Automated Generation of Microfluidic Netlists using Large Language Models
This work addresses the complexity of microfluidic design for practitioners by providing a more intuitive automation solution, though it is incremental as it builds on prior HDL code generation research.
The paper tackled the problem of designing microfluidic devices by introducing a method to automatically generate structural Verilog netlists from natural language specifications using large language models, achieving an average syntactical accuracy of 88% on practical benchmarks.
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%.