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An On-line Water Monitoring System Using a Smart

ISFET

Array

Sergio B errnejo,

Guillermo

Bedoya, Vicenq Parisi and Joan C abestany

Universitat Politecnica de Catalunya (UPC)

Jordi Girona 1-3,08034 Barcelona, SPAIN

{sbermejo.bedoya, parisi, cabestan}@eel.upc.

es

Abshnct

-In this work we present

a

new on-line water pollution

monitoring system. The system includes a smart array

of

ion-

selective field effect transistors (ISFETs) as

a

front-end and also

at post-processing stages

in

order to transmit the stored

measure s of ion concentrations. The intelligence in the s mart

sensors is provided by a blind source separation BSS)

algorithm which continuously learns from measures

how

to

detect the ion concentrations available

in

the mixed signal

observed i n the array's output. The computational simplicity of

the BSS algorithm and its capability

of

continuous learning

from the environment, allow the design of

a

low-power, cheap

and smaU system that monitors water in real-time, and is

a

contras t to the clas sical ON-line approach based on a water

analysis of the extracted measures in the laboratory. The work

is i n progress, as part of the SEWING project (IST-2000-28084)

I. INTRODUCTION

An enormous interest in managing hydrological resources

properly and detecting their pollution levels have been

aroused in recent times [ I ] . Monitoring of toxic substances in

industrial effluents is becoming a priority. C onsequently, the

next generation of water monitoring systems must be

designed in order to give precise information about the

quality o f the water to the end user, which im plies accurately

monitoring certain physical and chemical parameters

detected in the water.

A .

Commercial WaferPollution Monitoring System

In

a typical water pollution monitoring system, four phases

can be distinguished: measurement of parameters, storage of

information, data transmission and finally treatment and

evaluation. Today these monitoring equipment include: pH

sensors, temperature sensors, dissolved oxygen sensors,

conductivity sensors and others

[2].

In these systems, data

can be processed in two ways:

1)

Off-line processing:

The system only performs a

compilation of samples. Hence, the study of the collected

samples is done later in a laboratory using analytical

techniques like spectrophotometry, chromatography and

electrochemical. However, these techniques are often quite

expensive since complex analytical processes must be

performed using sophisticated laboratory equipment.

2)

On-line processing: The quality of the water is

examined in real-time by the monitoring system using low-

cost electronic devices which can store, analyze an d send the

relevant information extracted from the gathered measures.

This approach is mandatory when either the laboratory

instruments and procedures are extremely expensive or the

off-line processing takes too much time. However, even in

the cases in which the processing time

in

the off-line system

is assumable, the availability of low-cost, portable, real-time

0-7803-7474-6/021$17.00 02 002

IEEE

measuring instruments could also be

of

great interest since it

would reduce considerably time spent on laboratory

processing. Presumably the design of on-line systems, which

is currently an active area of research, will also have a great

impact on the off-line data analysis performed in the

laboratory.

B The SE

WING Project

In the SystEm for Water MonitorING (SEWING) project

(IST-2000-28084), a

full

system based on a smart array

of

ion-selective field effect transistors (ISFETs) array is

proposed to overcome the limitations of the present

commercial approaches to water monitoring. This project

proposes a novel synergistic combination of recent

progresses in different areas like semiconductor-based sensor

technology and artificial intelligence, in order to design a

low-power, cheap, small and smart system that monitors

water quality in real-time.

The goals of the project include detecting a large variety of

non-organic polluting ions with a broad range of sensitivity

for ion concentrations, which will make sensors suitable for

all types of water resources and waste water in high-risk

industrial regions, giving the possibility of early warnings.

The m icro system will be flexible, reliable, and will take into

account undesirable effects such as interferences

of

other ion

concentrations in the desired measure, dependence of the

response

on

temperature and ageing. It will

be

implemented

and verified by end-users, and prepared for industrial

implementation. Such a system will allow the design of a

general (or regional) European policy in water m anagement.

The SEWING Consortium is formed by 9 partners (7 of

them come from academia and

2

from industrial activity):

Politechnika Warszawska (coordinator), Instytut Technologii

Elecktronowej, Technical University of Lodz from Poland;

Valtion Teknillinen Tutkimuskeskus from Finland; CNRS-

LAAS from France; Universitat Politecnica de Catalunya

from Spain; IWGA from Austria; and the two companies:

Microsens from Switzerland and Systea from Italy. The

Project is multidisciplinary, and each partner was chosen in

relation to specific parts of it, according to their respective

area

of

expertise.

