TSINGHUA SCIENCE AND TECHNOLOGY ISSNll 1007-0214 ll 06/12 ll pp161-167 Volume 19, Number 2, April 2014 Tiny MEMS-Based Pressure Sensors in the Measurement of Intracranial Pressure Yanhang Zhang, Zhaohua Zhang, Bo Pang, Li Yuan, and Tianling Ren Abstract: This study presents a tiny pressure sensor which is used to measure the Intracranial Pressure (ICP). The sensor is based on the piezoresistive effect. The piezoresistive pressure sensor is simulated and designed by using nonlinear programming optimizing and Finite Element Analysis (FEA) tools. Two kinds of sensor sizes are designed in the case of childhood and adult. The sensors are fabricated by Microelectro Mechanical Systems (MEMS) process. The test results yield sensitivities of 1.03310 2 mV/kPa for the childhood type detection and 1.25710 2 mV/kPa for the adult detection with sensor chip sizes of 0.400.40 mm 2 and 0.500.50 mm 2 , respectively. A novel method for measuring ICP is proposed because of the tiny sizes. Furthermore, relative errors for sensitivity of pressure sensors are limited within 4.76%. Minimum Detectable Pressure (MDP) reaches 128.4 Pa in average. Key words: intracranial pressure; tiny sensors; finite element analysis 1 Introduction Intracranial Pressure (ICP) is the pressure within our skull. Factors that contribute to this are the brain tissue and the cerebrospinal fluid. In a normal situation, our body works to keep the ICP stable to allow for the free circulation of blood vessels in our brain. This pressure is usually measured in millimeters of mercury (mm-Hg) and there is a certain range that would be considered normal. The range for resting adults is 7 to 15 millimeters of mercury. For resting children, the range is 4 to 10 millimeters of mercury depending on his or her age. And while they are active, the range goes up a few units [1] . During accidents that involve head injuries, ICP Yanhang Zhang, Bo Pang, Li Yuan, and Tianling Ren are with the Institute of Microelectronics, Tsinghua University, Beijing 100084, China. E-mail: [email protected]; [email protected]; [email protected]; [email protected]. Zhaohua Zhang is with the Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 100083, China. E-mail: [email protected]. To whom correspondence should be addressed. Manuscript received: 2014-02-18; revised: 2014-02-24; accepted: 2014-02-25 monitoring is one of the standard operating procedures that have to be performed on the patient. This is because ICP monitoring helps the doctor figure out whether you sustained any severe head trauma. ICP that is too low or too high can be damaging. The brain is a highly sensitive part of the body so even just the slightest change in its immediate environment may cause complications. One normal method of implementing ICP monitoring is to derive cerebrospinal fluid and use the principle of communicating vessel, however at the cost of accuracy. In conventional methods of ICP detecting, insertion of a catheter into the cranium is usually needed to derive cerebrospinal fluid. However this measurement method suffers from relatively large error rate because of the derived fluid. The goal of this paper is to design a new pressure sensor, which is small enough to be implemented close to the tip of a needle. In this case, the ICP can be directly detected by acupuncture. With the development of Microelectro Mechanical Systems (MEMS) technology, it is possible for us to make a pressure sensor that is small enough to be installed in a lumbar puncture needle [2] . Using the MEMS sensor with the lumbar puncture needle, shown in Fig.1, the ICP monitoring will be more convenient,
Transcript
TSINGHUA SCIENCE AND TECHNOLOGYISSNll1007-0214ll06/12llpp161-167Volume 19, Number 2, April 2014
Tiny MEMS-Based Pressure Sensors in the Measurement ofIntracranial Pressure
Yanhang Zhang, Zhaohua Zhang, Bo Pang, Li Yuan, and Tianling Ren�
Abstract: This study presents a tiny pressure sensor which is used to measure the Intracranial Pressure (ICP). The
sensor is based on the piezoresistive effect. The piezoresistive pressure sensor is simulated and designed by using
nonlinear programming optimizing and Finite Element Analysis (FEA) tools. Two kinds of sensor sizes are designed
in the case of childhood and adult. The sensors are fabricated by Microelectro Mechanical Systems (MEMS)
process. The test results yield sensitivities of 1.033�10�2 mV/kPa for the childhood type detection and 1.257�10�2
mV/kPa for the adult detection with sensor chip sizes of 0.40�0.40 mm2 and 0.50�0.50 mm2, respectively. A novel
method for measuring ICP is proposed because of the tiny sizes. Furthermore, relative errors for sensitivity of
pressure sensors are limited within 4.76%. Minimum Detectable Pressure (MDP) reaches 128.4 Pa in average.
