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Enregistrement W3024292500 · doi:10.1149/ma2020-01221306mtgabs

Germanium Tin Surface Passivation and Its Effect on the Optoelectronic Performances

2020· article· en· W3024292500 sur OpenAlexaff
Léonor Groell, Salim Abdi, Simone Assali, Mahmoud R. M. Atalla, Anis Attiaoui, 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-clésPassivationMaterials scienceBand gapSemiconductorOptoelectronicsGermaniumDangling bondHeterojunctionSiliconNanotechnologyWafer bondingDirect and indirect band gapsQuantum tunnellingEngineering physicsLayer (electronics)

Résumé

récupéré en direct d'OpenAlex

Germanium Tin is an emerging semiconductor, with high carrier mobility and tunable bandgap directness and energy, that has been attracting a great deal of interest for applications in silicon-compatible electronics and monolithic optoelectronics. In contrast to compound semiconductors which have in the limelight to address several challenges in these technologies, this new emerging family of silicon-compatible group IV semiconductors holds the promise to combine both cost effectiveness and performance in several devices such as tunnelling field effect transistors, infrared detectors and emitters. By tuning strain and composition of GeSn, the band structure can be modified, thus allowing to engineer a large variety of low-dimensional heterostructures relevant to these devices. In fact, even though Ge is an indirect bandgap material, the incorporation of Sn in its lattice allows a transition into a direct bandgap material. Thus, GeSn yields to higher rate of radiative transitions and band-to-band tunneling. However, there are still several outstanding challenges at the materials level that must be overcome before harnessing GeSn advantageous properties. For instance, effective processes to effectively passivate its surface at various Sn composition are yet to be established. It is known that the surface of a semiconductor contains electronically active states because of unsaturated surface bonds or dangling bonds states, which act as localized energy levels the band gap that can change the intrinsic electrical behavior of the material. Therefore, understanding and controlling the surface states are of compelling importance. Moreover, the native oxide layer that forms at the surface of GeSn contains defects resulting in trapping charge carriers thus decreasing their mobility. The poor quality of the surface can decrease the rate of radiative transitions and contribute to the dark current. An effective passivation is thus needed to enhance the optoelectronic performances of GeSn. Passivation layer allows improved charge-separation, reduces charge recombination at surface states, passivates the dangling bonds and decreases the reactivity of the surface. To address these issues, this work investigates several chemical passivation processes and evaluate their effects on the optoelectronic properties of GeSn. The GeSn samples investigated in this work were grown on silicon wafers using ~0.6-3 µm-thick Ge interlay – commonly known as virtual substrates (Ge-VS). The epitaxial growth was carried out by the chemical vapor deposition (CVD) using monogermane (GeH 4 ) and tin-tetrachloride (SnCl 4 ) precursors. Strain minimization and the reduced growth temperature below 350 °C are of paramount importance to enhance Sn incorporation in Ge lattice to reach compositions of 7 – 17 at.%, much larger than the equilibrium composition of 1at.%. Several passivation treatments were evaluated and their basic mechanisms were elucidated. We first evaluated the capacity to remove the native oxide, leave the surface clean and passivate the dangling bonds of GeSn. The kinetic of oxide regrowth was studied to assess the chemical stability of the passivation through X-rays photoelectron spectroscopy (XPS) analysis. For instance, for a surface treatment consisting of a dip in HF followed by a dip in (NH 4 ) 2 S and then by a nitride drying. XPS measurements show that it is effective in cleaning and passivating the surface, it allows removing the major part of Ge and Sn oxides. Recorded spectra reveal that this treatment gives rise to a persistent sulfur bonds at 161.8 eV, which is the signature of sulphide compounds. Sulfur allows surface passivation and slows down the oxide regrowth. Moreover, alternative surface passivation processes will also be discussed, and their performances compared to the treatment above will be elucidated. Besides, to better understand the evolution of the surface state after the treatments, ellipsometric measurements combined with atomic force microscopy studies are conducted and will be presented. Also, complementary electrical measurements and photoluminescence emission studies will also be discussed to highlight the effectiveness of each treatment in improving the optoelectronic performances of GeSn.

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 enseignants0,132
Score d'incertitude au seuil0,397

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)0,0000,000

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 Opus0,015
Tête enseignante GPT0,239
Écart entre enseignants0,225 · 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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