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Research Papers

Simulation Based Design of Disk Resonator Biosensors Under Fabrication Uncertainty

[+] Author and Article Information
Amir Heidari

 School of Mechanical and Aerospace Engineering,  Nanyang Technological University, 639798 SingaporeAMIR0004@e.ntu.edu.sg

Yong-Jin Yoon

 School of Mechanical and Aerospace Engineering,  Nanyang Technological University, 639798 Singaporeyongjiny@ntu.edu.sg

Hungsun Son

 School of Mechanical and Aerospace Engineering,  Nanyang Technological University, 639798 Singaporehsson@ntu.edu.sg

Hae-Jin Choi1

 School of Mechanical Engineering,  Chung-Ang University, Seoul, 156-756, South Koreahjchoi@cau.ac.kr

1

Corresponding author.

J. Mech. Des 134(4), 041005 (Mar 19, 2012) (11 pages) doi:10.1115/1.4006144 History: Received September 28, 2010; Revised February 04, 2012; Published March 15, 2012; Online March 19, 2012

A high performance and cost effective biosensor is designed using a radial contour-mode disk resonator (RCDR). This sensor measures tiny biological mass attached on a disk vibrating at a high frequency, producing high quality of output signal. A series of analysis and simulation models is developed to predict the mass sensitivity, dynamic stability, and motional resistance of the RCDR biosensor with given geometry and signal input. In order to decrease motional resistance while keeping the fabrication cost low, a layer of dielectric material is deposited within the capacitor gap. In designing the RCDR biosensors, we employ Type I, II, and III robust design approach to design a device that is insensitive to various types of uncertainty associated with the fabrication processes and analysis models. A mathematical construct, error margin index, is employed for this robust design. Traditional optimization and robust design approaches are separately formulated, solved, and compared. From the design results, we observe that the RCDR is a promising bio-sensing device compared to the existing ones.

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Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

(a) Schematic view of a radial contour-mode disk resonator (RCDR), illustrating the two-port bias, excitation scheme and the important parameters; (b) top and cross sectional view of the RCDR biosensor

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Figure 2

Schematic process of immobilizing antibody on a disk surface; (a) A thin gold film (about 50 nm) is sputtered onto the disk surface; (b) The antibody (phage) is immobilized on top of the metal surface; (c) Casein is dropped on the surface to block the open space around the immobilized bio-receptor; (d) The sensor is immersed in a test solution; antigen (bacteria, virus …) is captured by the antibody.

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Figure 3

Simulation model of the RCDR biosensor with the mapped mesh in ANSYS, (a) The 3D model of the disk resonator and the additional layer of biological entities on its surface, (b) Cross section of 3D model with material properties of the disk resonator and biological entities [27]

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Figure 4

Harmonic analysis of the disk resonator; (a) relative displacement of Point A in radial and vertical directions (b) relative displacement of Point B in radial and vertical directions (Points A and B are shown in Fig. 3)

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Figure 5

Schematic view of capacitor after deposition of Si3 N4 . The initial 2um air gap reduced with Si3 N4 to improve the electrical characteristic of the sensor.

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Figure 6

Equivalent circuit of disk resonator and its electrodes using to calculate the motional resistance (Rx)

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Figure 7

Motional resistance of the reduced gap resonator versus the air gap resonator design (Clark [11]) for different gap size (d1)

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Figure 8

The motional resistance of the RCDR versus bias voltage at Vac=0.1 V; from the top, simulation for air gap, theoretical equation for air gap by Clark , Eq. 10 for reduced gap and simulation for reduced gap

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Figure 9

A simulation chain of performance analysis

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Figure 10

Kriging model of mass sensitivity based on sampling points obtained from simulation

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Figure 11

Mathematical construct of error margin indices [40]

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Figure 12

Feasible region of deterministic and EMI based robust design with Si3 N4 layer

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Figure 13

Probability distributions of LHC sampling results based on the nonlinear models; (a) breakdown voltage constraint (normal distribution), (b) motional resistance(lognormal distribution), and (c) mass sensitivity (normal distribution)

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Figure 14

Feasible region of deterministic and EMI based robust design without Si3 N4 layer

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