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IEEE/ICACT20250028 Slide.05        [Big Slide]	Oral Presentation
	In this study, we first review the SECS/GEM standard and existing code generation techniques before proposing an automatic code generation method for semiconductor equipment control using Generative AI. We validated the effectiveness of our approach using evaluation metrics commonly employed in LLM research and will share the results of our experimental evaluation. The experimental findings confirm the significance of the Generative AI-based approach. We expect this research to contribute to the automation and optimization of semiconductor equipment software development. In the final part of this presentation, we will discuss the implications of our study and outline directions for future research.

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