The contents of this paper are organized as follows.

Section 2 presents the SEWING architecture, which is based

on

array of ion-selective field effect transistors (ISFETs) plus

a blind source separation

(BSS)

algorithm as a front-end for

detecting concentrations

of

ions in water. Section 3 reviews

the basics of ISFETs while BSS fundamentals are presented

in Section 4. The smart array of ISFETs with some

preliminary experimental results are shown in Section

5

Finally, preliminary conclusions are given.

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11. AN OVERVIEW OF THE SE WING ARCHITECTURE

Figure 1 shows the three hierarchical levels in which the

SEWING architecture is divided:

1)

A smart sensor array for detecting ion concentrations

2) A data acquisition system with local processing

3) A central data processing and storage stage

capabilities

A .

The Smart Sensor Array or Detecting Ion Concentrations

The front-end of the SE WIN G architecture must be able to

detect several ion concentrations in the water. In order to do

that, several silicon sensors that are sensitive to chemical

components (ISFETs [3]) are used. As we will see later,

each ISFET mainly responds to a particular chemical

component. Therefore, an array of different ISFETs must be

employed to detect several ion concentrations. In this way,

the response of each output in an idealistic array would

correspond only to detected chemical compounds in the

water. However, the actual response is a signal formed by a

mixture of ions detected in the w ater (see S ection 111). On the

other hand, the response to a particular ion concentration

greatly varies between different ISFET of the sam e class so

calibration is a m ust. All these factors advocate the design of

a smart sensor array for detecting ion concentrations

available in the mixed signal observed in the array's output.

The so-called smart sensors

[4]

were bom with the

integration of artificial intelligence processing techniques

into, traditional sensor systems. Th e underlying idea of these

systems is to overcome th e inherent limitations of sensors by

introducing statistical signal processing techniques that:

1) enhance their output signals in order to facilitate the

extraction of relevant information in further stages

2

provide functions like self-calibration, self-diagnostic or

self-adaptation which are not available in the usual integrated

sensor with embedded data processing circuitry.

A s in

our

problem, smart sensors are typically involved in

the monitoring and control of

complex

real-world processes

which require the processing of signals from multiple sources

provided by an array of sensors. Hence, some kind of array

processing of the sensors' output signals must be performed.

One of the emerging methods for array processing in the last

few years is BSS

[SI

that can be very effective in separating

sources from a mixture of observed signals, which is our

case. See e.g. [6][7] for recent applications of

BSS

techniques in smart sensors.

B

The Data Acquisition System

The data acquisition system is the link between the output

of the smart sensor and a remote central computer. It is

possible to have various data acquisition systems and they

can be chosen depending on the smart sensor architecture

[SI.

In the SEWING project we have a sensor architecture in the

form of an array. With the help of a multiplexingkonversion

circuit, we can feed signals coming from the sensors.

V

R

0

R

Y

E

T

Fig.

I Hierarchical

levels

of

the

SEWING

ystem

There are two traditional data acquisition methods widely

used in mo dem automatic control and measuring systems:

1) Methods with time division channeling

based on the

multiplexing of the data acquisition channel over time

2 Methods with space-division channeling

based on the

simultaneous data acquisition from all sensors at the same

time.

In both cases, the permanency of data sources, i.e. the

opportunity to access information at any time depending on

the control an d the measuring task, is used.

The micro-controller (processing element) can store the

sensor's characteristic data in its internal ROM and then

transfers the corrected signal, which has been previously

processed, to the bus.

111 ION

SENSITIVE FIELD EFF ECT TRANSISTORS

A. Principles of Operation

A

new era in sensing began in

1970

when Bergveld

reported the first ISFET [3], which merged solid-state

electronic technology with chem ical sensors. Several decades

later, the principles of operation of such devices are clear

enough

to

use them in practical applications as

[9][10]

reported.

ISFETs are sensitive to the concentration of a particular

ion in a solution, which is done by replacing the metal gate of

a field-effect transistor with a membrane sensitive to a

particular kind of ion. Accordingly, the mode of operation

of

this device is based

on

being submerged in a chemical

solution (see Fig. 2). When there is a high concentration of

positive ions in the solution, many

of

them are accumulated

on the gate, causing an amplification of the channel . To

assure that the channel of the transistor is correctly polarized

on the sensitive surface, the solution is linked to a potential

of reference by introducing an electrode. In this way, the

potential of reference is adjusted to keep the source current

constant, so that the ionic concentration will be directly

related to the potential of reference with regards to the

potential of substrate.

