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Sensor technology advances and future trends
Article in IEEE Transactions on Instrumentation and Measurement · January 2005
DOI: 10.1109/TIM.2004.834613 · Source: IEEE Xplore
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Olfa Kanoun
Technische Universität Chemnitz
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Sensor Technology Advances and Future Trends
Olfa Kanoun and Hans-Rolf Tränkler
Abstract—Recent advances of sensor technologies have been
powered by high-speed and low-cost electronic circuits, novel
signal processing methods, and advanced manufacturing technologies. The synergetic interaction of new developments in
these fields provides promising technical solutions increasing the
quality, reliability, and economic efficiency of technical products.
With selected examples, we will give an overview about the significant developments of methods, structures, manufacturing technologies, and signal processing characterizing today’s sensors and
sensor systems. Predominantly observed development trends in the
future are discussed.
Index Terms—Future trends, review, sensor signal processing,
sensor technology, smart sensors.
Fig. 1. Decisive fields for the development of sensor technology.
HE COMPETITION in markets requires the permanent
enhancement of quality and reliability of products. The
rising demand for automation, security, and comfort leads to
completely new applications for sensor systems. The number of
sensor systems required and the diversity in most applications
are permanently increasing. To keep up with the new requirements, the design of sensor systems is required to provide novel
approaches and solutions profiting from recent developments in
science and technology.
Sensors and sensor systems achieve their function through an
interlocked interaction of sensor structure, manufacturing technology, and signal processing algorithms.
The developments in sensor technology are consequently
based on the permanent technical progress in these fields
(Fig. 1). Particularly, in the last years, a significant upturn is observed in these fields involving a great potential for completely
novel approaches of sensors and sensor systems. Using new
technologies and signal processing methods, even well-known
measurement principles could be used, leading to considerably
improved sensor features.
In the kernel of a sensor system is the sensor element,
which changes its output depending on the measured quantity.
In a preprocessing unit, the sensor signal is transformed in
an adequate amplified and filtered signal using analog signal
processing techniques. Using digital signal processing, the
measured quantity can be calculated under consideration of
manufacturing variance, influence factors, and aging processes
Manuscript received June 15, 2003; revised March 24, 2004.
The authors are with the Institut für Meß- und Automatisierungstechnik, University of the Bundeswehr Munich, Neubiberg, Germany (e-mail: [email protected]).
Digital Object Identifier 10.1109/TIM.2004.834613
By means of low-cost analog-to-digital converters, signal
processing is increasingly shifted from the higher system level
in the sensor level. The diverse facilities in digital signal processing involve new approaches for the improvement of sensor
properties. Calibration and consideration of several effects,
such as manufacturing variance or cross sensitivity, become a
simple task.
Embedding other functions, such as online self-test or selfcalibration, is today winning a special importance, improving
the system reliability, and reducing installation and maintenance
The structure of a sensor with self-monitoring differs from the
standard structure in particular through the consideration of supplementary knowledge to the actual measurement information.
Generally, specific relationships are required about the sensor
behavior and the expected confidence limits of sensor properties [2].
The state of the sensor system can be inspected by a comparison of the real output to the expected value due to the
previously known relationships [1]. For instance, acceleration
sensors with a closed-loop structure compensate the inertial
force acting on the mass through an electrically generated
restoring force (Fig. 2). Through the application of restoring
forces with well-known values, self-tests can be carried out
For a self-calibration process, the real sensor outputs by
fixed well-known inputs are moreover used in order to calculate
sensor parameters. Through self-calibration, aging effects can
be compensated so that defined measurement accuracy limits
could be guaranteed during the whole operating time.
The trend toward built-in self-test or self-calibration function
leads to the design of totally calibration-free sensor systems.
In recent research dealing with temperature measurement based
on p-n junctions [3], a novel sensor principle has been developed, in which temperature can be calculated without needing
any calibrations during production or maintenance processes.
0018-9456/04$20.00 © 2004 IEEE
The a priori knowledge about the sensor behavior is represented
in this case by the model of the – characteristic. Temperature
is one unknown parameter, among others (Fig. 3). The measured
voltages at different supply currents are fitted to the – characteristic model, so that temperature is simultaneously calculated
online together with all unknown parameters in the characteristic model.
Many recent advances in the sensor technology become
mainly possible by means of micro technologies. These new
technologies offer high-volume manufacturable systems with
small dimensions, lower power consumption, and higher reliability. Thereby, realized microsystems integrate sensors,
actuators, mechanical, and electronic units. They provide
low-cost solutions that were not realizable with microelectronic systems. Their development involves special challenges
for device modeling, microfabrication, material, and packaging technologies. Micromachined systems are today already
inherent components in automotives, color printers, mobile
phones, and medical systems. The most popular micromachined
sensors are pressure, angular rate (Fig. 4), and acceleration sensors. They allowed the widespread implementation of low-cost
airbag systems and catalytic converters.
