Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays - PowerPoint PPT Presentation

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Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays

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

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Title: Non-linear Blind Source Separation Applied to Ion-sensitive Field Effect Transistor Sensor Arrays


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
x As
S Wx
Environment
x1
S 1
S1 S2 SN
Source Separation Algorithm W
x2
S2
.
.
.
.
.
.
.
.
.
.
.
.
xN
SN
Sensors
Sources
Observations
5
What is Blind Source Separation
x1na11s1na1pspn . . xmnam1s1namp
spn
xn(x1nxmn)Asn
yn(y1nymn)Wxnsn
s
x
y
A
W
6
Identifiability Algorithmic solutions
Principles of information theory can be applied
to the BSS problem. We suppose that source
signals are independents (realistic assumption).
s
x As
y Wx
Then, we minimize a measure of the independence
(e.g., the mutual information (MI) I(.)) of the
outputs I(y), where yWx.
7
x
y
How do we do it?
W
g(x,W)
We consider a processing function g, which
operates on a scalar X using a function Yg(Xw)
in order to maximize the MI between X and Y.
Parameter w is chosen to maximize
I(XY). I(XY)H(Y) H(YX) The MI is
maximized when H(Y) is maximized (for g
deterministic). H(y)sumH(yi) - I(y1,,
yN) 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 !
8
How do we do it?
Assuming a particular functional form y g(x)
1/(1e-(wx))
?H(y) ?w
An adaptive scheme is to take ?w a
?w
? (ln ?y/?x) ?w
?y/?x wy (1-y)
then, ?w a
And we have a weight update rule wk1wk
stepsize ?w
1 w
x(1-2y)
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
(1) Reference Electrode (Vref) (2) Solution
(Electrolyte) (3) Membrane (MOSFET gate
metal) (4) Gate Insulator
ID
S
D
VG
VD
Silicon Substrate
11
ISFET/CHEMFET sensors (Short view)
ID for the conventional MOSFET is ID a(VG
VT)-0.5VDVD (1) where a µCoW VD/L We have
to establish new expressions for VG and VT to
adapt equ. (1) to the ISFET.
MOSFET VG ground-to-gate metal
potential ISFET VG ground-to-membrane potential
VG VG (Electrode-electrolyte potential)
Nernst Potential

12
ISFET/CHEMFET sensors (Short view)
QssQB Co
VT Fms 2 FF -
QssQB Co
VT Fcs 2 FF - Eref E0
zi/zj
VG Vref RT ln (ai Kij aj )
nF
ID a(VG VT)-0.5VDVD (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 sensors 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
Sensor fabrication
Application of signal processing techniques
  • 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
  • New materials.
  • New devices.
  • .
  • .

High cost !
17
How does BSS apply to semiconductor-based
chemical sensing?
Non-linear BSS
We have the observations matrix
Spatial diversity
ID1 a(Vref RT ln (ai K1 aj ) -
VT)- 0.5VD
zi/zj
nF
ID2 a(Vref RT ln (ai K2 aj ) -
VT)- 0.5VD
zi/zj
nF
IDN a(Vref RT ln (ai KN aj ) -
VT)- 0.5VD
zi/zj
nF
18
Adaptive algorithm for Non-Linear BSS
gID1
ai
âi
ID1
f1
g1
A
W
aj
âj
gID2
ID2
f2
g2
Linear mixing stage
Non linear distortion
Non linear compensation
Linear de-mixing stage
(ID Fa)/ Fb
zi/zj
ID Fa Fbln(ai Kijaj )
gID e
19
Adaptive algorithm for Non-Linear BSS
  • Initialization W I, gID ID, Fa and Fb.
  • Loop
  • Compute outputs by y Wx
  • Estimation of parameters
  • Fak1 Fak stepsize ?Fa
  • Fbk1 Fbk stepsize ?Fb
  • 3. Normalization
  • 4. Linear BSS algorithm wk1wk stepsize
    ?w
  • 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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