A novel silicon membrane-based biosensing platform using distributive sensingstrategy and artificial neural networks for feature analysis

Zhangming Wua, Khujesta Choudhuryb, Helen R. Griffithsb, Jinwu Xuc, Xianghong Maa,∗

aSchool of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK,bSchool of Life and Health Sciences, Aston University, Birmingham B4 7ET, UK,

cDepartment of Mechanical Engineering, University of Science and Technology Beijing 100083 Beijing, China

Abstract

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigatedin this paper. Distributive sensing scheme is designed to monitor the dynamics of the sensing structure.An artificialneural network is used to process the measured data and to identify cell presence and density. Without specifyingany particular bio-application, the investigation is mainly concentrated on the performance testing of this kind ofbiosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involveseeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamicsinformation of each tested silicon membrane in the form of a series of frequency response functions (FRFs). Allof those experiments are carried out in a cell culture medium to simulate a practical working environment. TheEA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial celllines represent a particular class of biological particles that have unregular shapes, non-uniform density and uncertaingrowth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal thatthe methodology of a neural-network based algorithm to perform the feature identification of cells from distributivesensory measurement, has great potential in biosensing applications.

Keywords: Biosensors, Microscale membrane, Distributive sensing, Neural network, Endothelial cell lines

1. Introduction

Research into the use of biosensors for the detec-tion of various biological particles/molecules have re-ceived extensive interest in recent decades, due to therapid progress of micro/nano technologies. A biosensorusually consists of a bioreceptor and a sensing trans-ducer, in which the bioreceptor is the interface that thebiosensor interacts with the biological environment andthe transducer is used to convert the physical/chemicalinformation of the biological particles into a measur-able signal[1]. Microcantilevers are the most widelyused transducer in mechanical-type biosensors, due totheir ultra-small size, high sensitivity and label-freebiological application by surface functionalization[2].This kind of biosensor can be fabricated in arrays ofmicrocantilevers[3] and can be integrated into a CMOS-based microsystem. In general, microcantilever can

∗*Email address: x.ma@aston.ac.uk (Xianghong Ma)

work under two different modes: static mode and dy-namic mode[4, 5]. In a static mode, surface stressesare accumulated after binding biological particles tothe microcantilever surface and it will inevitably in-crease the static deformation of microcantilever. Thedynamic mode use a microbalance approach, which de-tects surface-attached mass using resonant frequencyshift. However microcantilever based biosensors sufferfrom low sensitivity in liquid environment and fragilityin practical operation[6].

Micromachined membranes (plate/diaphragm) havegradually become a promising mass sensing structureto replace the microcantilever in recent years. Com-pared with microcantilevers, micro-membranes poten-tially have larger sensing area, higher sensitivity in liq-uid and less fragility. Moreover, it has the same advan-tages as the microcantilever in the application of masssensing. Some researchers have made attempts to ap-ply micro-membranes in biological detections. For ex-amples, Carlen et al [7] designed a micromachined sur-face stress sensor based on a thin suspended crystalline

Preprint submitted to Biosensors and Bioelectronics September 1, 2010

silicon circular plate to detect the bending behaviourcaused by vapor phase chemisorption of the alkanethiolmonolayers. Xu et al [6] developed a piezoelectricmembrane-based biosensor array for immunoassay ap-plications.

Distributive sensing techniques have been widelyused to monitor and reconstruct the static deforma-tion or dynamic responses of conventional structures,in the field of vibration control, damage detection andbiomedical analysis etc. One famous instance is a beam-like or plate-like smart sensing surface with few dis-tributive tactile sensors. The sensors are placed at se-lected locations and used to collect the data of surfacedeformation. Any change upon the sensing surface canresult in a corresponding change of measurements ineach sensor. The features or the properties of contactedobject are related to the sensory data. Advanced non-linear feature analysis methods, for example artificialneural networks, can be applied to infer the propertiesof the contacted subject. These kinds of tactile sensingsurfaces have been successfully applied to determinea description of force loading[8], localize a contactingsubject[9] or even human gait analysis[10]. Apparentlythe design of integrated microsystems for biosensingcan also borrow the concept of the smart sensing sur-face, in which multi-dimensional signals rather than sin-gle output from the sensing surface can be collected andused for post-processing. Therefore it has good poten-tial to extract more information such as distribution orpattern of biological targets, rather than just the massvariations.

