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

  • Yu Hsiang Wang*
  • , Wen Yue Lin
  • , Lin Hung Lai
  • , Chen Yi Lee
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationISCAS 2025 - IEEE International Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350356830
DOIs
StatePublished - 2025
Event2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025 - London, United Kingdom
Duration: 25 May 202528 May 2025

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference2025 IEEE International Symposium on Circuits and Systems, ISCAS 2025
Country/TerritoryUnited Kingdom
CityLondon
Period25/05/2528/05/25

Keywords

  • DEP
  • Dielectrophoresis
  • Image Recognition
  • Particle Manipulation
  • YOLO v8

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