Silicon micromachining is one of the most significant micro
technologies for sensor systems [4]. The eminent properties of
the silicon material, such as the freedom of hysteresis errors,
and the earlier advances in the field of microelectronics have
permitted this important technical evolution.
In case of bulk micromachining, the substrate is structured
by means of wet and dry etching processes. The high etching
selectivity and reliability are the advantages of the bulk micromachining [6]. In an isotropic process, the etching speed is independent from the direction in the substrate. In this case, the
obtainable device configurations are limited and the silicon material may not be efficiently utilized. In an anisotropic process,
the etching speed is orientation dependent. The manufactured
structures in bulk micromachining have from the beginning a
high aspect ratio. This means that the structure height is high
relative to the minimal lateral dimension of the whole structure.
This property involves considerable advantages for sensor performance, such as higher sensitivity, displacement, mechanical
robustness, and reduced noise.
In case of surface micromachining, three-dimensional mechanical structures are developed by a sequential deposition and
selectively removing of sacrificial layers (e.g., SiO ) separating
the individual layers in the structure. Recently, the use of reactive ion etching (RIE) allowed a cost-effective realization of
structures with a higher aspect ratio of 30 [6].
The signal processing has the task of determining the measured quantity from the measured data in spite of all unavoidable effects, such as manufacturing variance, influence factors,
and aging processes, which represent an additional source of
systematical measurements errors.
Fig. 2. Acceleration sensor with a closed-loop structure.
Fig. 3. Calibration-free temperature measurement based on the p-n junction
I –U characteristic [3].
Fig. 4.
Angular rate sensor in surface micromachining [5].
A. Signal Processing for Individual Sensors
Whereas the sensor element can deliver a weak signal, the
transmitted signal should generally have a high signal level, and
perhaps suitable values, in order to reach superior units undisturbed and to simplify the following calculations. Therefore, the
sensor signal should be generally preprocessed. Thereby several
important tasks could be realized (Fig. 5), such as signal amplification, scaling, linearization, conversion, and conjunctions with
other components in a chain, parallel, or closed-loop structure
For instance, giant magnetoresistance (GMR) elements are
able to measure an angle with a high resolution [7]. A particular
property of these elements is that they can measure the direction of a magnetic field independently of its amplitude. In this
Fig. 5. Signal processing by individual sensors.
case, the sensor signal must be generally amplified and the temperature influence compensated. The actual calculation of the
angle is carried out by an analog signal processing in a half or a
full bridge circuit with GMR elements with different preference
Today, a current practice is the local digitalization of the
sensor signal. In addition to the disburden of the higher system,
the local signal digitalization has the advantage, that measurement data could be transmitted without remarkable precision
loss independently of the distance between the sensor and the
higher processing unit.
The signal processing is increasingly shifted from hardware
to software, so that measurement accuracy can be simpler improved. Manufacturing variances can be considered by a simple
parameterization instead of mechanical or electrical trimming
processes. Physical or mathematical models describing the
sensor behavior can be used, taking into account influence
effects and realizing a more precise measurement.
The possibility to use sophisticated signal processing
methods leads to completely new sensors using principles,
which are in fact already well known. However, technological
problems, such as manufacturing variance, or the low level of
the signal, prevented their effective use for measurements.
vide synergetic effects that enhance the quality and availability
of information about the state of the measurement environment.
The aim of the signal processing by multisensor systems is
to acquire determined information, such as a decision or the
measurement of a quantity, using a selected set of measured data
stemming from a multisensor system. Generally, a certain level
of precision or reliability is required that only one sensor could
not achieve.
For example, for presence detection, ultrasonic detectors have
a high sensitivity to noise, thermal-induced air turbulence, and
movements of hanging curtains and plants. Microwave detectors
can also be used for presence detection, but they may detect an
object motion outside the observed room or be misled by other
electromagnetic fields (mobile telephones, etc.). The combination of both detectors and the use of adapted signal processing
[8] achieve a better detection reliability because of the different
ways in which both detectors are affected by disturbances.
Sophisticated signal processing based on data fusion techniques can generally improve the measurement accuracy more
than the more common simple threshold-based algorithms. The
process of multisensor data fusion should be specially designed
in each case under consideration of the special circumstances in
the target application in order to ensure the right calculation of
the required measurement values or decisions.