Frequency response function (FRF) is one of the mostuseful ways to represent the dynamics of a rectangularmembrane. Cell adhesion on a membrane surface re-sults in the change of its mass, stiffness and damping,all of which can be reflected in a FRF. By comparing theFRFs of a sensing membrane with and without cell at-tachment, we can identify the features of adhesive cells.Many researchers have successfully employed neuralnetworks on the measued FRF data for structural healthmonitoring and damage detection[11, 12, 13, 14]. Thispaper presents the successful cell monitoring applica-tion. Before applying FRF data into training a neuralnetwork, the size of FRF data has to be reduced. Manymethods exist to perform the data dimension reduction,such as sub-dataset[11], modal analysis[15] and princi-pal component analysis[12]. In this paper, Karhunen-Loeve decomposition is used for FRF data dimension-ality reduction.

The purpose of this paper is to apply a microma-chined rectangular membrane as biosensing platformusing the distributive sensing method. This novel ap-

proach is first in the field of biosensing, which pavesthe way for developing more advanced biosensors withhigh accuracy and multiple functions. The paper isorganised as follows. Section 2 presents the fabrica-tion methods of biosensing platform including the dis-tributive piezoresistive sensors and PZT actuators forself-actuation and self-sensing. Section 3 presents theprocess of biological testing. Experimental tests ofbiosensing micro-membranes are used for detection ofEA.hy 926 endothelial cell lines in a natural liquid en-vironment. EA.hy 926 is a well-established humanendothelial-like immortalised cell line that exhibits ad-herence and migratory characteristics, resulting in non-uniform shapes in culture. Dynamic responses of themicro-membrane at a few specific points are measuredand recorded. Such information forms a set of distribu-tive sensory data. In analysing the sensory data, firsta shift of resonance frequency at each measured modeis used to perform a preliminary estimation of the celldensity. It is found that frequency based indices aloneis unable to accurately reflect the attached cell distri-bution on the sensing surface. Finally, in Section 4, aBack-Propagation (BP) neural network with one hiddenlayer is trained to recognize the cell distribution fromthe distributive sensory data of a series of repeated bio-experiments. It shows precise prediction on cell densityby using this neural network model.

2. Fabrication of membrane biosensing devices

The silicon membranes were fabricated using thestandard micromachining techniques from silicon on in-sulator (SOI) wafers. The membrane was created byinductively coupled plasma (ICP) using the Deep Reac-tive Ion etching (DRIE) process from the back side ofSOI wafer, stopping at the buried oxide layer. Bound-ary conditions of the membrane were also defined byDRIE from the top side of the wafer, using the buriedoxide as stop layer. The buried oxide layer was finallyremoved to form the boundary holes. Figure 1 illus-trates an approximate 200µm square membrane of can-tilever structure. Three different boundary conditions ofthe micro-membranes were fabricated and tested: twoopposite edges clamped and the other two edges free(C-F-C-F), cantilever (C-F-F-F) and all edges clamped(C-C-C-C). All of the membranes are designed to besquare and with lengths of 100µm, 200µm or 300µm.

Figure 2 demonstrates an integrated microsystembased on a square sensing membrane, which was man-ufactured with distributive piezoresistive sensors andPZT actuators. Such a microsystem enables the deviceto be capable of self-sensing and self-excitation. This

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microsystem can be embedded into an electronic circuitto build a lab-on-chip system.