Key words: intracranial pressure; tiny sensors; finite element analysis
1 Introduction
Intracranial Pressure (ICP) is the pressure within ourskull. Factors that contribute to this are the brain tissueand the cerebrospinal fluid. In a normal situation, ourbody works to keep the ICP stable to allow for thefree circulation of blood vessels in our brain. Thispressure is usually measured in millimeters of mercury(mm-Hg) and there is a certain range that would beconsidered normal. The range for resting adults is 7to 15 millimeters of mercury. For resting children, therange is 4 to 10 millimeters of mercury depending onhis or her age. And while they are active, the range goesup a few units[1].
During accidents that involve head injuries, ICP
�Yanhang Zhang, Bo Pang, Li Yuan, and Tianling Ren are withthe Institute of Microelectronics, Tsinghua University,Beijing 100084, China. E-mail: [email protected];[email protected]; [email protected];[email protected].� Zhaohua Zhang is with the Beijing Institute of Nanoenergy and
Nanosystems, Chinese Academy of Sciences, Beijing 100083,China. E-mail: [email protected].�To whom correspondence should be addressed.
monitoring is one of the standard operating proceduresthat have to be performed on the patient. This isbecause ICP monitoring helps the doctor figure outwhether you sustained any severe head trauma. ICPthat is too low or too high can be damaging. Thebrain is a highly sensitive part of the body so evenjust the slightest change in its immediate environmentmay cause complications. One normal method ofimplementing ICP monitoring is to derive cerebrospinalfluid and use the principle of communicating vessel,however at the cost of accuracy.
In conventional methods of ICP detecting, insertionof a catheter into the cranium is usually needed toderive cerebrospinal fluid. However this measurementmethod suffers from relatively large error rate becauseof the derived fluid. The goal of this paper is to designa new pressure sensor, which is small enough to beimplemented close to the tip of a needle. In this case,the ICP can be directly detected by acupuncture.
With the development of Microelectro MechanicalSystems (MEMS) technology, it is possible for us tomake a pressure sensor that is small enough to beinstalled in a lumbar puncture needle[2]. Using theMEMS sensor with the lumbar puncture needle, shownin Fig.1, the ICP monitoring will be more convenient,
162 Tsinghua Science and Technology, April 2014, 19(2): 161-167
Fig. 1 The lumbar puncture needle. The length of thechildhood type is 40 mm, the adult is 70 mm, the diameteris from 400 to 550 ���m.
more accurate, and more flexible, thus alleviating thesuffering of patients.
This paper will show a brand new way of ICPmeasurement using the characteristics of MEMS sensorand technic of lumbar puncture. Figure 2 depicts thiscombination. Meanwhile this paper will also report onsimulations which provide design guidelines for theadaptive method that can be used on simulations ofother types of pressure sensors. The sensor reportedhere is based on piezoresistive effect and is less than0.50 mm in width and length. It will be proper touse especially inside the needle. The sensor chip sizeis smaller and the sensitivity is larger than previouswork[3, 4].
2 Design Principles and Optimization
2.1 Methods to evaluate sensitivity of the pressuresensor
Wheatstone bridge is traditionally applied to thenetwork of pressure sensors. When the pressureis loaded, the output voltage can be obtained.
Fig. 2 The micrograph of the lumbar puncture needle. TheMEMS sensor is installed at the very top, protected under theconical tip.
