High-temperature-annealed Al N(HTA-Al N) templates provide ideal substrates for high-quality Al Ga N epitaxy. However, the significant compressive stress accumulated within the Al Ga N layer makes it challenging to ac...High-temperature-annealed Al N(HTA-Al N) templates provide ideal substrates for high-quality Al Ga N epitaxy. However, the significant compressive stress accumulated within the Al Ga N 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 Al Ga N-based epilayers on HTA-Al N templates using in-situ curvature analysis in this study. To verify the mechanism, a low-Al-content Al Ga N interlayer is introduced between the Al N epilayer and the subsequent Al Ga N epilayer. The larger a-plane lattice constant of this interlayer relative to the Al Ga N epilayer slows the accumulation rate of compressive stress. The hexagonal hillock can be effectively suppressed and the surface of Al Ga N epilayer can be significantly regulated by adopting various low-Al-content Al Ga N interlayers. This work provides a comprehension on the stress accumulation mechanism in Al Ga N epilayers and a feasible method to obtain hillock-free surface of Al Ga N epilayers on HTA-Al N templates,which will be beneficial for fabricating Al Ga N based devices.展开更多
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 refl...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 realtime 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.展开更多
基金supported by the National Key R&D Program of China (Grant No. 2022YFB3605000)the National Natural Science Foundation of China (Nos. 62004127, 61725403, 62121005, 61922078, 61827813, and 62004196)+1 种基金the Youth Innovation Promotion Association of Chinese Academy of Sciencesthe Youth Talent Promotion Project of the Chinese Institute of Electronics (No. 2020QNRC001)。
文摘High-temperature-annealed Al N(HTA-Al N) templates provide ideal substrates for high-quality Al Ga N epitaxy. However, the significant compressive stress accumulated within the Al Ga N 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 Al Ga N-based epilayers on HTA-Al N templates using in-situ curvature analysis in this study. To verify the mechanism, a low-Al-content Al Ga N interlayer is introduced between the Al N epilayer and the subsequent Al Ga N epilayer. The larger a-plane lattice constant of this interlayer relative to the Al Ga N epilayer slows the accumulation rate of compressive stress. The hexagonal hillock can be effectively suppressed and the surface of Al Ga N epilayer can be significantly regulated by adopting various low-Al-content Al Ga N interlayers. This work provides a comprehension on the stress accumulation mechanism in Al Ga N epilayers and a feasible method to obtain hillock-free surface of Al Ga N epilayers on HTA-Al N templates,which will be beneficial for fabricating Al Ga N based devices.
基金supported by the Beijing Natural Science Foundation(Grant Nos.F251036 and L248103)CAS Project for Young Scientists in Basic Research(Grant Nos.YSBR-090 and YSBR-05)National Natural Science Foundation of China(Grant No.62274159).
文摘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 realtime 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.
