An Intelligent Digital Microfluidic Biochip System with GUI and Deep Learning-Based Automation

Abstract

This paper introduces an intelligent digital microfluidic biochip (DMFB) system that integrates deep learning for automated droplet manipulation with an intuitive interface for manual control. Our approach combines a custom, low-cost $8 \times 8$ electrode array fabricated on a printed circuit board (PCB) with a real-time vision system. The system utilizes copper-coated electrodes with cost-effective dielectric and hydrophobic layers, driven by a microcontroller-based high-voltage actuation circuit. Real-time, autonomous droplet manipulation is enabled by a deep learning-based pipeline that features a fine-tuned You Only Look Once (YOLOv11n) model for robust droplet detection and an intelligent routing algorithm for generating optimal, collision-free routing paths. Experimental validation demonstrates high system performance, with the automation pipeline achieving a 90% routing success rate and the YOLOv11n detector attaining a mean Average Precision (mAP@0.5) of 0.993 on a custom-created dataset. Furthermore, the system incorporates a graphical user interface (GUI) for manual control, which exhibited 98% task accuracy. By successfully integrating affordable hardware with advanced computer vision, this work presents a scalable and accessible platform for automated biochemical assays, demonstrating significant potential to advance applications in point-of-care diagnostics, drug discovery, and personalized medicine.