The piezoresistive pressure sensor is designed usingnonlinear programming which is a mathematic tool tofind the minimum value of the objective function. Theschematic view of the piezoresistive pressure sensor isshown in Fig. 3.
Vout D
.R2C�R2/ .R4C�R4/�.R1C�R1/ .R3C�R3/
.R1C�R1CR2C�R2/ � .R3C�R3CR4C�R4/Vin
(1)Where R1 D R2 D R3 D R4 D R. R is the originalpiezoresistance which can be expressed by Eq. (2),where L and W are the length and the width ofpiezoresitors and R is the sheet resistance.
R D RrL
W(2)
According to piezoresistance effect, if the piezoresistoris located in [011] direction on [100] facet of p typesilicon, Eq. (3) is obtained, where �l and �t are thelongitudinal and transverse stress, respectively.�R
RD�t�tC�l�lD
�44
2.�t � �l/D
�44
2L
ZL
.�x � �y/dx
(3)Based on Eqs. (2) and (3), Eq. (4) is obtained, whereA is the integration of stress difference with respect tothe path along R1, which can be realized convenientlyby ANSYS. l1 is the path along R1. It will changewhen different pressures are applied on the surface ofthe diaphragm.
�R1 DRr�44
2W
Zl1
��x1� �y1
�dx D
Rr�44
2WA (4)
There will be similar format with respect toR2,R3, andR4. We name the corresponding integrals B , C , andD,respectively. When there is neither translation deviationnor rotation deviation, then A equals C and B equalsD. Combine all the formula above, Vout can be modifiedas
Fig. 3 Schematic view of the sensor and its network.
Yanhang Zhang et al.: Tiny MEMS-Based Pressure Sensors in the Measurement of Intracranial Pressure 163
Vout D�44 .B � A/
4LC �44AC �44BVin (5)
With output voltage values for each pressure (fromzero to full-scale pressure) applied, it is easy tocalculate the sensitivity. The result will be achievedconveniently together with the Finite Element Analysis(FEA) software[5].
2.2 Rules and optimization goals
According to the deflection theory, we can calculate thestress distribution of a loaded thin square board[6], asshown in Fig. 4.
To obtain the maximum sensitivity, these resistorsshould be placed near the edges of the silicondiaphragm along X direction, which are the highstress regions when there is a pressure load. Ithas been found that the high stress region ofthe square silicon diaphragm extends significantlywhen the thickness of the film increases, similarlythe high stress region also extends when the sizeof the diaphragm increases. Using the nonlinearprogramming, the parameter of the sensors can beoptimized expediently. The size of the sensitivemembrane should be small. According to the materialproperties of silicon and the manufacturing realities, thethickness is designed as 10 �m[7, 8].
Regardless of the noise factor, first we only consider
Fig. 4 Stress distribution of square membrane.
the influence of piezoresistor branches number N tothe sensitivity S . Calculate the integral of the effectiveresistance, and add N to the denominator. The scanresult of N is shown in Fig. 5.
From Fig. 5, we can conclude that the sensitivityincreases when the factor N decreases. Because N
should be an integer in reality, the sensitivity reachesmaximum when N is one. Moreover the resistancereaches the minimum value, the thermal noise isminimized, so N is set to one. Length L has littleeffect on sensitivity. L is designed as 44 �m. Noiseis very important for this small structure. It affects theMinimum Detectable Pressure (MDP) which is a crucialperformance factor of the sensor[9, 10]. The nonlinearprogramming simulation results are shown in Table1. W , T , and L are the width, thickness, and lengthof the resistors, NAA is the doping concentration. Thetemperature is set to 26ıC[11]. Numbers in bold are theoptimized values.
According to the analysis and the nonlinearprogramming result above, the main parameters of thesensor sizes are determined.
2.3 FEA simulation
Both the stress analysis and path integration areaccomplished by finite element analysis softwareANSYS. Figure 6 shows the contour plot of adulttype pressure sensor unit. All the parameters of these
Fig. 5 Scan result of piezoresistance number N.