For the fabrication of distributive piezoresistive sen-sors, a 500 nm-thick poly-silicon layer was depositedonto the oxidised device layer of a SOI wafer bylow pressure chemical vapour deposition (PCVD). Thislayer was then doped by ion beam implantation usinga 50Kev Boron source giving a doping density of 1e15to enhance the piezoresistive deflection sensitivity. Thesensor shapes were formed by photo-lithography andsubsequent reactive ion etching (RIE).

In the PZT film fabrication, a sandwiched structure ofa 100 nm-thick Pt/Ti bottom electrode, a 1µm PZT filmand a 100 nm-thick Pt top electrode was deposited onthe SOI. The top and bottom electrodes were depositedby evaporation using e-beam evaporator systems, thedeposited PZT was deposited as a spin on sol-gel whichis then annealed to produce the required PZT film. Thetop and bottom electrodes are patterned and etched byion beam milling. The redundant PZT material was wetetched.

Figure 1: Laser scanning image of a 200µm square pure membrane ofcantilever structure

Figure 2: SEM image of an integrated microsystem using a 100µmsquare membrane and attaching with distributive piezoresistive sen-sors and PZT actuators

3. Biological experiments

3.1. The process of bio-experiments

The human hybrid EA.hy 926 cell used in this paperis derived from the fusion of the human umbilical veinendothelial cells with A549/8 human lung carcinomacell line. EA.hy 926 is a permanent human endothe-lial cell line that expresses highly differentiated func-tions characteristic of human vascular endothelium. Hu-man EA.hy 926 endothelial cell lines are maintained in30ml Dulbecco’s Modified Eagle’s Medium (DMEM),supplemented with 10% FBS, streptomycin 100µg/mland penicillin 100U/ml, and 10ml HAT (100µM hypox-anthine, 0.4µM aminopterin, 16µM thymidine). Cellswere cultured in an incubator at 37◦C with an atmo-sphere of 5% CO2 and 95% air. Cells were grown ina 75cm2 flask and passaged when reaching ∼ 90% con-fluence. Once cells roughly reached 90% confluence themedia was removed and the cells washed with 5ml phos-phate buffered saline (PBS). The process of passage ofEA.hy 926 cells is that briefly cell culture media was re-moved from the cells and cells were then washed with10ml sterile PBS until the media appears without color.EA.hy 926 cells were then detached by the addition of2.5ml trypsin with a 3 minute standard incubation. Cellclusters were also dispersed for uniform distribution byrepeated pipetting with 5ml new DMEM media.

Figure 3 shows a LSM image that the endothelialcells coated on the surface of a micro-membrane. It canbe seen that those endothelial cells were tightly adheredto the silicon surface showing a typical spreading pat-tern.

Figure 3: Laser scanning image of endothelial cells coating on thesurface of a micro-membrane

The seeding of biological experiment is separatedinto two phases: seeding a certain amount of cells on the

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membrane (Figure 4-a,b) and measure the correspond-ing dynamics of this membrane. The dynamic test-ing device is illustrated in Figure 4-c. Identical micro-membranes were repeatedly used several times for ob-taining a batch of experimental results with differentdensities of cells. Each experiment was performed ac-cording to the following work flow:

1. Initially, silicon micromembranes were cleanedand sterilised using washes (ethanol and acetonemixture), autoclaving and UV light irradiation.

2. Before seeding cells on the micro-membranes, thecell density of suspension during the process ofpassage was established. The numbers of viablecells were estimated by taking 20µl of the cell sus-pension and mixing it with a 20µl trypan blue.Cells count was then performed from this new mix-ture by using improved Neubauer haemocytome-ter. Once the cell density was established, a 5mlcell suspension of EA.hy 926 cells of known den-sity is made up using the media. By controlingthe incubation time, various cell density and dis-tribution on the membrane surface can then beachieved.

3. Cell distribution on the membrane sensing surfaceis recorded using a LSM (laser scan microscopy)image. The density or distribution of cells can bequantitated based on this LSM image.

