Atomistic and Optical Properties of Group IV Ultrathin Superlattices
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".