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Enregistrement W3024389024 · doi:10.1149/ma2020-01221320mtgabs

Lattice Vibrational Modes in Epitaxial Metastable Germanium-Tin Semiconductors

2020· article· en· W3024389024 sur OpenAlexaffabout
E. Cousineau, Samik Mukherjee, Assali Simone, Jérôme Nicolas, Oussama Moutanabbir

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomainePhysics and Astronomy
ThématiqueSemiconductor Quantum Structures and Devices
Établissements canadiensPolytechnique Montréal
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceSemiconductorGermaniumBand gapRaman spectroscopyEpitaxyPhononDirect and indirect band gapsOptoelectronicsSiliconTinInfraredExcitationCondensed matter physicsOpticsNanotechnologyPhysics

Résumé

récupéré en direct d'OpenAlex

Group IV semiconductors, such as GeSn and SiGeSn, are of growing interest for silicon-compatible electronic and photonic applications, notably due to the possibility of tuning the bandgap directness and energy to cover a broad range from the short-wave infrared (SWIR) to the mid-infrared (MIR) [1]. While germanium is an indirect-bandgap semiconductor, an indirect-to-direct transition in GeSn alloys is expected for Sn incorporation higher than 9%, but the residual compressive strain typical to epitaxial layers increases the concentration of Sn to obtain a direct-bandgap [2]. Therefore, an understanding of the interplay of strain and composition on the band structure of GeSn semiconductors is of compelling importance in order to precisely control their physical properties. This calls for accurate analysis of the as-grown material properties and its correlation with the measured optical and electrical performance. Raman spectroscopy is a non-destructive method commonly used to assess the role of the effects of composition and strain in shaping the lattice vibrations. The use of a 633 nm excitation enables a clear detection of all Raman modes in GeSn layers independently of their composition, comparatively to the more restricted results associated with the use of 488 nm[3] or 532 nm [4] excitation. This is plausibly attributed to the fact that this wavelength might be close to resonance with the alloy’s E1 gap [5]. There are very few reports on the identification of the Ge-Sn mode [6] and the disorder-activated mode coinciding with the maximum in the one-phonon density of states in Ge [7], but quantitative analyses of the effects of composition and strain on these modes remain conspicuously missing in literature. With this perspective, this work presents a detailed study of Raman vibrational modes in Sn-rich GeSn semiconductors and how their behavior is affected by temperature. The use of samples of various degrees of Sn composition and in-plane compressive strain allows the decoupling of these effects on the Ge-Ge, DA and Ge-Sn vibrational modes, thus providing an effective platform to test the current predictive models. Samples were grown in a chemical vapor deposition (CVD) reactor using monogermane (GeH 4 ) and tin-tetrachloride (SnCl 4 ) precursors [8]. The GeSn multi-layer heterostuctures with a Sn content in the 4-16 at.% range were grown on a Ge virtual substrate (VS) on a Si wafer. Using X-ray diffraction (XRD), reciprocal space mappings (RSM) were performed on all samples to retrieve the Sn composition and the strain. Temperature-dependent Raman measurements were performed from room temperature (300K) and down to 77 K. In GeSn, the asymmetrical profile of the observed Raman modes is due to alloying as substitutional Sn atoms break the translational symmetry and lead to a relaxation of the momentum selection rule [9]. Therefore, the use of exponentially modified gaussian functions (EMG) as a fitting curve can reflect the asymmetrical profile of the different analyzed Raman modes with much better accuracy [10]. The measurement temperature has a clear effect on the fitting parameters, such as the integrated area of the peak , the full width at half maximum (FWHM), the asymmetry, and its position. For example, the Ge-Sn and Ge-Ge peaks increase in wavenumber with decreasing temperature, while the DA peak shows the opposite trend. Ongoing work focuses on establishing the behavior of the characteristics of each mode. Based on these detailed Raman studies, an exhaustive discussion of the influence of temperature, lattice strain and Sn content on GeSn vibrational modes will be presented. Acknowledgements The authors thank J. Bouchard for the technical support, and NSERC Canada (Discovery, SPG, and CRD Grants), Canada Research Chairs, Canada Foundation for Innovation, Mitacs. Reference [1] S. Gupta, et al., J. Appl. Phys. 113, 7 (2013). [2] A. Attiaoui and O. Moutanabbir, J. Appl. Phys. , 116, 6 (2014). [3] R. R. Lieten et al. , ECS J. Solid State Sci. Technol. , 3, 12, pp. P403–P408 (2014). [4] A. Gassenq et al. , Appl. Phys. Lett. , 110, 11, p. 112101 (2017). [5] R. Chen et al. , ECS J. Solid State Sci. Technol. , 2, 4, pp. P138–P145 (2013). [6] D. Zhang et al. , J. Alloys Compd. , 684, pp. 643–648 (2016). [7] V. D’Costa et al. , Solid State Communications , 144, 5-6, pp. 240-244 (2007). [8] S. Assali, J. Nicolas and O. Moutanabbir, Journal of Applied Physics , 125, 2, p. 025304 (2019). [9] P. Parayanthal and F. H. Pollak, Phys. Rev. Lett. , 52, 20, pp. 1822–1825 (1984). [10] É. Bouthillier et al. , “Decoupling the effects of composition and strain on the vibrational modes of GeSn”, https://arxiv.org/abs/1901.00436 , 2019.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,125
Score d'incertitude au seuil0,884

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,256
Écart entre enseignants0,229 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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