4. The dynamics of membranes with adherent cellsare measured. The FRF data for each specificmicro-membrane with cells and without cells arecompared to infer the information of cells, whichis recorded in the LSM scanned images.

5. Finally, the cells are removed from the surface ofmicro-membranes following the same procedure asthe first step. The re-sterilised micro-membranecan be used for the next experiment.

Figure 4: Endothelial cells coating on the surface of a micro-membrane: (a)The left upper image shows a silicon die (membrane)inside in a petri dish, (b)The left bottom image shows the same after aperiod of incubation, (c)The right image is the dynamic testing device.

3.2. Experimental results

Figures 5, 6 and 7 illustrate the frequency responsefunctions (FRFs) of three different types of micro-membranes under three different cell densities. Themost dominant change of the dynamics of membraneinduced by cell-loading is the shift of resonance fre-quencies. As the first mode shapes remain almostconstant[16], and the amplitudes of each FRF were self-normalized with respect to the amplitude of first reso-nant mode. Relative amplitudes of resonant modes arefound to be significantly varied after the cell loading.It means that additional mass loading of attached cellson the surface of membrane also results in the distor-tion of vibration shapes. The mass or quantity of targetcells can be estimated through the detection of the shiftof resonance frequencies. Eq. 1 demonstrates the rela-tionship between mass change and frequency shift of adynamic system, under the assumption that the stiffnessremains constant. This approach has been widely usedin the microcantilever based biosensors.

f =1

2π

√km,

∆mm

=k

4π2 (1f 21

−1f 2 ) ≈ 2

∆ ff

(1)

Comparing the changes of FRFs presented in Figures5, 6 and 7, it is concluded that different types (dimen-sion and boundary conditions) of the rectangular siliconmicro-membranes reflect very different biosensing per-formance. It implies that the first type membrane (a 100µm square C-F-F-F) has highest sensitivity among thethree membranes, in terms of resonance frequency shift.It is also noted that nonlinearity occurs on the dynamicsof fluid-loaded micro-membranes. In fact most experi-mental results of FRFs micro-membranes involved cellattachment have suffered with nonlinearity to a certaindegree. In general, the experimental results shown inFigures 5, 6 and 7 demonstrate the great potential abil-ity of micro-membrane in biosensing, even when theyare immersed in a high-damping liquid environment.

3.3. Preliminary analysis

Two resonant frequency based indices (Eq. 2) are uti-lized to perform a preliminary analysis on the experi-mental results in this paper. FDRn (Frequency Differ-ence Ratio) is evaluated as the normalized resonant fre-quency difference between the cell-loaded and cell-freemembrane at each measured resonance mode. AFDR isthe average of all measured FDRn.

FDRn =∆ fnfn, AFDR =

1N

N∑n

FDRn (2)

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Figure 5: Above: endothelial cells coating on the surface of a 100µm square C-F-F-F micro-membrane. Below: normalised velocityamplitude according to cell density.

Figure 6: Above: endothelial cells coating on the surface of a 200µm square C-F-C-F micro-membrane. Below: normalised velocityamplitude according to cell density.

Figure 7: Above: endothelial cells coating on the surface of a 300µm square C-C-C-C micro-membrane. Below: normalised velocityamplitude according to cell density.

The indices of FDRn and AFDR evaluation were per-formed on three batches of bio-experimental results us-ing three different micro-membranes, which are all ap-proximate 200 µm square C-F-C-F membranes. Thethree micro-membranes are labeled as No.I, No.II andNo.III respectively. In each batch of the experiment, anidentical membrane was repeatedly used four times andthe cell culture density was gradually increased from to25 × 103/µl to 200 × 103/µl. Figures 8, 9 and 10 illus-trate the trends of the FDRn with increasing the amountof cells of each tested micro-membrane. Figure 11 com-pares the AFDR index of these three micro-membranesin each batch of experiment.