"g*

i

. .

. . ,

I

Fig. 2. Representationofan ISFET measuring system

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B The

Ion

Sensitive Membrane

An important problem in the ISFETs design and

manufacture is the safety in which the membrane is adhered

to the sensor. If the integrity of the membrane is

compromised, the device will be useless. On the other hand,

the ion sensitive membrane must only respond to one kind of

ion. Many different types of oxide coatings (e.g. silicon

dioxide, silicon nitride and tantalum oxide) are used

[I 1][12][13] in order to generate a specific ion-selectivity.

C. Response

o

an Arrav o ISFETs as a Linear Mixture

o

Ion Concentrations

According to [141, the drain current of an ISFET

i

which is

active in ions of class k can he expressed in a linear range as

Id, =a,[V ,,-V, -O.SVds)Vds= (1)

= a, Vgs- EO i + b i l n k + ~ K k J a j ~

0.5Vds Vds

j

zk

where

V

is the gate-source voltage,

Vds

is the drain-source

voltage, EO i s the m embrane potential referring to th e hulk

solution consisting of a single type of ions of ISFE T i, a k is

the activity of the main ion k,

Kkj

is the selectivity coefficient

which relates the response to the interfering ions a,,

Zk

s the

valence of the main ion k and

Zj

is the valence of the

disturbing ion

j

in the solution. See [15][16] for additional

information about models of ISFETS.

The use of the first-order approximation of the natural log

function around a work ing point

q,

In(x)=In(q)+ ---I + 0 ( x 2 )

t

allows the transformation of

(I),

after some simple algebraic

manipulations, into the following expression:

Id, = A ,+ B , a k + z K y a J 5

i J

(3)

where i and

Bi

are constants that depend

on

the physical

and electrical characteristics of the ISFET.

111.

BLIND SOURCE

SEPARATION

A . BSS as a Statistical L earning Method

Blind source separation

(BSS)

attempts to reconstruct a set

of hidden signals

{si}

rom several observed signals

{x j

that

(presumably) have been generated from a linear mixture of

the original signals. The term blind refers to the fact that

we must recover the unseen signals from the observed ones

and also that there is

no

(or little) prior information about

how the mixture has been produced. Howe ver, this

deficiency is compensated to some degree by the existence of

a set of (empirical) samples of the observed signals D,={xi},

which allows learning from data in order to recover the

original signals. A BSS method, as. a statistical learning

procedure, mainly cons ists of three parts:

1) A probabilistic model

o

the data (i.e. sources) that

denotes in which way the

data

is distributed and how the

original sources are related to the mixing signals. There are

.two main approaches for determining the data distribution:

parametric and non-parametric models. In parametric

modelling a particular distribution is assumed (e.g. uniform,

exponential, etc.) while non-parametric approaches attempt

to produce consistent estimates of any distribution given

enough training samples to construct the non-parametric

model.

On

the other hand, the simplest model assumed

between so urces and mixing signals is linear.

2

An objective function

in which the minimum (or

maximal) point assures the achievement of a good solution

for the BSS problem.

3)

An optimization or learning algorithm

which

minimizes (or maximizes) the objective function in order to

compute the solution.

B BSS Models

In the simplest

BSS

model

[SI,

we observe m discrete-time

signals xl[n],

...,

x,[n] that correspond to a linear mixture of

a p source signal

sl[n],. ,

s,[n]. i.e.

x , [n ]= a , IsIn]+ ...+alpsp[n]

(4)

x,[n]= a,,s, [n]+

...

a,,sp[n]

or expressed in a vector form

x[n]=(x,[n]

...

x m [ n r

= s[n]

(5)

where is known as the mxp mixing matrix and

T

denotes

transpose.

A

simple extension of

(5)

includes the presence

of

a noise vector n as an additive presence in the BSS model,

seee.g.[17],

x[n]= As[n]+ n[n] (6)

Other extensions of the basic model denoted in 5 ) include

the generalization to a non-linear mapping [ I S ] A non-linear

BSS can be expressed as the estimation of the following

generative model for the data,

x[n]=

f(s[nD

(7)

where

f

is an unknown function from

Rp

o

Rm.

