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

Abstract

Dielectrophoresis (DEP) is powerful for manipulating biological cells. However, single cell manipulation is usually time consuming and skill needed. 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.

Lin-Hung Lai

Visiting Student Researcher @ Stanford University

My research interests include distributed robotics, mobile computing and programmable matter.