Deep learning-driven microfluidic chip architecture design for intelligent particle motion control.

Area of Science:

• Microfluidics
• Artificial Intelligence
• Biotechnology

Background:

• Designing complex microfluidic channel networks (MCNs) for precise particle manipulation is challenging.
• Current methods struggle to translate desired particle trajectories into manufacturable device designs efficiently.

Purpose of the Study:

• To introduce a modular deep learning framework for automated MCN design.
• To enable rapid and precise spatiotemporal control of particles within microfluidic devices.

Main Methods:

• Decomposition of MCNs into standardized, reusable functional modules.
• Use of dedicated neural networks to predict particle states (position, velocity, transit time) within each module.
• A multi-module reconfiguration algorithm (MMRA) to assemble local predictions into device-scale trajectories, ensuring physical state continuity.

Main Results:

• The framework enables deterministic port routing and precise spatiotemporal scheduling with a mean absolute timing error below 0.031 s.
• Integration into the PathChip platform allows automatic generation of optimized module sequences, geometries, and control parameters.
• Designs for up to 5000 modules can be generated in as little as 18 seconds.

Conclusions:

• This work presents a scalable approach for programmable, device-level spatiotemporal particle manipulation in microfluidics.
• The framework has significant implications for lab-on-a-chip automation, high-throughput screening, and adaptive microfluidic systems.