Table 1 Results of nonlinear programming optimizing by Matlab.
Optimizing functionW T L NAA MDP Sensitivity Noise�m �m �m 1018cm�3 Pa 10�3mV/kPa 10�6V
164 Tsinghua Science and Technology, April 2014, 19(2): 161-167
Fig. 6 Stress difference of the diaphragm of one-quarter ofan adult type sensor.
two types of pressure sensors are the same exceptfor the size of the square silicon diaphragm and thetotal unit size: childhood type has a diaphragm lengthof 100 �m; adult type, 150 �m. The main steps ofthe simulation include modeling (building geometryand paths), meshing, applying loads (fix support andpressure), defining result, and solving[12]. In orderto save computer memory and simulation time, onequarter of the ICP sensor model is constructed becauseof its property of symmetry.
As stated above, it is important to determine thelocation of piezoresistors for covering the high stressregion[13]. Besides, the highest stress region is in themiddle of the diaphragm edge. Thus, set two pathsalong x-axis and y-axis. The variation of stress along thepath can be reflected accurately. The results are shown
Fig. 7 Stress difference of the piezoresister for 5 kPa loadmeasured at position R4 (see Fig. 3).
Fig. 8 Stress difference of the piezoresister for 5 kPa loadmeasured at position R1 (see Fig. 3)
in Figs. 7 and 8.After the resistors are determined, the output voltage
can be solved with the result of integrals A and B andEq. (5) derived above. A and B represent the integraladdition result explained in Eq. (4). The integrals Aand B are actually the area under the curves in Figs.7and 8. Tables 2 and 3 are the integral results and thesensitivities through simulation.
3 Fabrication
The fabrication is based on MEMS process. The waferwe used is 4 inches SOI wafer which can help toobtain a highly homogeneous siliceous film. The devicethickness is 10 �m, handle thickness is 400 �m. Themain process steps are wafer oxidation using boiled
Table 2 The adult type sensor simulation result.
Pressure A B Vout SensitivitykPa 10�7N/�m 10�7N/�m 10�2mV 10�2mV/kPa
Yanhang Zhang et al.: Tiny MEMS-Based Pressure Sensors in the Measurement of Intracranial Pressure 165
H2SO4+H2O2; ion implantation to form piezoresistors,the energy of ion is 80 keV, the dosage is 4�1014 cm�2;oxidation in pure O2 under 1050 ıC and annealing;conducting PECVD to form thin SiO2 protection layer;etching silicon cup through inductively coupled plasma;and wafer bonding and metallization[14]. The opticalphoto of ICP sensors are shown in Fig. 9.
4 Test Result and Analysis
Typical substantive test of ICP sensor samples is amust. The samples are tested through standard testmethods. In order to test more accurately, we usehigh performance instrumentation amplifier to drivethe ICP sensor. It makes sense in practice becausethe lumbar puncture needle can hold the electric wireswhich connect the ICP sensor and the instrumentationamplifier. Figure 10 shows the test environment, theinstrumentation amplifier, and the board which providesthe reference voltage, reference current, and necessarystable power.
The magnitude of enlargement is set to 100. Noiseinfluence in practice is more serious than previousthermal noise simulation result, because it consists ofa variety of noise sources. In order to decrease thenoise influence and test the sturdiness of the sensordiaphragm, the pressure interval is set to 5 kPa. Thevariation data result is shown in Tables 4 and 5.
Fig. 9 The ICP sensor sample optical photo. Childhood typeis on the left, adult type right.
Fig. 10 The test environment, the instrumentationamplifier, and the necessary boards.
Table 4 Test results for the childhood type sensors.
