Work

Posters: Edge AI-Based Integrated Model Architecture for Optimization of Semiconductor ALD Processes

Year: 2025

Type: conference-paper

Abstract: 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.

Source: International Conference on Mobile Systems, Applications, and Services

DOI: 10.1145/3711875.3734557

Authors: Hyungkoo Kim, ChulSeoung Chae, ByeolHee Sim, DongSik Yoon, JeongHoon Kang

Institution: Korea Electronics Technology Institute

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funders: Ministry of Trade, Industry and Energy, Korea Evaluation Institute of Industrial Technology