Singular value decomposition for deconvolution and physical insight into reflectance anisotropy in zincblende semiconductors

Reflectance Anisotropy Spectroscopy (RAS), also known as Reflectance Difference Spectroscopy (RDS), is an optical probe with high surface specificity for cubic semiconductors. In this paper, we demonstrate the use of Singular Value Decomposition (SVD) for the deconvolution of the RA spectrum of GaAs(001) surfaces with an As-rich reconstruction into its constitutive spectral components. SVD analysis allows the decomposition of the RA spectrum in terms of a small number of linearly independent spectra, which in turn allows the determination of their physical origin. We identify three main components: a first component that is associated with the upper atomic layers, a second component that originates from the subsurface layer that is orthorhombically strained by the reconstruction of the GaAs surface, and a third component associated with the surface roughness due to the surface steps. Moreover, these obtained SVD components are well compared with both first-principles calculations and experimental reflectance anisotropy spectra reported in the literature. The results presented here are relevant for the use of RAS/RDS as a surface probe for cubic semiconductors. In particular, for the real-time in situ monitoring of epitaxial growth of zincblende semiconductors.

Authors

Institutions

Publication Details

Journal
Journal of Applied Physics
Published
2025-03-14
DOI
https://doi.org/10.1063/5.0249884
Primary Topic
Electron and X-Ray Spectroscopy Techniques
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Singular value decomposition for deconvolution and physical insight into reflectance anisotropy in zincblende semiconductors

R. Martínez-Espinosa, Luis Felipe Lastras-Martínez, Miguel Ángel Lastras-Montaño, L. E. Guevara-Macías et al.
Journal of Applied Physics
Electron and X-Ray Spectroscopy Techniques
article

Singular value decomposition for deconvolution and physical insight into reflectance anisotropy in zincblende semiconductors

R. Martínez-Espinosa, Luis Felipe Lastras-Martínez, Miguel Ángel Lastras-Montaño, L. E. Guevara-Macías, R. E. Balderas‐Navarro, U. Zavala-Morán, A. Lastras-Martı́nez, J. Ortega‐Gallegos
article en

Abstract

Reflectance Anisotropy Spectroscopy (RAS), also known as Reflectance Difference Spectroscopy (RDS), is an optical probe with high surface specificity for cubic semiconductors. In this paper, we demonstrate the use of Singular Value Decomposition (SVD) for the deconvolution of the RA spectrum of GaAs(001) surfaces with an As-rich reconstruction into its constitutive spectral components. SVD analysis allows the decomposition of the RA spectrum in terms of a small number of linearly independent spectra, which in turn allows the determination of their physical origin. We identify three main components: a first component that is associated with the upper atomic layers, a second component that originates from the subsurface layer that is orthorhombically strained by the reconstruction of the GaAs surface, and a third component associated with the surface roughness due to the surface steps. Moreover, these obtained SVD components are well compared with both first-principles calculations and experimental reflectance anisotropy spectra reported in the literature. The results presented here are relevant for the use of RAS/RDS as a surface probe for cubic semiconductors. In particular, for the real-time in situ monitoring of epitaxial growth of zincblende semiconductors.

Journal of Applied PhysicsVol. 137(10)
Autonomous University of San Luis Potosí (MX)
Consejo Nacional de Ciencia y Tecnología
Openalex Percentile: Top 95%
Electron and X-Ray Spectroscopy Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.