Accurate temperature control and effective oxide removal are essential for achieving high−quality epitaxial growth in molecular beam epitaxy (MBE). However, traditional methods often rely on manual identification of Reflection High−Energy Electron Diffraction (RHEED) patterns. This process is heavily influenced by the grower’s experience, leading to issues with reproducibility and limiting the potential for automation. In this report, we propose an unsupervised learning framework for real−time RHEED analysis during the deoxidation process. By incorporating temporal similarity constraints into contrastive learning, our model generates smooth and interpretable feature trajectories that illustrate transitions in the deoxidation state, thus eliminating the need for manual labeling. The model, pre−trained using grouped contrastive loss, shows significant improvement in RHEED feature boundary discrimination and localization of critical regions. We evaluated its generalizability through two transfer learning strategies: calibration−free clustering and few−shot fine−tuning. The pre−trained model achieved a clustering accuracy of 88.1% for GaAs deoxidation samples without additional labels and reached an accuracy of 94.3% to 95.5% after fine−tuning with just five sample pairs across GaAs, Ge, and InAs substrates. This framework is optimized for resource−constrained edge devices, allowing for real−time, plug−and−play integration with existing MBE systems and swift adaptation across various materials and equipment. This work paves the way for greater automation and improved reproducibility in semiconductor manufacturing.
Just Accepted manuscripts are peer-reviewed and accepted for publication. They are posted online prior to technical editing formatting for publication and author proofing.
High-temperature-annealed AlN (HTA-AlN) templates provide ideal substrates for high-quality AlGaN epitaxy. However, the significant compressive stress accumulated within the AlGaN layer makes it challenging to achieve a smooth surface free of hexagonal hillocks on these templates. To address this issue, we investigate the mechanism of compressive stress accumulation during the growth of AlGaN-based epilayers on HTA-AlN templates using in-situ curvature analysis in this study. To verify the mechanism, a low-Al-content AlGaN interlayer is introduced between the AlN epilayer and the subsequent AlGaN epilayer. The larger a-plane lattice constant of this interlayer relative to the AlGaN epilayer slows the accumulation rate of compressive stress. The hexagonal hillock can be effectively suppressed and the surface of AlGaN epilayer can be significantly regulated by adopting various low-Al-content AlGaN interlayers. This work provides a comprehension on the stress accumulation mechanism in AlGaN epilayers and a feasible method to obtain hillock-free surface of AlGaN epilayers on HTA-AlN templates, which will be beneficial for fabricating AlGaN based devices.
A high-speed single-mode vertical-cavity surface-emitting laser (VCSEL) is one of the most important light sources for optical interconnects in data centers. Single-mode VCSEL can improve the transmission distance. In this letter, we demonstrate a single-mode 850nm VCSEL with a bit rate of 60 Gb/s under NRZ modulation and 104 Gb/s under PAM4 modulation across a 100 m length of OM5 fiber, without the need for equalization or a filter. In addition, by using optical injection locking, the 3dB bandwidth is enhanced to 68.5 GHz.
