In Situ Studies of Germanium-Tin and Silicon-Germanium-Tin Thermal Stability
Notice bibliographique
Résumé
GeSn and SiGeSn semiconductors provide a wealth of opportunities to enable the realization of mid-infrared photonics in group-IV semiconductors will enable on-chip CMOS optoelectronic systems with a potential impact on chemical and biological sensing, spectroscopy, and free-space communication. Moreover, the fact that these alloys are silicon-compatible will make possible the integration of group-IV-based photonics and optoelectronics with CMOS technology. Efficient group-IV-based light emitting devices and photodetectors can now be implemented using band gap engineering in GeSn and SiGeSn semiconductors, which show indirect-to-direct transition at a particular composition and strain. Besides the potential applications in photonics and optoelectronics, Sn-containing group IV semiconductor alloys and heterostructures are also highly relevant for high-mobility and low-power electronics. Additionally, the control of the composition and structure of SiGeSn alloys and heterostructures are also crucial to implement carbon-free energy conversion devices such thermoelectrics and high-efficiency solar cells. A deep understanding of the structural and morphological stability of GeSn and SiGeSn metastable alloys is of utmost importance in order to achieve the aforementioned technologies. With this perspective, we present in this contribution detailed in situ studies of the evolution throughout thermal processing of both composition and structure of set of monocrystalline binary and ternary Sn-containing group-IV alloys. The investigated layers were grown using an industry compatible metal cold-wall Reduced Pressure AIXTRON TRICENT reactor (RP-CVD) with a showerhead for 200/300mm wafers. The epitaxial layers were grown using Si 2 H 6 , Ge 2 H 6 (10% diluted in H 2 ) and SnCl 4 precursors, and N 2 carrier gas, which warrant reasonable growth rates at growth temperatures in the 350-475 °C range. The growth of GeSn and SiGeSn layers was performed on Si(100) wafers using a low-defect density Ge virtual substrate. Figure 1 displays a representative image of cross-sectional scanning transmission electron microscopy of Si 0.04 Ge 0.84 Sn 0.12 layer. The composition and strain of the grown layers were investigated using a variety of experimental techniques including Raman spectroscopy, Rutherford backscattering spectrometry, x-ray reciprocal space mapping, and energy dispersive x-ray spectroscopy. Subsequently, the as-grown layers were subjected to in situ investigations of their structural and elemental properties as a function of annealing temperatures using low energy electron microscopy (LEEM), photoelectron emission microscopy (PEEM), and nano-Auger spectroscopy. These investigations have unraveled unprecedented insights into the stability of these layers as well as into the dynamics of phase separation in Sn-rich alloys. In the latter, we have traced the formation, evolution, surface diffusion of Sn-rich droplets and clusters. We have also indentified the interplay between Sn concentration and the critical temperature that triggers the alloy instabilities in both binary and ternary layers. The effects of dislocations on the dynamics of phase separation were also identified and elucidated. A theoretical treatment including both thermodynamic and kinetic considerations was developed to discuss the observed phenomena.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».