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

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.