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Record W4401879224 · doi:10.1109/jstqe.2024.3450302

Transfer-Printed Multiple GeSn Membrane Mid-Infrared Photodetectors

2024· article· en· W4401879224 on OpenAlexafffund
Cédric Lemieux‐Leduc, Mahmoud R. M. Atalla, Simone Assali, Sebastian Koelling, Patrick Daoust, Lu Luo, Gérard Daligou, Julien Brodeur, Stéphane Kéna‐Cohen, Yves-Alain Peter, Oussama Moutanabbir

Bibliographic record

VenueIEEE Journal of Selected Topics in Quantum Electronics · 2024
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersArmy Research OfficeMitacsAir Force Office of Scientific ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsPhotodetectorOptoelectronicsInfraredMaterials scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Due to their narrow band gap and compatibility with silicon processing, germanium-tin (Ge$_{1-x}$Sn$_{x}$) alloys are a versatile platform for scalable integrated mid-infrared photonics. These semiconductors are typically grown on silicon wafers using Ge as an interlayer. However, the large lattice mismatch in this heteroepitaxy protocol leads to the build-up of compressive strain in the grown layers. This compressive strain limits the material quality and its thermal stability besides expanding the band gap, thereby increasing the Sn content needed to cover a broader range in the mid-infrared. Released Ge$_{1-x}$Sn$_{x}$membranes provide an effective way to mitigate these harmful effects of the epitaxial strain and control the band gap energy while enabling the hybrid integration onto different substrates. Nevertheless, the epitaxial strain is also known to affect the fabrication of membrane devices due to a significant bowing upon release from the growth substrate, especially in high Sn content structures. With this perspective, herein these limitations are discussed and addressed by introducing bow-free, strain-relaxed Ge$_{1-x}$Sn$_{x}$membranes in the fabrication of mid-infrared devices. These devices are transfer-printed with metal contacts to create multiple photodetectors in a single transfer step. The resulting photodetectors exhibit an extended photodetection cutoff reaching a wavelength of$3.1 \,\mu$m for a Sn content of${x=0.11}$compared to as-grown photoconductive devices. The latter yields a reduced cutoff of$2.8 \,\mu$m due to the inherent compressive strain. Additionally, a significant reduction in the dark current of two orders of magnitude is observed, which could be related to the formation of a Schottky barrier or to a change in the contact resistivity during the processing steps of the membranes. Furthermore, the impact of chemical treatment and annealing on the device performance was also investigated showing a further reduction in the dark current. The demonstrated transfer printing, along with the use of an adhesive layer, allows the transfer of multiple GeSn membranes onto virtually any substrate. This approach paves the way for scalable fabrication of hybrid optoelectronic devices leveraging the tunable band gap of Ge$_{1-x}$Sn$_{x}$in the mid-infrared range.

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 score0.008
Threshold uncertainty score0.026

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.010
GPT teacher head0.227
Teacher spread0.217 · 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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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