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[2602.19297] Automated Generation of Microfluidic Netlists using Large Language Models
Summary
This article presents a novel approach to automate the generation of microfluidic netlists using large language models (LLMs), demonstrating a method that converts natural language specifications into functional designs with high accuracy.
Why It Matters
The automation of microfluidic design can significantly enhance accessibility for researchers and practitioners, streamlining the development of complex devices. This work showcases the potential of LLMs in bridging the gap between technical specifications and practical implementations, which is crucial for advancing microfluidic technologies.
Key Takeaways
- Introduces a methodology for generating microfluidic netlists from natural language.
- Achieves an average syntactical accuracy of 88% in netlist generation.
- Demonstrates the feasibility of using LLMs in microfluidic design automation.
- Addresses the complexity barrier in microfluidic device design.
- Provides a foundation for future research in hardware description language generation.
Computer Science > Artificial Intelligence arXiv:2602.19297 (cs) [Submitted on 22 Feb 2026] Title:Automated Generation of Microfluidic Netlists using Large Language Models Authors:Jasper Davidson, Skylar Stockham, Allen Boston, Ashton Snelgrove. Valerio Tenace, Pierre-Emmanuel Gaillardon View a PDF of the paper titled Automated Generation of Microfluidic Netlists using Large Language Models, by Jasper Davidson and 4 other authors View PDF HTML (experimental) Abstract: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%. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.19297 [cs...