For instance, the use of several low cost sensors in a multisensor system can reach a significant improvement of reliability
and precision in the gas concentration measurement [9]. Important circumstances for the data fusion are, in this case, the cross
sensitivity of the sensors and effects of influence factors such
as temperature, humidity, or pressure. Separate sensors should
generally measure the relevant influence factors. Through calibration processes, the reaction of the multisensor system on different lead gases is tested. Depending on the sensor reaction, the
combination of sensors for data fusion is determined. An accurate concentration measurement can, thereby, be carried out in
spite of the deficiencies of the individual sensors [9].
Multisensor systems are today indispensable in hazard
warning applications such as free-range protection by video
signal evaluation, detection of lying persons, or in the early fire
detection, because of the required high level of reliability. For
instance, in the early fire detection, sensor arrays, including
optical scattered light detectors and gas sensors, have been
proposed [10]. In this case, the signal processing should be
able to discriminate between fire, not-fire, and disturbing event
situations by identifying fire signatures from measured sensor
responses [10] (Fig. 6). A feature extraction unit is required in
order to reduce the dimensionality of the measurement space
and to extract suitable information characterizing fire situations.
The extracted features are then classified by means of a neural
network in order to estimate the class to which the measured
data belong and to know if an alarm should be sent to the fire
B. Signal Processing for Multisensor Systems
In general, single sensor systems can only provide partial information on the state of the environment, while multisensor
systems combine related data from multiple similar and/or different sensors. The goal of using multisensor systems is to pro-
The development trends in sensor technology result from
market-economical aspects, general customer requests, and
specific requirements of the target applications.
Fig. 6.
Structure of a sophisticated fire detection algorithm [10].
Costs reductions and more improvement in accuracy and
speed will be achieved in the future using measurement methods
with higher performance, new manufacturing technologies, and
sophisticated signal processing methods. The greater demand
for environmental protection demands the development of
highly reliable sensors. Maintenance-free sensors with long life
expectancy and low electric power consumption will, thereby,
be the focus of interests.
The main development trends in sensor technology are, in
general, toward miniaturization and an increasing use of multisensor and wireless systems (Fig. 7).
A. Trend in Miniaturization: Microsystem Technology
Miniaturization is an outstanding strategy of success in
modern technologies. A reduction of characteristic dimensions
usually results in shorter response times so that a correspondingly higher speed is achievable in signal generation and
processing. In many cases, it reduces costs because of the
higher integration rate, lower power consumption, and higher
reliability. Miniaturization is generally gaining importance in
all fields of applications, where smaller structures and greater
precision are becoming decisive to the market acceptance of
individual products. The development trend to miniaturization
goes on within nanotechnologies, which will open up access to
still smaller dimensions [11].
For instance, for the monitoring of vital parameters of human
beings, health care devices can be used so that an emergency call
could be released automatically in case of unconsciousness of
the observed person. For acceptance by users, the device should
be light and provide unhindered mobility. The user should be
able to ignore it and to live normally without being obliged to
take it off in any situation during the whole day.
The concept of the MIT-ring (Fig. 8), as a highly miniaturized solution, fulfills the requirements for this special application. A light-emitting diode in the ring continuously emits light
into the finger of the observed person. By an evaluation of the
reflected light, the ring can measure the pulse rate, the potential cardiac condition, and possibly blood pressure. By means
of an embedded antenna, signals can be transmitted to a signal
receiver nearby.
B. Trend in the Use of Multisensors
The use of multisensor systems is becoming more important in widespread applications [8]–[10]. Their applications
reach from the monitoring and automation of manufacturing
processes to robotics, automotive applications, smart home,
Fig. 7. Future trends in sensor technology.
Fig. 8. MIT-ring for healthcare [12].
process control, environmental engineering, biotechnology, and
life sciences.
Multisensor systems provide the advantage that economical
sensors can be used even for the achievement of a high level
of precision and reliability. Thereby, a big amount of available
information is managed using sophisticated signal processing
techniques so that the system achieves a better performance.
Multisensor data fusion is in effect intrinsically performed by
animals and human beings to achieve a more accurate assessment of the surrounding environment [12]. A directly related
example is the electronic nose, which consists of an array of
different sensors that have been shown to respond to definite
organic and inorganic compounds with low concentrations. In
order to reach a high resolution at low concentrations, the response of a sensor array is used like in the real human nose.
The applications of the electronic nose are widespread in the
chemical analysis, environment monitoring, food and wine inspection, emission control, and narcotic detection.
The development trends of multisensor systems are in the
development of modular systems [9], which are easily extendible with new units without disturbing the already available
C. Trend in Wireless Systems
With the large amount of components, which are indispensable for the achievement of the required functionality, the electric wiring of spatially distributed systems becomes complex
and causes difficulties in the system’s handling.
The use of wireless systems implies a better convenience and
leads to a considerable cost reduction. Wireless sensor systems
have the advantage that they can be placed anywhere, and can,
therefore, record the measured quantity closely to its occurrence, independent of potential harsh circumstances.
