Machine-learning-assisted and real-time-feedback-controlled growth of InAs/GaAs quantum dots.

ABSTRACT: The applications of self-assembled InAs/GaAs quantum dots (QDs) for lasers and single photon sources strongly rely on their density and quality. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Here, we report a real-time feedback control method to realize the growth of QDs with arbitrary density, which is fully automated and intelligent. We develop a machine learning (ML) model named 3D ResNet 50 trained using reflection high-energy electron diffraction (RHEED) videos as input instead of static images and providing real-time feedback on surface morphologies for process control. As a result, we demonstrate that ML from previo

SUBMITTER: Shen C 

PROVIDER: S-EPMC10980817 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature