Machine Learning-Based Hyperspectral Image Analysis of Emission Homogeneity of InGaAs Multiple Quantum Wells
Machine learning based analysis of hyperspectral images of the emission of InGaAs multiple quantum wells reveals the specific spectral features describing at least one type of quantum structure defects, which are likely to be dislocations.
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)
Institutions
- Center for Physical Sciences and Technology (LT)
Publication Details
- Published
- 2025-06-29
- DOI
- https://doi.org/10.23919/oecc/psc62146.2025.11110600
- Primary Topic
- Advanced Semiconductor Detectors and Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00