Wireless sensors can communicate over ultrasonic or infrared
signals [12], [13]. For instance, surface acoustic wave devices
(SAW transponders) [13] can be used for object identification
and for the measurement of physical, chemical, and biological
quantities such as temperature, pressure, torque, acceleration, or
Energy-autonomous sensors will gain a particular importance
among wireless sensors [13] because, in this case, wires are no
longer necessary, even for electricity supply. This kind of sensor
is necessary for many applications in which long distances are
to be bridged, or a large number of distributed components are
Sensor technology profits from synergetic concurrence
of both manufacturing technologies and signal processing
methods. New sensors provide promising technical solutions,
which can significantly contribute to an improvement of quality,
reliability, and economic efficiency of technical products.
For the development of new sensors, an interdisciplinary
work of key competence from university and industry is indispensable. In the future, sensor systems would be designed in an
integrated design process, including not only the technological
aspects, but also the design of the specific manufacturing steps
and signal processing algorithms.
[6] D. R. Sparks, S.-C. Chang, and D. S. Eddy, “Applications of MEMS
technology in automotive sensors and actuators,” in Proc. Int. Symp. Micromechatronics Human Sci., Nov. 25–28, 1998.
[7] K.-M. H. Lenssen, D. J. Adelerhof, H. J. Gassen, A. E. T. Kuiper, G.
H. J. Somers, and J. B. A. van Zon, “Robust giant magnetoresistance
sensors,” in Proc. Eurosensors XIII, The Hague, The Netherlands, Sept.
12–15, 1999, pp. 589–596.
[8] H. Ruser, A. v. Jena, V. Mágori, and H.-R Tränkler, “A low-cost ultrasonic-microwave multisensor for robust sensing of velocity and range,”
presented at the Proc. Sensor., Nürnberg, Germany, 1999.
[9] T. Doll, I. Eisele, and H.-R. Tränkler, Intelligentes Gas-Multisensorsystem. Rosenheim: Geronimo-Verlag, 1998.
[10] F. Derbel, “Performance improvement of fire detection systems by
means of gas sensors and LVQ neural networks,” presented at the Proc.
ACIDCA., Monastir, Tunisia, 2000.
[11] S. D. Senturia, “Simulation and design of microsystems: A 10 year perspective,” Sens. Actuators, vol. A 67, pp. 1–7, 1998.
[12] E. A. Thomson, H. H. Asada, and B.-H. Yang, MIT Ring Monitors Patients’ Vital Signs. Cambridge, MA: MIT News, 1997.
[13] W.-E. Bulst, G. Fischerauer, and L. Reindl, “State of the art in wireless
sensing with surface acoustic waves,” IEEE Trans. Ind. Electron., vol.
48, pp. 265–271, Apr. 2001.
Olfa Kanoun was born in Sfax, Tunisia, in 1970.
She received the M.Sc. degree from the Technical
University in Munich, Germany, in 1995 and the
Ph.D. degree on calibration-free temperature measurement using p-n junctions from the Institute
for Measurement and Automation, University of
Bundeswehr Munich, Neubiberg, Germany, in 2001,
where she is currently pursuing the habilitation
degree in the field of smart sensors.
Dr. Kanoun was awarded for best dissertation in
2001 by the AHMT (The Commission of Professors
in Measurement Technology in Germany).
[1] H.-R. Tränkler and O. Kanoun, “Symbiosis of information and sensor
technologies,” in Proc. Sensors, Nürnberg, Germany, May 13–15, 2003.
[2] G. Schneider, “Status monitoring and selfcalibration of sensors,” Automatisierungstechnische Praxis, vol. 38, no. 9, pp. 9–17, 1996.
[3] O. Kanoun, “Modeling the P-N junction I –U characteristic for an accurate calibration-free temperature measurement,” IEEE Trans. Instrum.
Meas., vol. 49, pp. 901–905, Aug. 2000.
[4] M. Esashi, “Microsystems by bulk micromachining,” in Proc. 30th European Microwave Conf., vol. 1, 2000, pp. 248–251.
[5] H.-P. Trah and R. Neul, “Physik und Design micromechanischer Automobilsensoren,” VDI-Berichte, Ludwigsburg, Germany, Rep. 1530,
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Hans-Rolf Tränkler was born in Munich, Germany,
in 1941.
Since 1980, he has been a University Professor
at the Institute for Measurement and Automation,
The University of Bundeswehr Munich, Neubiberg,
Germany. He is the head of this institute where he
has been leading different research projects in several fields in instrumentation and measurement. His
recent research projects have dealt with investigation
and modeling of sensors for physical and chemical
quantities, smart sensor systems, and sensor actuator
systems for smart home applications.