h. adldoost s.j. fattahi project of mems course supervisor: dr.zabihollah 1 sharif university of...

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Multisensing Minimaly Invasive Surgery

H. Adldoost S.J. Fattahi

Project of MEMS CourseSupervisor: Dr.Zabihollah

Sharif University of Technology, Int’l Campus, 2010

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Outlines

• Introduction

Invasive surgery Advantage

Invasive surgery Problems

• Gripper + Camera• Motivation for MIS• Why flexible tactile sensor ?• Sensing Using FBG• Shear Sensing Using ZnO• Final proposed Structure• Modeling

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Invasive surgery Advantage

•less pain, less strain of the organism •faster recovery

•small injuries (aesthetic reasons) •economic gain (shorter illness time)

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Invasive surgery Problems

•Restricted vision

•Difficult handling of the instruments

•Very restricted mobility

•Difficult hand-eye coordination

•No tactile perception

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Gripper + Camera(Conventional System)

Outside bodyInside body

Minimally Invasive Surgery (MIS, in german: MIC) performed by the help of: 1- small endoscopic camera

2-several long, thin, rigid instruments (Grippers, Graspers, Cutter and….

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Motivation for MIS

Haptic information Tactile sensor

Microsurgery

Actuator

Robotics

Pulsating system

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Why flexible tactile sensor ?

Increased reach, more accurate surgery Miniaturized surgical tool

Smaller space for sensors Not plane (arbitrary) surface !!

Silicon supporting structure

Zno with Polysilicon Cover

Diaphragm

Thin diaphragm structure

Silicon substrate

Zno Passivation

Oxide

Air gap Poly-Si

Capacitive type Structure

Silicon substrate Bulk Sensing material: Polysilicon Rather complex process Mechanically not stable Bulk micromachining Time consuming work

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Sensing Using FBG

Wavelength signal will be reflected coherently to make a large reflection.

r = 2neff

in

Reflection spectrum

reflect

Transmission spectrum

trans.

n (refraction index difference)

where neff is the effective refractive index of the mode propagating in the fiber and is the FBG period

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Temperature SensingUsing FBG

B = B (1 - ) + B( + )T

FBG sensors are sensitive temperature by:

, , and are respectively the photoelastic, thermal expansion and thermo-optic .

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Temperature SensingUsing FBG

0 0.13 0.63000000000000

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1.13 1.63 2.13 2.63 3.13 3.63 4.13 4.63 5.13 5.63 6.13 6.63 7.13 7.63 8.13 8.63 9.13 9.63

Temperature

0 1.313

6.36299999999999

11.413

16.4629999999999

21.513

26.563

31.613

36.663

41.713

46.763

51.813

56.863

61.913

66.963

72.013

77.063

82.113

87.163

92.213

97.263

10.00

30.00

50.00

70.00

90.00

110.00

ΔλB/ΔT = 10.1 pm/◦C

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X-Y Strain SensingUsing FBG

x = x (1 - ) + B( + )T

FBG sensors are sensitive temperature by:

y = y (1 - ) + B( + )T

Normal stress in FBG under diametrical loading

E: 69 GPan0 = 1.45ν = 0.29p11 = 0.121p12 = 0.270Fiber diameter = 125 microns

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Normal stress in FBG via FEA

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Bragg Wavelength change

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Shear Sensing

Polyimide substrate

Polyimide passivation

Bottom electrode (Cr/Au)

Top electrode (Al)

Silicon dioxide

Sensing layer (ZnO)

Polyimide substrate and passivation layer Flexibility Sensing material: ZnO Simpler fabrication process

Mechanically more stable Only with surface micromachining

ZnO sensing layer No thermal problems and additional

thermal poling process such as PZT or PVDF

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Fabrication Steps

Silicon wafer

(a) Polyimide spincoating and curing

(b) Bottom electrode deposition and patterning

(c) Silicon dioxide deposition and patterning

(d) ZnO layer deposition and patterning

(e) Silicon dioxide deposition and patterning

(f) Top electrode deposition and patterning

(g) Polyimide passivation layer spincoating and patterning

(h) Etch-release

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Electro Mechanical Coupling of ZnO

E=Electric fieldD=Electric displacementϵ=PermittivityT=Thicknessd= strain coefficient

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Finding Rate of Normal and Shear Stain using a ZnO

33 Mode:

31 Mode:

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Viscous Material Complience Matrix

Above stress/strain-rate relationship may be obtained for the flow of a viscous Material

where : σ is the stress ἐ the extensional strain-rateγ the shear-rate components

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Final Gripper Structure

Zno

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Modeling

Elements used are:PLANE 82 for the grasper jawPLANE 223 for ZnO layerPLANE 42 for FBGPLANE 85 for fixture plate of ZnO

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ANSYS Model

Gripping an object gripper

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ANSYS Model

• Force Applied • Deformation

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References

• J. Dean Callaghan, and M. Mark McGrath, “A Force Measurement Evaluation Tool for Telerobotic Cutting Applications: Development of an Effective Characterization Platform”, International Journal of Mathematical, Physical and Engineering Sciences, vol 1, no 3, August 2007.

• S. Sokhanvar, M. Packirisamy and J. Dargahi, “A multifunctional PVDF-based tactile sensor for minimally invasive surgery”, Smart Mater. Struct. Vol.16, 2007, 989–998.

• M. Shikida, T. Shimizu, K. Sato and K. Itoigawa, “Active tactile sensor for detecting contact force and hardness of an object”, Sensors Actuators, 2003, Vol. 103 213–8.

• Anindya Ghoshal, Mannur J. Sundaresan, Mark J. Schulz, P. Frank Pai, “Structural health monitoring techniques for wind turbine blades”, Journal of Wind Engineering and Industrial Aerodynamics 85 (2000) 309-324.

• [Kyungmok Kim, Jong Min Lee, Yoha Hwang, “ Determination of engineering strain distribution in a rotor blade with fiber Bragg grating array and a rotary optic coupler”, Optics and Lasers in Engineering 46 (2008) 758– 762.

• M. Tanimoto, F. Arai, T. Fukuda, H. Iwata, K. Itoigawa, Y. Gotoh,M. Hashimoto, and M. Negoro, “Micro force sensor for intravascular neurosurgery and in vivo experiment,” Proc. IEEE Int. Workshop Micro Electro Mechanical Systems (MEMS 98), pp. 504–509.

• [21] H. Takizawa, H. Tosaka, R. Ohta, S. Kaneko, and Y. Ueda, “Development of a microfine active bending catheter equipped with MIF tactile sensors,” Proc. 12th IEEE Int. Conf. Micro Electro Mechanical Systems (MEMS’99), pp. 412–417.

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Thank You