First of all, some trends of the index FDRn at oneor two modes are not coherent with the increase of cellquantity. This phenomenon is quite different with thebio-experimental results of microcantilever, where theFDR0 of its fundamental mode always has a linearlyrelationship with cells number[17, 18]. The potentialreasons of this phenomenon are: (a)Micro-membranesusually have much larger sensing area and carry manymore cells than microcantilever in the bio-experiments.Apart from mass change, the accumulation of cellsmay also result in change of structural stiffness. Insuch cases, the linear relationship of FDR will be vio-lated. (b)The bio-experiments presented in this work for

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Figure 8: Above: FDR trends of No.I micro-membrane in four inde-pendent biological experiments. Below: typical cell growth observedat the four time intervals selected.

micro-membranes are maintained in a relevant environ-ment, for example the dynamics of microplates are mea-sured in cell culture media. (c)Nonlinearity of the dy-namics of submerged micro-membranes with randomlydistributed cells exists in most experimental measure-ments.

On the other hand, index AFDR is capable of givingan approximate prediction of the amount of cells. Thesensitivity of AFDR on these three micro-membranes isquite different. The values of AFDR for No. I and No.II membranes are very close, but that of No. III is muchlower. This is due to the fact that No. I and No.II mem-branes were taken from the same wafer, while No.III isfrom the other one. It reveals that using the index AFDRfor the micro-membrane as a biosensing platform is nota robust method. Calibration on such a biosensing de-vice is probably required before the estimation on celldensity.

Considering the submerged sensing membrane as ageneral oscillation structure, resonant frequency canbe approximately determined only by its stiffness andmass, the first equation in 1. If one assumes the sys-tem stiffness is a constant, the mass change ratio isproportional with frequency change ratio as shown insecond equation of 1. It is therefore believed that in-dices FDRn and AFDR are able to roughly reflect thecell density. However in realistic situations cells attach-ment would also affect the stiffness of sensing micro-membrane more or less, especially the endothelial cells.

It leads to more complication and FDRn and AFDRmore difficult to accurately indicate the cells density.

Figure 9: Above: FDR trends of No.II micro-membrane in four inde-pendent biological experiments. Below: typical cell growth observedat the four time intervals selected.

4. Neural network method

On the whole, resonant frequency based indices ei-ther FDRn or AFDR are only able to predict the celldensity with very limited accuracy. It is mainly due tothe complication and nonlinearities of micro-membranesensing system. Other algorithms are desired to per-form more accurate and reliable identification on celldistribution from the measured dynamics data. In thissection, a simple attempt that using an artificial neuralnetwork technique to build the relationship between thesensory data and cell distribution is carried out.

4.1. Quantitation of cell density

In the above experimental results, LSM images wereused to intuitively presented the cell population in themicro-membrane sensing domain. However a quanti-tative index is also necessary to indicate the amountof cells for a more precise analysis. This is especiallytrue for endothelial cells, the number of which are veryhard to count. A simple image processing procedurewas carried out on each LSM image to convert it intoa binary image using the MATLAB Image ProcessingToolbox. Initially the LSM image is loaded and a mostclear layer is selected for the following processes, as

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Figure 10: Above: FDR trends of No.III micro-membrane in fourindependent biological experiments. Below: typical cell growth ob-served at the four time intervals selected.

Figure 11: AFDR trends of three different micro-membranes in eachbatch of bio-experiments

the LSM image taken under the reflection mode usu-ally contains three layers. Then the background im-age of this LSM image is created by the morphologicalopening technique. Afterwards the background image issubtracted from the original image and the image con-trast is enhanced, this is in order to highlight the areaof cells occupied. Finally the corresponding binary im-age is created, in which the background is black and theparts of implanted cells are white. Therefore the cellspopulation on the sensing domain can be approximatelyevaluated by the white area ratio in this binary image.This ratio is called cell density ratio (CDR) in this pa-per. Figure 12 demonstrates the results of this evalua-tion processes on four different LSM images, which areobtained in a same batch of bio-experiments. It can beseen that the white region of each binary image roughlyindicates the shapes of endothelial cells distribution, al-though some local errors exist in the binary images. Theevaluated ratios of white region are also listed in the bot-tom of Figure 12.

