Atomistic and Optical Properties of Group IV Ultrathin Superlattices
Notice bibliographique
Résumé
Engineering atomically abrupt interfaces between multi-layered thin films of different material have yielded some significant fundamental and technological breakthroughs in the past. For example, the Nobel Prize winning integral and quantum Hall effect owes its discovery to abrupt semiconductor interfaces grown by molecular beam epitaxy. On the other hand, the functionalization of many modern-day semiconductor devices depend critically on the quality of interface of the various thin films, which constitute the device. Silicon (Si)/Germanium (Ge) and Si/SiGe based materials have emerged in the recent years as an integral part to the Si semiconductor industry, promising a wide range of devices ranging from Fin-field effect transistors, quantum cascade lasers, quantum dots, near and mid-infrared photodetectors, resonant tunnel diodes, to name a few.1–3 To this end, a continuous feedback from post-growth characterization techniques elucidating the intermixing of atoms at the interface, the interfacial width and roughness, the defect density at the interfaces as a function of the growth conditions and integration parameters is therefore crucial for optimizing the ultimate performance of these devices. Not surprising that the semiconductor industry is directing a significant amount of its resources and efforts towards enriching our current understanding of the buried interfaces of these materials. In this work, we use a combination of scanning transmission electron microscopy (STEM) and atom probe tomography (APT) to present an atomistic understanding of the nature of interfaces in Si/Ge and Si/SiGe superlattices, grown on 300 mm Si wafers using chemical vapor deposition. The APT investigations were done using the state-of-the-art LEAP 5000 providing a supreme mass sensitivity and a detection efficiency of 80%, a 30% improvement compared to its predecessor. Figure 1(a) shows the STEM-APT of the last six bi-layers of a sixteen period Si/SiGe superlattice. Figure 1(b) shows the one-dimensional (1D) concentration profile obtained from the 3D APT reconstruction of the sample along the growth (analysis) direction. While the STEM data reveals the average Si and SiGe layer thickness to be about 1.3 nm and 2.2 nm respectively, the concentration profile from the APT data reveals the Ge content within the SiGe layers to be 25.0 +/- 0.5 at.%. The average interfacial width, evaluated as the separation between the 10.0 at.% and 90.0 at.% Ge concentration points, revealed the Si to SiGe transition to be about 1.4 +/- 0.3 nm wide while the width of the SiGe to Si transition to be narrower than 1.0 nm. The possible role of Ge diffusion on the observed interfacial thicknesses shall be discussed in details in this work. Figure 1(c) shows the Si/SiGe hetero-interfaces drawn as isoconcentration surfaces at 12.0 at.% Ge concentration. The work shall also highlight the results obtained from the STEM-APT measurements on superlattices of different period lengths, layer thicknesses and growth conditions from that shown in Figure 1. Alongside the structural aspects, the vibrational properties of these superlattices as obtained from Raman spectroscopic measurements and optical properties as obtained from the ellipsometry and absorption measurements shall be highlighted. Such superlattices holds tremendous prospect for Si semiconductor industry wherein a magic sequence of the number of monolayers within each period can render the whole superlattice to be direct gap4 for optoelectronic applications or an array of ultrathin free-standing nanowires could be fabricated by selective etching of the Ge layers in a Si/Ge superlattice for nanoelectronic applications. (1) Tan, K.-M.; Liow, T.-Y.; Lee, R. T. P.; Hoe, K. M.; Tung, C.-H.; Balasubramanian, N.; Samudra, G. S.; Yeo, Y.-C. IEEE Electron Device Lett. 2007, 28 (10), 905–908. (2) Dehlinger, G.; Diehl, L.; Gennser, U.; Sigg, H.; Faist, J.; Ensslin, K.; Grutzmacher, D.; Muller, E. Science 2000, 290 (5500), 2277–2280. (3) Maune, B. M.; Borselli, M. G.; Huang, B.; Ladd, T. D.; Deelman, P. W.; Holabird, K. S.; Kiselev, A. A.; Alvarado-Rodriguez, I.; Ross, R. S.; Schmitz, A. E.; Sokolich, M.; Watson, C. A.; Gyure, M. F.; Hunter, A. T. Nature 2012, 481 (7381), 344–347. (4) d’Avezac, M.; Luo, J.-W.; Chanier, T.; Zunger, A. Phys. Rev. Lett. 2012, 108 (2), 27401. Figure 1
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».