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Record W3117457718 · doi:10.1149/ma2020-02241718mtgabs

(Invited) Engineering SiGeSn Semiconductors for MIR and THz Opto-electronic Devices

2020· article· en· W3117457718 on OpenAlexaff
Simone Assali, Anis Attiaoui, Mahmoud R. M. Atalla, Aashish Kumar, Samik Mukherjee, Jérôme Nicolas, Sebastian Koelling, Oussama Moutanabbir

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials scienceHeterojunctionOptoelectronicsSemiconductorPhotoluminescencePhotocurrentBand gap

Abstract

fetched live from OpenAlex

Sn-containing group IV semiconductors (Si)GeSn represent a versatile platform to implement a variety of Si-compatible photonic, optoelectronic, and photovoltaic devices operating from SWIR to MIR wavelengths. This class of semiconductors provides two degrees of freedom, strain and composition, to tailor the band structure and lattice parameter thus enabling a variety of heterostructures and low-dimensional systems on a Si substrate.[1] In this presentation, the recent progress in the epitaxial growth and opto-electronic properties of metastable (Si)GeSn semiconductors with Sn contents above 15 at.% will be discussed. The growth of the (Si)GeSn multi-layer heterostructure is performed in a chemical vapor deposition (CVD) reactor on a Si wafer. By reducing the growth temperature, the Sn content in the alloy is increased, while preserving a high degree of crystal purity for the heterostructure in the topmost Sn-rich layer. Atom probe tomography (APT) measurements will be discussed to address the abruptness of the interfaces and the compositional profile across the GeSn multi-layer heterostructure.[2-4] By tailoring the strain relaxation in Ge0.83Sn0.17 room-temperature photoluminescence (PL) emission wavelength above 4.0 μm upon is achieved.[2,5] The absence of defect- and impurity-related emission and limited carrier losses into non-radiative recombination channels will be addressed using temperature-dependent PL measurements. Direct band gap absorption will be shown using transmission measurements performed at room-temperature, with energies closely matching the PL data. These observations will be discussed in the light of photocurrent measurements on GeSn photodetectors operating up to ~4.6 μm at room-temperature. The integration of p-i-n heterostructures in GeSn photodetectors will be investigated by correlating structural and opto-electronics properties of the fabricated devices. In addition, strategies to further extend the operational wavelength range of (Si)GeSn devices toward THz wavelengths will be discussed. [1] S. Wirths, et al., Prog. Cryst. Growth Charact. Mater. 62, 1 (2016) [2] S. Assali, et al., Appl. Phys. Lett. 112, 251903 (2018). [3] S. Assali, et al., J. Appl. Phys. 125, 025304 (2019). [4] S. Assali, et al., Appl. Phys. Lett. 114, 251907 (2019) [5] S. Assali, et al., arxiv:2004.13858

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0490.024

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 head0.013
GPT teacher head0.212
Teacher spread0.200 · 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
GenreMethods

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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