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Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays Guillermo Bedoya Advanced Hardware Architectures (UPC)

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Presentation on theme: "Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays Guillermo Bedoya Advanced Hardware Architectures (UPC)"— Presentation transcript:

1 Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays Guillermo Bedoya Advanced Hardware Architectures (UPC) Laboratoire des Images et des Signaux (INPG) UNIVERSITAT POLITÉCNICA DE CATALUNYA UPC INPGrenoble

2 Outline Introduction - Overview - What is Blind Signal Separation (briefly) Identifiability & Algorithmic solutions -When can it be done -How do we do it (briefly) Applications to semiconductor-based sensor arrays -The ISFET/CHEMFET devices -How does BSS apply to semiconductor-based chemical sensing

3 Overview Potential advantages of integrated circuit technology applied to the field of physiological data acquisition and water monitoring systems : small size reliability low cost & rapid time response multi sensor chip on chip signal processing Our objective is to design a low cost/high performance smart sensor system for Biomedical and environmental monitoring applications, using ISFET/CHEMFET sensor arrays.

4 Introduction Blind Source Separation deals with the « separation » of a mixture of sources, with a little prior information about the mixing process and the sources signals Sensors S1S2SNS1S2SN Sources Environment Source Separation Algorithm W x2x2 xNxN x1x1 Ŝ 1 Ŝ2Ŝ2 ŜNŜN Observations x = As Ŝ = Wx

5 What is Blind Source Separation x 1 [n]=a 11 s 1 [n]+…+a 1p s p [n]. x m [n]=a m1 s 1 [n]+…+a mp s p [n] x[n]=(x 1 [n]…x m [n])=As[n] y[n]=(y 1 [n]…y m [n])=Wx[n]=s[n] sxy AW

6 Identifiability & Algorithmic solutions Principles of information theory can be applied to the BSS problem. We suppose that source signals are independents (realistic assumption). Then, we minimize a measure of the independence (e.g., the mutual information (MI) I(.)) of the outputs I(y), where y=Wx. sx= Asy= Wx

7 How do we do it? We consider a processing function g, which operates on a scalar X using a function Y=g(X;w) in order to maximize the MI between X and Y. Parameter w is chosen to maximize I(X;Y). I(X;Y)=H(Y) – H(Y|X) The MI is maximized when H(Y) is maximized (for g deterministic). H(y)=sum [ H(y i ) ] - I(y 1,…, y N ) In order to maximize H(y) (we can maximize each H(yi) or minimize I(y1,…, yN)). g must be the CDF of x. The mutual information is minimized when all the outputs are independent ! W g(x,W) xy

8 How do we do it? An adaptive scheme is to take: Δw α Δw = ; ∂ (ln |∂y/∂x|) ∂w 1w1w then, Δw α + x(1-2y) Assuming a particular functional form: y = g(x) = 1/(1+e-(wx)) ∂y/∂x = wy (1-y) ∂H(y) ∂w And we have a weight update rule : w[k+1]=w[k]+ stepsize Δw

9 Applications to semiconductor- based sensor arrays Ion-Sensitive Field Effect Transistors (ISFETS and CHEMFETs) are basically metal oxide semiconductor field-effect devices. The construction of an ISFET differs from the conventional MOSFET devices, in that the gate metal is omitted and replaced by a membrane sensitive to the ions of interest. Potentiometric sensors!

10 ISFET/CHEMFET sensors SD (1) Reference Electrode (Vref) (2) Solution (Electrolyte) (3) Membrane ( MOSFET gate metal ) (4) Gate Insulator VGVG VDVD IDID Silicon Substrate

11 ISFET/CHEMFET sensors (Short view) ID for the conventional MOSFET is: I D = α[(V G –V T )-0.5V D ]V D (1) where α = μC o W V D /L We have to establish new expressions for VG and VT to adapt equ. (1) to the ISFET. MOSFET V G ground-to-gate metal potential ISFET V G * ground-to-membrane potential V G * = V G + (Electrode-electrolyte potential)+ Nernst Potential

12 ISFET/CHEMFET sensors (Short view) V T = Φ ms + 2 Φ F - Q ss +Q B Co V T *= Φ cs + 2 Φ F - +Eref – E 0 Q ss +Q B Co V G * = Vref + RT ln (a i + K ij a j ) nF zi/zj I D * = α[(V G * –V T *)-0.5V D ]V D (2)

13 Main characteristics Sensitivity: Lowest level of chemical concentration that can be detected. The smallest increment of concentration that can be detected in the sensing environment. Selectivity: The sensor’s ability to detect what is of interest and to separate that from interferents

14 Comparison of Chemical sensors The use of different type of sensors is recomended to obtain more robust semiconductor-based chemical sensor arrays

15 Current problems Miniaturized chemical sensors have yet to achieve their full potential. They must accomodate: High noise levels in chemical composition of the field environment. Highly variable environmental conditions (temperature, humidity)

16 Solutions: 1. Classical methods : calibration (LUTs, polygonal interpolation, progressive calibration) compensation (Structural, adjust, etc) 2. ISFET SOURCE SEPARATION techniques Advantages: Cancel crosstalk (added noise caused by interferent ions Cancel cross-sensitivity (when the response varies with the temperature – drift). Low cost/high performance implementation Sensor fabrication Application of signal processing techniques New materials. New devices.. High cost !

17 How does BSS apply to semiconductor- based chemical sensing? I D1 * = α [ ( Vref + RT ln (a i + K 1 a j ) - V T *) - 0.5V D ] nF zi/zj We have the observations matrix: I D2 * = α [ ( Vref + RT ln (a i + K 2 a j ) - V T *) - 0.5V D ] nF zi/zj I DN * = α [ ( Vref + RT ln (a i + K N a j ) - V T *) - 0.5V D ] nF zi/zj Non-linear BSS Spatial diversity

18 Adaptive algorithm for Non-Linear BSS Linear mixing stage Non –linear distortion Non –linear compensation Linear de-mixing stage AW f1 f2 g1 g2 aiai ajaj âiâi âjâj I D1 I D2 g I D1 g I D2 I D = Φ a + Φ b* ln(a i + K ij a j ) ; zi/zj g I D = e (I D – Φ a )/ Φ b

19 Adaptive algorithm for Non-Linear BSS 1.Initialization W = I, g ID = I D, Φa and Φb. 2.Loop 1.Compute outputs by : y =Wx; 2.Estimation of parameters Φa[k+1]= Φa[k]+ stepsize ΔΦa Φb[k+1]= Φb[k]+ stepsize ΔΦb 3. Normalization 4. Linear BSS algorithm: w[k+1]=w[k]+ stepsize Δw 5. Repeat until convergence

20 Preliminary Results The algorithm recovers the wave forms of the main ion activity (ai) and the interferent ion activity (aj). aj is considered as noise in other approaches. Selectivity is improved. The algorithm works well for sensor arrays with poor selectivity coefficients. Sensor arrays with poor performance bring to the algorithm spatial diversity and more statistical information. We can use low cost sensors. The adaptive scheme allows the separation when environment characteristics varies (e.g., temperature). We hope to study the algorithm behaviour as a function of the device drift. The scheme can be adjusted to build a multi-parametric system, adding more sensors (sensitive to other ions) and adjusting the algorithm parameters. Hardware implementation using a DSP card is currently being developed. Previous implementations had shown good performance.

21 Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays UNIVERSITAT POLITÉCNICA DE CATALUNYA UPC INPGrenoble


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