However, these evaluated CDRs are not suitable to beused directly in the analysis due to the following points:(1)Apart from each cell height above the growth sur-face, the endothelial cells also generate a thin film overall of the culture surface. Each evaluated CDR is raisedup 10% ∼ 15% to consider this thin film loading ef-fect, for distinguishing from the case of no cells load-ing; (2) For the case that cells covered nearly the wholesensing domain, i.e. the 4th image in Figure 12, thepredicted value of CDR is usually much lower than theactual situation. Therefore the predicted value needs tobe increased. The modified CDRs for each experimen-tal sample are then used as the target values in neuralnetwork applications.

4.2. FRF Data Normalization and Order-Reduction

Although all of experimental settings are the same ineach time of dynamic experiment, the amplitudes of ev-ery FRF measurements are varied with experimental en-vironment and external disturbances. Consequently it isbetter to normalize the measured FRFs and scale theminto a same level for comparison and analysis. On theother hand, there are multiple FRF datasets in each dy-namical measurement and each FRF dataset contains avery large number of frequency spectral lines. In thiswork, frequency spectral lines are set to be 6400 foreach FRF and 4 sensory FRFs were recorded for eachtest. Obviously such FRF datasets are too large to di-rectly apply into the neural network. Therefore the di-mension of each FRF has to be reduced before the ap-plication of neural network.

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Figure 12: Quantization of cells population based on a simple image process technique

For the FRF normalization, each spectrum is normal-ized with respect to the amplitude of its own first reso-nant mode. The reason for choosing the first resonantmode as the reference is based on the theoretical anal-ysis results in [16], which prove that the mass loadinghas the slightest effects on the first resonant mode of arectangular membrane.

For the dimensionality reduction, Karhunen-Loeve(K-L) decomposition method is then used to extractthe principal components on a multiple-FRFs dataset.The Karhunen-Loeve (K-L) decomposition is a usefulmethod to create low dimensional, reduced-order mod-els of dynamical systems[19]. Assuming there are Mof FRFs with N frequency in each of dynamics mea-surement of membrane, then this dataset forms a M ×Nmatrix [H(ω)]M×N . The process of principal componentextraction of the matrix [H(ω)] using Karhunen-Loeve(K-L) method has the following steps:

1. Firstly, a correlation matrix [C]M×M is createdbased on the FRF matrix [H(ω)]M×N .

[C]M×M = [H(ω)]M×N[H(ω)]TN×M (3)

2. The principal components are then obtained fromcalculating the eigenvalues and correspondingeigenvectors of matrix [C].

[C][X] = λ[X] (4)

3. Finally, the M extracted eigenvalues are exam-ined. The eigenvectors associated with theselargest eigenvalues are then considered to be theprincipal components and be able to represent themost significant information of the original FRFdataset.

4.3. Dataset creation

The dynamics (FRF) of 4 different used membraneswithout any cells loading are also provided in the datasetas references. Two additional samples are also providedfor the purpose of validation. Consequently there are18 different samples in total are created for training andvalidation of the neural network. The eigenvectors re-lated to the largest eigenvalue of FRF dataset of eachsample are extracted as the neural network input andthe CDRs of every samples are calculated as the neuralnetwork targets.

4.4. Network design and training

The widely used back-propagation (BP) neural net-work was selected to predict cells density in this work.Figure 13 illustrates the concept of using BP neural net-work to predict the value of CDR. Besides the principalcomponents extracted from FRF datasets, the value ofindex AFDR of each sample provide an additional in-put to the neural network. As the index of AFDR hasbeen proved to be highly related to cells distribution inlast section, it can help the neural network to achieve afast convergence and good predictions. Among the 18samples in the dataset, the first 14 samples are used fortraining neural network and the left 4 sample are usedfor validation.

