Smart Pattern Generation on Programmable Dielectrophoresis Array Chip for Single Particle Manipulation

Dielectrophoresis (DEP) is a powerful tool for manipulating biological cells. However, single cell manipulation is usually time-consuming and skill-intensive. This paper presents a system that integrates AI for real-time image recognition with a programmable dielectrophoresis (DEP) array chip for automated particle manipulation. The system comprises a DEP chip, an FPGA, a computer, a microscope, and a server. The YOLO v8 model is used to detect particle positions within microscope images and generate DEP manipulation patterns. The system utilizes a Breadth-First Search (BFS) algorithm for path planning, ensuring collision-free movement of particles within a grid structure. Experimental results demonstrated the system’s effectiveness in manipulating 20 μm polystyrene particles with a success rate of over 90%. This system offers a significant advancement in automated DEP-based manipulation, providing precise control at micro scales with high computational efficiency.