grown from a silicon seed inside a silicon oxide template on a silicon-on-insulator wafer. InP, InGaAs, InAs, and GaAs were grown by metal-organic chemical vapor deposition. The yield of nanowires with a well-defined single-facet {1 1 1} B growth front was assessed using scanning electron microscopy, reaching 92.55%. Scanning transmission electron microscopy and energy-dispersive X-ray spectroscopy analysis revealed good composition control and the formation of sharp single-faceted quantum wells throughout the sample. The robustness of the process was further demonstrated by a forced merger of individual nanowires into one large crystal. In this sample, the quantum wells were as well defined as those in the single-seed nanowires. At the same time, we did not observe the formation of any dislocations at the merge location.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: ■ Experimental Sectionmentioning

confidence: 99%

Section: ■ Experimental Sectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-Plane III–V Nanowires on Si(1 1 0) with Quantum Wells by Selective Epitaxy in Templates

Brugnolotto,

Schmid,

Georgiev

et al. 2023

Crystal Growth & Design

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InP Nanowire Array Laterally Grown on (001) SOI

Chen,

Ren,

Zhao

et al. 2024

2024 IEEE Photonics Conference (IPC)

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Machine Learning Inspired Nanowire Classification Method based on Nanowire Array Scanning Electron Microscope Images

Brugnolotto,

Aleksandrov,

Sousa

et al. 2024

Open Res Europe

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Background This article introduces an innovative classification methodology to identify nanowires within scanning electron microscope images. Methods Our approach employs advanced image manipulation techniques in conjunction with machine learning-based recognition algorithms. The effectiveness of our proposed method is demonstrated through its application to the categorization of scanning electron microscopy images depicting nanowires arrays. Results The method’s capability to isolate and distinguish individual nanowires within an array is the primary factor in the observed accuracy. The foundational data set for model training comprises scanning electron microscopy images featuring 240 III-V nanowire arrays grown with metal organic chemical vapor deposition on silicon substrates. Each of these arrays consists of 66 nanowires. The results underscore the model’s proficiency in discerning distinct wire configurations and detecting parasitic crystals. Our approach yields an average F1 score of 0.91, indicating high precision and recall. Conclusions Such a high level of performance and accuracy of ML methods demonstrate the viability of our technique not only for academic but also for practical commercial implementation and usage.

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Growth of type I superlattice III-V heterostructure in horizontal nanowires enclosed in a silicon oxide template

Cited by 4 publications

References 22 publications

In-Plane III–V Nanowires on Si(1 1 0) with Quantum Wells by Selective Epitaxy in Templates

In-Plane III–V Nanowires on Si(1 1 0) with Quantum Wells by Selective Epitaxy in Templates

InP Nanowire Array Laterally Grown on (001) SOI

Machine Learning Inspired Nanowire Classification Method based on Nanowire Array Scanning Electron Microscope Images

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