arXiv subjects

Wenkang Zhan

Publications and source records attributed to Wenkang Zhan.

6 recordsLinked to original sources

arXiv2025

On-Demand Growth of Semiconductor Heterostructures Guided by Physics-Informed Machine Learning

Developing tailored semiconductor heterostructures on demand represents a critical capability for addressing the escalating performance demands in electronic and optoelectronic devices. However, traditional fabrication methods remain constrained by simulation-based design and iterative trial-and-error optimization. Here, we introduce SemiEpi, a self-driving platform designed for molecular beam epitaxy (MBE) to perform multi-step semiconductor heterostructure growth through in-situ monitoring and on-the-fly feedback control. By integrating standard MBE reactors, physics-informed machine learning (ML) models, and parameter initialization, SemiEpi identifies optimal initial conditions and proposes experiments for heterostructure growth, eliminating the need for extensive expertise in MBE processes. As a proof of concept, we demonstrate the optimization of high-density InAs quantum dot (QD) growth with a target emission wavelength of 1240 nm, showcasing the power of SemiEpi. We achieve a QD density of 5 x 10^10 cm^-2, a 1.6-fold increase in photoluminescence (PL) intensity, and a reduced full width at half maximum (FWHM) of 29.13 meV, leveraging in-situ reflective high-energy electron diffraction monitoring with feedback control for adjusting growth temperatures. Taken together, our results highlight the potential of ML-guided systems to address challenges in multi-step heterostructure growth, facilitate the development of a hardware-independent framework, and enhance process repeatability and stability, even without exhaustive knowledge of growth parameters.

cond-mat.mtrl-sci↗

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↗

arXiv2024

In-situ Self-optimization of Quantum Dot Emission for Lasers by Machine-Learning Assisted Epitaxy

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, we integrate in-situ reflection high-energy electron diffraction (RHEED) 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. We successfully optimized InAs QDs on GaAs substrates, with a 3.2-fold increase in PL intensity and a reduction in full width at half maximum (FWHM) from 36.69 meV to 28.17 meV under initially suboptimal growth conditions. Our automated, in-situ self-optimized lasers with 5-layer InAs QDs achieved electrically pumped continuous-wave operation at 1240 nm with a low threshold current of 150 A/cm2 at room temperature, an excellent performance comparable to samples grown through traditional manual multi-parameter optimization methods. These results mark a significant step toward intelligent, low-cost, and reproductive light emitters production.

cond-mat.mes-hall↗

arXiv2024

Epitaxial growth of high-quality GaAs on Si(001) using ultrathin buffer layers

The direct growth of III-V semiconductors on silicon holds tremendous potential for photonics applications. However, the inherent differences in their properties lead to defects in the epitaxial layer, including threading dislocations (TDs), antiphase boundaries (APBs), and thermal cracks, significantly impacting device performance. Current processes struggle to suppress these defects simultaneously, necessitating the development of methods to inhibit TDs and APBs in a thin buffer on silicon. This study introduces a GaSb buffer layer during GaAs epitaxy on a silicon (001) substrate. This approach successfully suppresses defect formation by promoting the formation of interfacial misfit dislocation (IMF) arrays at both the AlSb/Si and GaAs/GaSb interfaces. The resulting GaAs layer exhibits a step-flow surface with a rough mean square of approximately 3.8 nm and a full width at half maximum of 158 arcsec. Remarkably, the growth is achieved without any observable interfacial intermixing. Building on this platform, InAs/GaAs quantum dots (QDs) are grown with a density of 3.8E10/cm2, emitting at a wavelength of 1288 nm. This breakthrough holds immense promise for developing high-quality GaAs films with reduced defect densities on silicon for O band lasers, laying the foundation for the mass production of silicon-based integrated circuits.

cond-mat.mtrl-sci↗

arXiv2023

Universal Deoxidation of Semiconductor Substrates Assisted by Machine-Learning and Real-Time-Feedback-Control

Thin film deposition is an essential step in the semiconductor process. During preparation or loading, the substrate is exposed to the air unavoidably, which has motivated studies of the process control to remove the surface oxide before thin film deposition. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for a random substrate is a multidimensional challenge and sometimes controversial. Due to variations in semiconductor materials and growth processes, the determination of substrate deoxidation temperature is highly dependent on the grower's expertise; the same substrate may yield inconsistent results when evaluated by different growers. Here, we employ a machine learning (ML) hybrid convolution and vision transformer (CNN-ViT) model. This model utilizes reflection high-energy electron diffraction (RHEED) video as input to determine the deoxidation status of the substrate as output, enabling automated substrate deoxidation under a controlled architecture. This also extends to the successful application of deoxidation processes on other substrates. Furthermore, we showcase the potential of models trained on data from a single MBE equipment to achieve high-accuracy deployment on other equipment. In contrast to traditional methods, our approach holds exceptional practical value. It standardizes deoxidation temperatures across various equipment and substrate materials, advancing the standardization research process in semiconductor preparation, a significant milestone in thin film growth technology. The concepts and methods demonstrated in this work are anticipated to revolutionize semiconductor manufacturing in optoelectronics and microelectronics industries by applying them to diverse material growth processes.

cond-mat.mes-hall↗

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↗