Pressure(kPa)
Voltage (mV)Test 1 Test 2 Test 3 Test 4 Test 5 Test 6
Using polyfit function in Matlab, we can easily get 12lines which represent the data tested above. The slopeis the sensitivity of the pressure sensor and the nodalincrement of y-axis is the zero shift of the pressuresensor. Calculate the mean values of these sensitivitiesand zero shift, and divide them by the magnitude ofenlargement 100. We can conclude that the sensitivityfor childhood type sensor is 1.033� 10�2 mV/kPa,adult type 1.257�10�2 mV/kPa. Comparing to thesimulation result above, we can see that relative errorsfor sensitivity of pressure sensors are limited within4.76%.
Furthermore, repetitive tests are conducted to see thecomprehensive performance of ICP sensors. Choosingtypical sensor samples that have good linearity, weconduct four times increasing and four times decreasingtests to form four cycles for each sensor. According toBessel formula, we can get the repetitiveness. The resultis shown in Table 6.
In general, the total accuracy is satisfying.Homogeneity, however, is not so good. The reasonmight be the inhomogeneity of the SOI wafer and thedeviation of etching process which is shown in Fig. 11.
5 Conclusions
New way of using tiny ICP sensor wasproposed. Optimization was achieved by mathematiccalculation. New methods based on finite elementanalysis and mathematical integrations were applied to
166 Tsinghua Science and Technology, April 2014, 19(2): 161-167
evaluate the sensitivity of piezoresistive pressuresensors. ICP pressure sensors were fabricatedand controlled by MEMS process. Instrumentationamplifier was used to get clearer results. Sensitivityand properties behavior showed that the ICP sensor issuitable for biochemical use. Test results confirmed thesimulation method, which is significant in improvingthe product consistency for piezoresistive pressuresensor designers. The results also show that the sensorsmeet the demand of ICP measurement. As the sensorsize reduces, the effect of noise on the output signalbecomes the limiting factor in the sensor design, andfuture work needed is the noise factor optimization.
Acknowledgements
This work was supported by the National Natural ScienceFoundation of China (Nos. 61025021 and 61020106006),and the National Key Projects of Science and Technologyof China (No. 2011ZX02403-002).
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Yanhang Zhang received his BSdegree from Tianjin University, China,in 2011. He was an exchange masterstudent of KU Leuven, Belgium in 2011-2012. Currently he is a master student atTsinghua University. His research interestsare in the area of MEMS pressure sensors,acceleration sensors, and circuits design.
Zhaohua Zhang received the BS degreein electrical engineering from the HarbinInstitute of Technology, Harbin, China, in1997, and the MEng and PhD degreesin electrical engineering from TsinghuaUniversity, Beijing, China, in 2004. Heis currently working as an associatedprofessor in the Beijing Institute of
Yanhang Zhang et al.: Tiny MEMS-Based Pressure Sensors in the Measurement of Intracranial Pressure 167
Nanoenergy and Nanosystems, CAS. He has published about 40papers in international journals. His research interests includeintegrated microelectromechanical inertial sensors, pressuresensors, actuators, resonator designs, and solid-state circuits. Heis the member of the Chinese Institute of Electronics (CIE)and is a senior member of the China Society of Micro-NanoTechnology.
Tianling Ren received his PhD degreefrom Tsinghua University, China,in 1997. He is a full professor ofInstitute of Microelectronics, TsinghuaUniversity since 2003. He was a visitingprofessor at Electrical EngineeringDepartment, Stanford University from2011 to 2012. His research is focused
on novel micro/nano electronic devices and key technologies,including MEMS/NEMS, memories, RF devices, and flexibleelectronics. He has published 300 journal and conference papersand 50 patents. He is an administrative community member anddistinguished lecturer of IEEE Electron Devices Society. Heis also a council member of Chinese Society of Micro/NanoTechnology.
Bo Pang received his MEng degreefrom Tsinghua University in 2013.He is now an engineer of China FirstAutomobile Works. His research interestsinclude the designing, packaging,and testing technology of MEMSsensors especially in automobilefields.
Li Yuan received his BS degree fromBeijing Institute of Technology in2011. He is currently a master student inthe Institute of Microelectronics, TsinghuaUniversity. His research interests are inpressure sensors and photodiode detectors.
