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IEEE/ICACT20250028 Slide.12        [Big Slide]	Oral Presentation
	In this paper, we propose an automatic SECS/GEM code generation method using LLMs. Existing SECS/GEM codes, which serve as control data for semiconductor equipment, are stored in a vector database. When a user requests code generation through a prompt, the system utilizes the Retrieval-Augmented Generation (RAG) approach to automatically generate the desired SECS/GEM code based on the stored data. Fine-tuning is essential for handling downstream tasks, but it requires significant computational resources. To address this, we opted for the relatively lightweight RAG technique, offering an efficient and cost-effective solution. To facilitate code generation, the necessary design documents are converted into PDF format and loaded into a Lang Chain-based service. These documents are then stored in a vector database, enabling efficient retrieval. This approach not only conserves computational resources but also provides code generation based on design documents.

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