Real-Time Self-Optimization of Quantum Dot Laser Emissions During Machine Learning-Assisted Epitaxy.

ABSTRACT: Traditional methods for optimizing light source emissions rely on a time-consuming trial-and-error approach. While in situ optimization of light source gain media emission during growth is ideal, it has yet to be realized. In this work, in situ reflection high-energy electron diffraction (RHEED) is integrated with machine learning (ML) to correlate the surface reconstruction with the photoluminescence (PL) of InAs/GaAs quantum dots (QDs), which serve as the active region of lasers. A lightweight ResNet-GLAM model is employed for the real-time processing of RHEED data as input, enabling effective identification of optical performance. This approach guides the dynamic optimization of growth parameters, allowing real-time feedback control to adjust the QDs emission for lasers. InAs QDs on GaA

SUBMITTER: Shen C 

PROVIDER: S-EPMC12279205 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature