Semiconductor ALD (Atomic Layer Deposition) is a precision-critical process involving sequential stages and large-scale time-series data from recipe settings and sensors. This paper proposes an edge AI architecture combining lightweight models on edge devices with centralized model training. The system enables early predictions from recipe data, real-time adjustments via sensor inputs, and continuous refinement using post-process outcomes. Only extracted features and result data (film thickness and uniformity) are transmitted to reduce communication overhead and protect sensitive data. The architecture supports performance monitoring and seamless model redeployment, adapting to changing equipment and environments. This approach improves product quality, reduces defect rates, and enhances manufacturing adaptability.

This paper presents the design and implementation of a system for processing and analyzing large-scale time-series data generated in semiconductor deposition processes. By adopting a real-time data collection and analysis architecture divided into Edge and Server layers, the system enables continuous retraining and updating of machine learning models based on real-time data streams. The evaluation of the model's performance demonstrates that additional training data significantly improves the model's accuracy in predicting process outcomes. Our approach not only provides a practical solution for real-time decision-making support in semiconductor manufacturing but also offers a scalable and adaptable framework applicable to various industrial sectors requiring real-time data analysis and processing. The results highlight the potential of integrating big data and artificial intelligence technologies to drive industrial innovation and optimize manufacturing processes.