{"ID":2870429,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13055","arxiv_id":"2509.13055","title":"Automating Code Generation for Semiconductor Equipment Control from Developer Utterances with LLMs","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.","short_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 du...","url_abs":"https://arxiv.org/abs/2509.13055","url_pdf":"https://arxiv.org/pdf/2509.13055v1","authors":"[\"Youngkyoung Kim\",\"Sanghyeok Park\",\"Misoo Kim\",\"Gangho Yoon\",\"Eunseok Lee\",\"Simon S. Woo\"]","published":"2025-09-16T13:12:11Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}