Determining Strain, Chemical Composition, and Thermal Properties of Si/SiGe Nanostructures Via Raman Scattering Spectroscopy
Bibliographic record
Abstract
Raman scattering by phonons has been used for several decades to obtain information about the structural and electronic properties of various Si/SiGe nanostructures including quantum wells, superlattices, and dot/cluster multilayers [1–5]. A Raman scattering event arising from lattice vibrations (or phonons) in a semiconductor is described as the interaction of incoming light (or photon) of frequency ωi and wavevector qi with a phonon of frequency ωp and wavevector qp to produce scattered light of frequency ωs and wavevector qs. The scattering process is required to satisfy energy and momentum conservation (viz., ωs = ωi ± ωp). Since the wavevector of visible light is relatively small, Raman scattering involves only phonons with energies close to the center of the unit-cell Brillouin zone, where acoustic phonons have much smaller energies compared to those of optical phonons. As compared to the usual bulk acoustic phonons, mini Brillouin-zone folded acoustic phonons appear specifically in periodic structures such as superlattices and produce additional peaks in inelastic light scattering at wavenumbers between bulk acoustic phonons and optical phonons [6–8] and that are typically observed at wavenumbers less than 100 cm-1. In Si/SiGe nanostructures, as is also found in bulk SiGe alloys, the Raman spectrum comprises three major bands with a Si-like (Si) peak at ~ 520 cm-1, an alloy-like (SiGe) peak at ~ 400 cm-1, and a Ge-like (Ge) peak at ~ 300 cm-1 [9, 10], with other weaker Raman features located between the major peaks. The presence of highly-disordered (or amorphous) Si, SiGe, and Ge inclusions in a sample can be observed through the presence of broader, but otherwise similar, Raman features. These Raman peaks exhibit various dependencies on strain, temperature and chemical composition. Experiments involving Raman thermometry require a comparison of the Stokes (positive frequency shift) and anti-Stokes (negative frequency shift) Raman peak intensities. The optical polarization dependence of the Raman scattering intensity is defined by the Raman scattering tensors. In Si/SiGe nanostructures, this technique can be used to detect various imperfections in epitaxially grown samples, including inhomogeneous strain. Results obtained from inelastic light scattering spectroscopy investigations employing first- and second-order Raman scattering, polarized Raman scattering, and low-frequency light scattering associated with folded acoustic phonons of Si/SiGe nanostructures comprised of either planar superlattices or cluster (SiGe dot) multilayers separated by Si layers are used for analyzing the chemical composition, strain, and thermal conductivity in such technologically important materials as these for electronic and optoelectronic devices. References [1] F. Cerdeira, A. Pinczuk, J.C. Bean ,B. Batlogg, and B.A. Wilson, Appl. Phys. Lett. 45, 1138 (1984). [2] J.L. Liu, Y.S. Tang, K.L. Wang, T. Radetic, and R. Gronsky, Appl. Phys. Lett.74, 1863 (1999). [3] E.G. Barbagiovanni, D.J. Lockwood, P.J. Simpson, and L.V. Goncharova, J. Appl. Phys. 111, 034307 (2012). [4] J. Menéndez, A. Pinczuk, J. Bevk, and J.P. Mannaerts, J. Vac. Sci. Technol. B 6 1306 (1988). [5] B.V. Kamenev, L. Tsybeskov, J.-M. Baribeau, and D.J. Lockwood, Appl. Phys. Lett. 84 1293 (2004). [6] M. Cardona and P. Yu, Fundamentals of Semiconductors, Springer-Verlag, Berlin, Heidelberg (2005), p. 619. [7] M.I. Alonso and K. Winer, Phys. Rev. B 39, 10056 (1989). [8] P.M. Mooney, F.H. Dacol, J.C. Tsang, and J.O.Chu, Appl. Phys. Lett. 62, 2069 (1993). [9] S.A. Mala, L. Tsybeskov, D.J. Lockwood, X. Wu, and J.-M. Baribeau, J. Appl. Phys. 116, 014305 (2014). [10] F. Cerdeira, M.I. Alonso, D. Niles, M. Garriga, M. Cardona, E. Kasper, and H. Kibbel, Phys. Rev. B 40, 1361 (1989).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".