Automating Code Generation for Semiconductor Equipment Control from Developer Utterances with LLMs

• URL: http://arxiv.org/abs/2509.13055v1
• Date: Tue, 16 Sep 2025 13:12:11 GMT
• Title: Automating Code Generation for Semiconductor Equipment Control from Developer Utterances with LLMs
• Authors: Youngkyoung Kim, Sanghyeok Park, Misoo Kim, Gangho Yoon, Eunseok Lee, Simon S. Woo,
• Abstract summary: Equipment languages such as the Algorithmic Pattern Generator (ALPG) are critical for precise hardware control.<n>We propose Progressive Knowledge Enhancement (PKE), a novel multi-stage prompting framework.<n> Empirical evaluation on an industrial ALPG dataset shows that PKE significantly outperforms standard prompting.
• Score: 22.922338629324244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semiconductors form the backbone of modern electronics, with their manufacturing and testing relying on highly specialized equipment and domain-specific programming languages. Equipment languages such as the Algorithmic Pattern Generator (ALPG) are critical for precise hardware control but are challenging to program due to their low-level syntax and steep learning curve. While large language models (LLMs) have shown promise in generating high-level code from natural language, their effectiveness on low-level equipment languages remains limited. To address this, we propose Progressive Knowledge Enhancement (PKE), a novel multi-stage prompting framework that progressively extracts and activates the latent knowledge within LLMs, guiding them from simple to complex examples without extensive fine-tuning. Empirical evaluation on an industrial ALPG dataset shows that PKE significantly outperforms standard prompting and surpasses state-of-the-art methods in generating correct ALPG code, achieving 11.1\% and 15.2\% higher exact match scores compared to the second-best technique. Further analysis of individual components confirms that progressive knowledge extraction based on difficulty enhances accuracy. Our study offer a practical approach to boosting LLM capabilities for specialized low-level programming, supporting greater productivity in semiconductor software development.

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