[WP-D] Poster Session 2

[WP-D-5] Machine Learning-Based Hyperspectral Image Analysis of Emission Homogeneity of InGaAs Multiple Quantum Wells

○Yaraslau Padrez¹, Lena Golubewa^1,2, Andrea Zelioli¹, Aivaras Špokas^1,3, Bronislovas Čechavičius¹, Augustas Vaitkevičius^1,3, Evelina Dudutiene¹, Renata Butkute^1,3 (1. State research institute Center for Physical Sciences and Technology (Lithuania), 2. Institute of Chemical Physics, Vilnius University (Lithuania), 3. Institute of Photonics and Nanotechnology, Vilnius University (Lithuania))

Keywords:

Artificial intelligence and machine learning for optical network design,control,and management,Semiconductor lasers,optical amplifiers,and light emitting diodes

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.