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The semiconductor industry demands a high level of automation and unmanned operation, making equipment control based on the SECS/GEM standard essential. However, due to a shortage of skilled developers and high turnover rates, developing equipment control software using the SECS/GEM standard is becoming increasingly challenging.
In this study, we propose a novel approach that leverages Generative AI to overcome the limitations of traditional code generation methods. Existing SECS/GEM-based code generation tools provide predefined template codes, but adapting these templates to real-world applications requires significant developer intervention. In contrast, the Generative AI-based optimized code generation method proposed in this study learns from existing code data and effectively generates code that closely aligns with the developer's intended functionality.
This approach reduces the time required for code modification and refinement, enabling efficient semiconductor equipment control software development with fewer personnel.
We experimentally evaluated the performance of the proposed AI model to validate its effectiveness, demonstrating the feasibility of our approach. We believe that this research marks a significant turning point in enhancing productivity and advancing automation in the semiconductor industry.
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IEEE/ICACT20250028 Slide.04
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