As the number of samples are limited, it is more sen-sible to design and use a simple neural network ratherthan a complicated one. The BP neural network usedhere is designed to have only one hidden layer with fewneurons. Several trials with different number of hiddenlayer neurons were carried out to test the differences on

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Figure 13: Schematic diagram of BP network used for cells identifi-cation

the normalized system error. It proves that the hiddenlayer with 5 neurons produces the best performance.

The training process of BP network herein establishesan approximate function (nonlinear regression) betweenthe inputs and targets, through iteratively adjusting theweights and biases of network to meet a setting goal(mean square error). The training parameters can affectthe network convergence speed as well as the final pred-ication accuracy. Bad parameters may lead to very slowtraining processes or over-fitting results. Several testswere then carried out to find optimal training parame-ters. The final training parameters used in this work areselected as: moment rate is 0.9, learning rate is 0.1, themax error is 0.001 and the max number of iteration is3000.

4.5. Prediction resultsFigure 14 demonstrates the prediction results of CDR

on samples of No.15 ∼ No.18 from the trained BP net-work. The prediction results match very well with theCDR values calculated from corresponding LSM im-ages, with erros within 10 percent.

5. Conclusion and disscussion

The experiments implemented in this research haveexamined the biosensing performance of a microma-chined rectangular silicon membrane in a normal cellculture environment. The principals of biosensing usedon the micro-membrane are based on the changes of itsdynamic properties caused by cell adhesion. In contrastto previous research of biosensors, the bio-experimentson each type of micro-membranes were repeated manytimes. Initially, the shifts of resonant frequencies wereemployed to analyse the experimental results. The an-alytical results demonstrates that the rectangular micro-membranes have capability on cell detection, under

high-damping liquid conditions. Nevertheless, a fairlylinear relationship of the micro-membrane sensitivity israrely achieved. It reflects the complexity of rectan-gular micro-membrane in the applications of biosens-ing. Those results also reveal the issue of that a certainamount of difference of the biosensing sensitivity be-tween two different micro-membranes exists, even theyare of an identical type.

Further biosensing analysis of the micro-membraneis based on the novel methodology that uses an artificialneural network with a distributed sensing scheme to es-timate the adhesive cell distribution. Karhunen-Loeve(K-L) decomposition method is successfully used to re-duce the dimension of measured FRF datasets. A BPneural network is trained from a set of selected experi-menal samples. The final predicted results on the othersamples prove that this methodology can be success-fully applied to identify the cell features. Significant ad-vantages are discovered by applying this methodologyin the biosensing analysis: (1)it is a robust algorithmand can repress the uncertainties in experimental mea-surements, comparing with using a single value as sens-ing parameter; (2)it is capble of eliminating the differ-ences between diffferent substances of micro-membranebased biosensors; (3)it can overcome the inherent non-linearity of sensing structure; (4)it is suitable to anal-yse the cells that are of very unregular shapes and non-uniform density, such as the EA.hy 926.

The work described in this paper is the first attemptof using the neural-network algorithm to perform thebiosensing function of rectangular micro-membraneswith a distributive sensory scheme. Much further re-search is required to develop more potential applica-tions of this methodology in the field of biosensing. Al-though many repeated bio-experiments have been im-plemented in this work, the number of samples remainsinsufficient large for training a sophisticated neural net-work. Current predicted results of cell adhesion is onlyfor the cell density spreading on the membrane sens-ing surface, which primarily reflects the weight infor-mation of cells. The distributive sensory data of mem-brane also provide the space information of the adhesivecells. Consequently, it is likely to predict the position,morphology and behaviours of living biological parti-cles by using the proposed methodology. Such informa-tion are more useful in the biological applications thanthe weight information.

6. ACKNOWLEDGMENT

The authors would acknowledge the funding supportfrom EPSRC committee.

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Figure 14: Predicted results on the CDR of No.15 ∼ No.18 samples using the trained BP neural network

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