C.

A

BSS

Learning Algorithm

According to the classical blind source separation BSS)

model

(5),

and given a temporal window of the observable

vector x, i.e. D,={xi[n], n=l,.__,, i=l ,

...,

m}, we must

compute

a

pnm separating matrix B which allows a

estimation of the source signals {%[n], i= l, ..,p } using the

following reconstruction algorithm,

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Clearly, the solution to the BSS reconstruction problem is

B=A-l if

no

noise is assumed.

The point of departure for computing B is the assumption

that the source signals

s= sI

are independent, which is

often true in an array of sensors since unrelated physical

information can be detected. I f s is formed by independent

random variables, then its pdf can be expressed as the

product

of

the marginal distributions,

i=l

In order

to

compute B using

D,

an objective function must

be defined and minimized by the

BSS

learning algorithm.

The K ullback-Leibler (KL) divergence is a natural candidate

for this purpose since it measures the divergence between

two

probability distributions py(y) and q(y) a s follows,

Note that KL=O if and only if p=q and

>O

otherwise.

Hence, if pdy) is the pdf o he reconstructed signal and q(y)

i s the probabil ity of , t he source s ignal s n i f s , he KL

divergence will measure how close y=Bx is to the original

source s. It can b e sh own .[I91 that

IO)

can be estimated

using the set

of

samples DT a s

an on-line learning algorithm [ZI], we must use the

instantaneous empirical estimate of

IO) ,

which only uses one

training sample,

Thus, we can apply the stochastic gradient descent method

to compute B,

w here ~ [ n ]s the step size function of the learning algorithm.

It is worth noting that the use of the stochastic approach

avoids the need to store all o f set DT n the memory and only

the. sample x[n] is necessary. However, in order to ensure a

good convergence of the algorithm, it is desirable to store as

much training samples as possible and perform the on-line

approach using a sample procedure (e.g. a cyclic sampling)

over

Dr.

As it is shown in [19], (13) gives

wh ere f(y[nD= (fl(y,[nD...fp(yp[nl))t s obtained from qi(yi) as

Observe that

(14)

involves the computation o ft he inverse

of

the matrix B[n]', which can be time-consuming since we

would typically apply an iterative algorithm in order to

compute (B[n]'}-' numerically. On the other hand, it has been

observed [20] that the ordinary gradient descent does not

work for non-Euclidean spaces since the descent direction in

such a situation is represented by the usual gradient direction

multiplied by the inverse of the Riemannian metric G(B). In

BSS, G (B) can be easily computed and then 13) can be

modified as

B[n+l]=B[n]-v[n]dR(B[nDB [n]B[n]

74

Equation 16) is

known

as the natural gradient descent

learning algorithm for BSS [19], which give s

B b + I ]= B b - v b l b - f( ~ b b '[ n ]} B [ n ]

(17)

where

I

denotes the identity matrix. Equation (17) involves

performing p[2m+p(l+m)] multiplications, pm(l+p)

additions, p(p+m) subtractions and p non-linear transforms

f j ( y i ) .For instance, if the true pdf of the sources is unknown ,

we can select f , y i ) = a y i yjlyil for sub-Gaussian source

signals with neg ative kurtosis

([19],

p.2034), which implies

that the p non-linear operations are in fact 3p m ultiplications

and

p

additions. However, other more complex functions can

be employed, e.g . f i (y i )= ay i t a nh hi ) for super -Gauss ian

sources and consequently additional computations will be

needed.

2

IV. THE BBS-BASED SMA RT ISFET ARRAY

In the SEWING project, we are considering an array of

ISFET sensors, as the front-end of the data acquisition

system, which aims to detect several ion concentrations

on

the water. As we will show below, the output

of

this array

can be considered as a mixture of several ion co ncentrations

and an additive noise caused by the interference in the

sensing process of the multiple ions located in the water

sample. Given this mixture corrupted by noise, the central

problem is to recover the original signals, i.e. the ion

concentrations using a BSS algorithm.

A . The

ISFET

Array as a Linear Mixture o Ion

Concentrations

According to

(3),

the outputs of an array of ISFETs can be

expressed as

where m denotes the number of sensors in the ISFETs array.

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Sin ce the sum of interference ions a;vzi can be cons idered

as noise, (18) can he reduced to a linear

S S

model with

noise given by

(6).

