Microfluidic devices have emerged as powerful tools in various laboratory applications, but the complexity of their design limits accessibility for many practitioners. While progress has been made in microfluidic design automation (MFDA), a practical and intuitive solution is still needed to connect microfluidic practitioners with MFDA techniques. This work introduces the first practical application of large language models (LLMs) in this context, providing a preliminary demonstration. Building on prior research in hardware description language (HDL) code generation with LLMs, we propose an initial methodology to convert natural language microfluidic device specifications into system-level structural Verilog netlists. We demonstrate the feasibility of our approach by generating structural netlists for practical benchmarks representative of typical microfluidic designs with correct functional flow and an average syntactical accuracy of 88%.

翻译：微流控设备已成为各类实验室应用中的强大工具，但其设计的复杂性限制了许多从业者的使用。尽管微流控设计自动化（MFDA）领域已取得进展，但仍需一种实用且直观的解决方案来连接微流控从业者与MFDA技术。本研究首次在此背景下引入大型语言模型（LLMs）的实际应用，提供了初步的演示。基于先前利用LLMs生成硬件描述语言（HDL）代码的研究，我们提出了一种初步方法，可将自然语言描述的微流控设备规格转换为系统级的结构化Verilog网表。我们通过为具有代表性的典型微流控设计生成结构化网表来证明该方法的可行性，这些基准测试案例功能流正确，平均语法准确率达到88%。