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Automated Generation of Microfluidic Netlists using Large Language Models

Jasper Davidson, Skylar Stockham, Allen Boston, Ashton Snelgrove, Valerio Tenace, Pierre-Emmanuel Gaillardon

TL;DR

This work proposes an initial methodology to convert natural language microfluidic device specifications into system-level structural Verilog netlists, and introduces the first practical application of large language models (LLMs) in this context, providing a preliminary demonstration.

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%.

Automated Generation of Microfluidic Netlists using Large Language Models

TL;DR

This work proposes an initial methodology to convert natural language microfluidic device specifications into system-level structural Verilog netlists, and introduces the first practical application of large language models (LLMs) in this context, providing a preliminary demonstration.

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%.
Paper Structure (16 sections, 2 figures, 4 tables)

This paper contains 16 sections, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Microfluidic Design Automation
  4. LLMs for Hardware Development
  5. Methodology
  6. Describing Microfluidic Systems with Verilog
  7. LLM Software Structure
  8. Prompt Methodology
  9. Finishing the Workflow
  10. Performance Evaluation and Analysis
  11. Experimental Setup
  12. Experimental Results and Discussion
  13. Prompting Methodology Outcomes
  14. Comparison between LLMs
  15. Future Work
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Microfluidic netlist generation methodology using LLMs, spanning from laboratory applications to Verilog netlists that are compatible with existing MFDA techniques.
  • Figure 2: A three-dimensional rendering of a microfluidic chip designed to mix six solutions in parallel, produced using OpenMFDA with a Qwen2-generated system of six mixers.