Consequently, we have a problem of

source separation that is suitable to be solved by

BSS

techniques. Note that if we only hav e one class of

ISFETs,

which responds to one kind of ion, in the array, the BSS

problem is reduced

to

one source signal (1-dimensional

case).

B

Simulations

The goal of this experiment was to get some idea about the

possible quality of the separation taking into account the

presence of different ions in the sample solution. The

experiment consists in simulating the CaZC ISFET (or

ChemFET) sensor array introducing into the algorithm four

different signals (Fig. 3). The first one re resents the Ca2'

concentration (based on the CH EM FET Ca characteristics),

the second one represe nts'th e NH4' concentration (based on

the CHEMFET Ca2' characteristics), and two additional

signals (sin and saw-tooth) that represent disturbing ions.

Four sensors compose the array and the goal is to separate

and to recover the four different souce signals. The

CHEMFETs curves were obtained by taking samples from

the cu mes presented in [I 41 between the intervals of values

that correspond to the linear region. Data blocks of length

from 1 to 200 were generated by means of a linear spacing

relation and the four signals were adjusted to have a zero

mean and unit variance. Then, the generated signals were

mixed using a random mixing matrix. We made a set of

preliminary experiments with the FastICA algorithm [22],

which is related to the BSS learning algorithm described in

Section III.C, in order to restore the original signals. Results

are shown on Fig. 4, which demonstrate that restoration is

possible.

C Hardware Considerations

s it's shown through this paper we must use hardware able

to implement adaptive algorithms and learning competences,

to

read analog input signals, at the cheapest price possible,

and with the minimum wastage of energy, since

it

must be

portable and expendable, if possible. This leads us to, at least

two approaches.

p

0 3

Fig .

The

four source signals used

in the

experiment.

Fig.

4.

The recovered signals using FastlCA.

Starting from the processing perspective, in the last

paragraph, one tends to settle on digital signal processors

(DSPs) as a common solution for adaptive algorithms. These

processors were initially conceived to satisfy the numerical

demands of signal treatment, based

on

Harvard architecture

which allows efficient computations, and have been evolving

to offer additional inpudoutput capabilities as a response to

market demands. Nowadays several devices are available,

announced as

lowpower ,

at very attractive prices.

On the other hand, if we choose power as a primaly

characteristic, we'll surf above the micro-controller units

(MCUs) market, finding devices really ecologic at a cost of

several cents of Euro. They were designed initially to

integrate versatile inpuUoutput peripherals and lately they

have been incorporating more complex Central Processing

Units, so in this way they a re an alternative to DSPs.

An initial exploration of the market leads

us

to the c5000 as

the most preferred DSP and to the MSP430 as the most

appropriate MCU, both families of devices come from Texas

Instruments (TI).

'The Texas Instruments MSP430 series [23] is an ultralow-

power microcontroller family consisting of several devices

designed to be hattely operated for use in extended-time

applications, it consumes less than 400 pA in active mode

operating at 1 MHz in a typical

3-V

system.

On the other hand, the C5000 DSP architecture [24], is a

precious alternative due to its high performance and low

power achieved through increased parallelism and total focus

on reduction in power dissipation. The CPU supports an

internal bus structure that is composed of one program bus,

three data read buses, two data write buses, and additional

buses dedicated to peripheral and DMA activity. These buses

provide the ability to pe rfo m up to three data reads and two

data writes in a single cyc le.

V.

CONCLUSIONS

A new on-line water pollution monitoring system has been

introduced. As a front-end, the system includes a smart array

of ISFETs in order to detect different ion concentrations in

real-time. Since, the drain current of an array of ISFETS can

he reduced to a linear model in which concentrations of ions

appear mixed in the output's array,

BSS

methods could he

employed to recover the original concentrations. The BSS

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algorithm introduced in Section IILC can learn in real-time

from measures how to detect the ion concentrations available

in the mixed signal observed in the arrays output.

Preliminary experimental results have shown how this kind

of learning algorithms can work in the context of an ISFET

array. Due to the computational simplicity of the proposed

BSS algorithm, the design of a low-power, cheap and small

system is suitable using standard processing systems like

DSP or MCU.

VI . ACKNOWLEDGMENT

This work is supported by the IST Programme, under

Information concerning the project can be found in

contract No. 2000-28084 (SEW ING) of the EU.

htm://www sewine mixdes org

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