A Microwave Resonator Biosensor Based on MEMS Technology and Rough Cu Electrodes for Biological Cell Detection

Chia Feng Liu, Pao Cheng Huang, Min Haw Wang, Chun Hong Chen, Ling Sheng Jang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work presents a near-field microwave resonant sensor combining with a micro-channel for detecting biological cells. The hairpin biosensor with a high-Q dielectric resonator allows the detection of a small variation of DUT in the microchannel by measuring the scattering parameters responses at resonance of around 2.2 GHz. The sensor was constructed by using microelectromechanical-system (MEMS) technology. Cu electroplating technique was used to fabricate 14-μm-thick Cu electrodes to reduce the transmission loss caused by the rough surface of electrodes and to match the size of cells. Measurements of Dulbecco's modified Eagle's medium (DMEM) and B16F10 melanoma cells (mus musculus skin melanoma) were performed. The results showed that the thick Cu electrodes provided two advantages: loss reduction and cell detection sensitivity. These analysis results are helpful for further applications such as cell trapping, measurement, and characteristics.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728132792
DOIs
Publication statusPublished - 2019 May
Event6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019 - Yilan, Taiwan
Duration: 2019 May 202019 May 22

Publication series

Name2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019

Conference

Conference6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
CountryTaiwan
CityYilan
Period19-05-2019-05-22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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