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Record W4255353982 · doi:10.1149/ma2014-02/35/1843

Optical and Electronic Propreties of GeSn and GeSiSn Heterostructures and Nanowires

2014· article· en· W4255353982 on OpenAlexaff
Anis Attiaoui, Oussama Moutanabbir

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsHeterojunctionMaterials scienceTernary operationSuperlatticeBand gapSemiconductorMonolayerDirect and indirect band gapsLattice constantSubstrate (aquarium)AlloyCondensed matter physicsOptoelectronicsNanotechnologyDiffractionComputer scienceOpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

Sn-containing group-IV semiconductor binary and ternary alloys (GeSn, SiSn, and GeSiSn) provide a wealth of opportunities to independently manipulate band gap and lattice parameter. This opens up the possibility to create an entirely new family of electronic and optoelectronic devices. Towards this end, it is of paramount importance to develop a deep understanding of the electronic and optical properties of these group-IV alloys-based heterostructures, nanoscale structures, and devices. In this perspective, this work reports detailed studies of the influence of both strain and composition on the band structure of GeSiSn ternary alloys. First, we developed a simple yet rigorous semi-empirical second nearest neighbors tight binding1 sp3s*method that incorporates the effect of substitutional disorder. We have found that the composition of α-Sn at the direct to indirect crossover of the ternary alloy decreases from 11% in a fully relaxed alloy to 7% in tensile strained alloy (for a strain value of 0.71%). In the latter case, we have considered a thin GeSiSn layer is epitaxialy grown on a thin Ge substrate. Furthermore, we also studied the properties of electronic confinement and optical responses in variety of systems. This includes Ge/GeSn, Ge/GeSiSn and GeSn/GeSiSn heterostructures, where the strain effect is incorporated using the second nearest neighbors sp3s* semi-empirical tight binding model.1 - 2 The second set of the investigated systems consists of superlattices3 (,), where n and m are the number of monolayer considered. The compositions of Si and Sn in the ternary alloy vary in the range of 0-40% and 0-20%, respectively. The effect of growth orientations are also treated by focusing on three crystallographic growth orientations; mainly [001], [110] and [111]. The strain effect was also considered in these cases. Moreover, the effect of spatial charges separation on the intersubband and interband transitions in the superlattice, is treated in order to quantify optical properties of theses superlattices. The last section of this work addresses the behavior of GeSn and GeSiSn semiconductor nanowires. Herein, we have solved the effective mass Hamiltonian in cylindrical coordinates using a finite difference technique4 for the core-shell nanowires where the core and the shell are made of different alloys. In figure I. (b), a schematic representation of one of the investigated nanowire heterostructures consisting of a radial Type I junction of a compressively strained GeSn core and tensile strained GeSiSn shell. Moreover, in order to quantify the effect of doping concentration and the radius of the core on the spatial localization of electrons and holes densities inside the nanowire, we present in Figure I. (a) the calculated effect of the doping concentration on the electronic confinement for a Ge0.90Sn0.10/Ge core-shell nanowire for different core radiuses in the 10-40 nm range. In the case of relaxed nanowire heterostructures, we show that for an indirect band gap Ge0.9Sn0.1 binary alloy localized at L symmetry point, above a doping concentration of 5×1016 cm-3and below a core radius of 20 nm, the electron density is localized in the Ge shell. Above this concentration, electrons are confined in the core. This change in localization of electron in core/shell nanowire, corresponds to a transition where the electron gas in the middle of the core becomes strongly localized in the heterojunction core/shell interface or beyond. Based on these calculations, we will present and discuss a variety of novel electronic and optoelectronic devices. References: 1 P. Vogl, H.P. Hjalmarson, and J.D. Dow, J. Phys. Chem. Solids 44, 365 (1983). 2 T.B. Boykin, G. Klimeck, R.C. Bowen, and R. Lake, Phys. Rev. B 56, 4102 (1997). 3 D. Smith and C. Mailhiot, Rev. Mod. Phys. 62, 173 (1990). 4 L.R. Ram-Mohan, J. Appl. Phys. 95, 3081 (2004). 5 C.G. Van de Walle, Phys. Rev. B 39, 1871 (1989).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.191
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes1
Has abstractyes

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