Key Takeaways & Executive Findings
- •• An unsupervised contrastive learning framework with temporal similarity constraints enables real-time RHEED analysis during substrate deoxidation, eliminating manual labeling. • The pre-trained model achieves 88.1% clustering accuracy on GaAs deoxidation samples without additional labels, and 94.3–95.5% accuracy after fine-tuning with only five sample pairs across GaAs, Ge, and InAs. • The framework is optimized for edge devices, allowing plug-and-play integration with existing MBE systems and rapid adaptation to different materials and equipment. • This approach enhances automation and reproducibility in semiconductor manufacturing by providing smooth, interpretable feature trajectories for deoxidation state transitions.
Abstract
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.
1. Introduction
Semiconductor materials such as gallium arsenide (GaAs), germanium (Ge) and indium arsenide (InAs) are crucial for developing high-performance optoelectronic devices, including optical computing, communication, and coupling[1, 2]. Due to equipment instability and complex heterogeneous material growth, setting appropriate growth parameters is essential for high-quality fabrication[3−5]. This process typically involves precise control of substrate temperature and surface preparation, particularly the removal of native oxides before epitaxial growth.
In molecular beam epitaxy (MBE), reflection high-energy electron diffraction (RHEED) is a standard in-situ technique for monitoring surface structure and oxide desorption. However, conventional RHEED pattern analysis relies heavily on the grower's visual inspection and experience, which is subjective and hampers reproducibility and automation. To address these challenges, we propose an unsupervised learning framework that leverages contrastive learning with temporal constraints to automatically analyze RHEED patterns in real time, enabling data-efficient and reliable deoxidation monitoring.
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Yuehao Li, Chao Shen, Wenkang Zhan, Bo Xu, Yazhou Yang, Xu Zhang, Hongchang Wang, Chao Zhao, Haifang Jian (2025). Contrastive learning for data-efficient substrate deoxidation monitoring in edge-side adaptive molecular beam epitaxy systems. SinoTechIntel Verified Research. https://doi.org/10.1088/1674-4926/25070029
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Frequently Asked Questions
What is the main contribution of this paper?
The paper introduces an unsupervised contrastive learning framework for real-time RHEED analysis during substrate deoxidation in MBE, eliminating the need for manual labeling and enabling data-efficient, automated monitoring.
How does the proposed method achieve data efficiency?
By using grouped contrastive loss with temporal similarity constraints, the model learns smooth feature trajectories from unlabeled RHEED data, achieving high accuracy with minimal labeled samples (e.g., 94.3-95.5% with only five sample pairs).
What are the practical benefits of this framework?
The framework is optimized for edge devices, allowing real-time, plug-and-play integration with existing MBE systems, and can be quickly adapted to different materials and equipment, improving automation and reproducibility.
What substrates were used to evaluate the method?
The method was evaluated on GaAs, Ge, and InAs substrates, demonstrating generalizability across different material systems.
How does the method compare to traditional RHEED analysis?
Traditional methods rely on manual identification of RHEED patterns, which is subjective and experience-dependent. The proposed method automates the analysis, providing objective and reproducible monitoring of the deoxidation process.
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