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

Abstract

Precise spatiotemporal manipulation of particles in complex microfluidic channel networks (MCNs) underlies numerous advanced applications, but remains constrained by the difficulty of rapidly translating prescribed trajectories into manufacturable device designs. In this work, we introduce a modular deep learning framework that overcomes these limitations by decomposing MCNs into standardized, reusable functional modules with well-characterized fluidic and structural properties. For each module, a dedicated neural network predicts the full spatiotemporal particle state—including position, velocity, and transit time—under diverse flow conditions. A multi-module reconfiguration algorithm (MMRA) assembles these local predictions into continuous, device-scale trajectories while rigorously preserving physical state continuity. This approach enables deterministic port routing and precise spatiotemporal scheduling on “DUT” and “grid” chips, with a mean absolute timing error below 0.031 s. Integrated into PathChip, our user-friendly end-to-end design platform, the proposed approach enables users to specify target particle behaviors and automatically generate optimized module sequences, geometries, and control parameters, producing fabrication-ready device blueprints. Using this reverse design workflow, the integration of 5,000 modules can be completed in as little as 18 s. This work establishes a structurally scalable pathway toward programmable, device-level spatiotemporal particle manipulation in microfluidics, with broad implications for lab-on-a-chip automation, high-throughput screening, and adaptive microfluidic systems.