用大模型把自然语言描述转为微流控电路图，提升设计效率。
Automated Generation of Microfluidic Netlists using Large Language Models
- 通过大模型将自然语言需求转化为微流控系统级网表结构。
- 在典型设计上实现88%的语法准确率，功能流程正确。
- 适合微流控新手或快速原型设计者使用。
微流控器件在各类实验应用中表现突出，但其设计复杂性限制了广泛使用。尽管微流控设计自动化（MFDA）已有进展，但仍缺乏实用且直观的连接方式。本文首次将大语言模型（LLMs）应用于该领域，基于硬件描述语言（HDL）生成的研究基础，提出一种方法：将自然语言的微流控设备规格转化为系统级结构化Verilog网表。通过代表性实际基准测试，验证了该方法可行性，生成的网表具备正确功能流，平均语法准确率达88%。
原文摘要 · Abstract (English)
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%.
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