Unsupervised Machine Learning Study of GaAsBi Quantum Well Evolution After Annealing Based on Spatially Resolved Micro-Photoluminescence Imaging
Unsupervised machine learning-based analysis of spatially resolved photoluminescence images of GaAsBi quantum well structures reveals non-uniformity of optical properties of as-grown samples and their gradual modification and surface homogeneity increase after annealing at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$700-750^{\circ} C$</tex>.
Authors
- Bronislovas Čechavičius (ORCID: https://orcid.org/0000-0002-7119-7213)
- Renata Butkutė (ORCID: https://orcid.org/0000-0002-1682-056X)
- A. Vaitkevičius (ORCID: https://orcid.org/0000-0003-3625-198X)
- Evelina Dudutienė (ORCID: https://orcid.org/0000-0003-1097-8531)
- Andrea Zelioli (ORCID: https://orcid.org/0000-0001-6829-3549)
- Yaraslau Padrez (ORCID: https://orcid.org/0000-0003-0852-6579)
- Lena Golubewa (ORCID: https://orcid.org/0000-0003-2125-6366)
- Aivaras Špokas (ORCID: https://orcid.org/0009-0009-9289-0285)
- Aistė Štaupienė
Institutions
- Center for Physical Sciences and Technology (LT)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11111501
- Primary Topic
- Advanced Semiconductor Detectors and Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Lietuvos Mokslo Taryba