arXiv subjects

Manyang Li

Publications and source records attributed to Manyang Li.

2 recordsLinked to original sources

arXiv2024

Enhanced Radiation Hardness of InAs/GaAs Quantum Dot Lasers for Space Communication

Semiconductor lasers have great potential for space laser communication. However, excessive radiation in space can cause laser failure. In principle, quantum dot (QD) lasers are more radiation-resistant than traditional semiconductor lasers because of their superior carrier confinement and smaller active regions. However, the multifaceted nature of radiation effects on QDs resulted in ongoing controversies. Comprehensive testing under simulated space conditions is also necessary to validate their performance. In this work, we conducted radiation tests on various In(Ga)As/GaAs QD and quantum well (QW) materials and devices. Our results revealed that InAs/GaAs QDs with filling factors greater than 50% exhibit greater radiation hardness than those below 50%. Furthermore, most InAs/GaAs QDs showed superior radiation resistance compared to InGaAs/GaAs QW when exposed to low proton fluences of 1E11 and 1E12 cm-2, resulting from radiation-induced defects. The linewidth enhancement factor (LEF) of well-designed QD lasers remains remarkably stable and close to zero, even under proton irradiation at a maximum fluence of 7E13 cm-2, owing to their inherent insensitivity to irradiation-induced defects. These QD lasers demonstrate an exceptional average relative intensity noise (RIN) level of -162 dB/Hz, with only a 1 dB/Hz increase in RIN observed at the highest fluence, indicating outstanding stability. Furthermore, the lasers exhibit remarkable robustness against optical feedback, sustaining stable performance even under a feedback strength as high as -3.1 dB. These results highlight the significant potential of QD lasers for space laser communication applications, where high reliability and resilience to radiation and environmental perturbations are critical.

physics.app-ph↗

arXiv2023

Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applications strongly rely on the density and quality of these dots, which has motivated studies of the growth process control to realize high-quality epi-wafers and devices. 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 developed 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 demonstrated that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5E10 cm-2 down to 3.8E8 cm-2 or up to 1.4E11 cm-2. Compared to traditional methods, our approach, with in situ tuning capabilities and excellent reliability, can dramatically expedite the material optimization process and improve the reproducibility of MBE, constituting significant progress for thin film growth techniques. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for optoelectronic and microelectronic industries.

cond-mat.mes-hall↗