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Record W4245505532 · doi:10.1149/ma2018-02/11/621

Determining Strain, Chemical Composition, and Thermal Properties of Si/SiGe Nanostructures Via Raman Scattering Spectroscopy

2018· article· en· W4245505532 on OpenAlexaff
L. Tsybeskov, Selina A. Mala, Xaolu Wang, J.‐M. Baribeau, Xiaohua Wu, D. J. Lockwood

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsPhononBrillouin zoneRaman spectroscopyWave vectorRaman scatteringCondensed matter physicsSuperlatticeMaterials scienceX-ray Raman scatteringScatteringBrillouin scatteringPhysicsOpticsLaser

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.232
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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