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The recent advances of sensor technologies have been powered by high-speed and low-cost electronic circuits, novel signal processing methods and innovative advances in manufacturing technologies. The synergetic interaction of new developments in these fields allow completely novel approaches increasing the performance of technical products. Innovative sensor structures have been designed permitting self-monitoring or self-calibration. The rapid progress of sensor manufacturing technologies allows the production of systems and components with a low cost-to-performance ratio. Among Microsystems manufacturing technologies, surface and bulk micromachining are increasingly winning recognition. The potential in the field of digital signal processing involves new approaches for the improvement of sensor properties. Multi-sensor systems can significantly contribute to the enhancement of the quality and availability of information. For this purpose, sophisticated signal processing methods based on data fusion techniques are more effective for an accurate computation of measurement values or a decision than usually used threshold based algorithms. In this state-of-the-art lecture we will give an overview of the recent advances and future development trends in the field of sensor technology. We will focus on novel sensor structures, manufacturing technologies and signal processing methods in individual systems. The predominantly observed future development trends are: The miniaturization of sensors and components, the widespread use of multi-sensor systems and the increasing relevance of radio wireless and autonomous sensors.
AUTHORS: GOURISHANKAR.N PREMNATH.K
YEAR: IV/V IV/V
REGN.NO: 02931A2208 02931A2226
BRANCH: I.C.E. I.C.E.
The 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 sensor applications. The number of sensor systems required and the diversity of their application is permanently increasing.
Nowadays, sensor technology intrudes in new application fields and mass markets like automobiles or smart homes. To keep up with the requirements in these application fields and with additional rapid developments in science and technology, the sensor design is required to provide novel approaches and solutions. An exhaustive consideration of the newest developments in the wide spectrum of sensor technology is very important to promote synergy effects in this field. For new applications, even well-known solutions should be in general adapted to specific requirements.
DEVELOPMENTS OF SENSOR 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 this 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. With selected examples, we will give in this state-of-the-art paper an overview about the significant developments of methods, structures, manufacturing technologies and signal processing characterizing todayâ„¢s sensors and sensor systems.
In the design process the technical and economical consideration of the target application are generally necessary. The specific requirements and boundary conditions are useful for the concrete specification of the sensor system on which the design of the sensor system bases (fig. 2). At beginning, several libraries of well-known system structures, sensor elements, simulation models and signal processing methods are available. These generally result from earlier approaches and solutions. In a pre-design step, rough structures of the sensor system are specified and evaluated. As a result we obtain the sensor concept, which is gradually refined. The final component are generally obtained after an experimental examinations within the total system.
Simulation techniques are usually used in order to considerably shorten the sensor time-to-market and to improve the sensor properties during the design process. The realized efficiency of the design cycle thereby can lead to a successful system design on the first fabrication attempt, so that the total resulting costs are significantly reduced. Making use of simulation techniques, statistical characteristics describing the behavior of the sensor system can be easily calculated.
In the kernel of a sensor system is the sensor element, which changes its output depending on the measured quantity (fig. 3). In a pre-processing unit, the sensor signal is transformed in an adequate signal using analog signal processing tech-niques. By means of low-cost analog-digital converters, signal processing is increasingly shifted from the higher system level in the sensor level. The diverse facilities in the field of digital signal processing involve new approaches for the improvement of sensor properties. For instance, the consideration of manufacturing variance caused by fluctuations during the technological production process becomes a simple task, which can be carried out during the system configuration. In order to be integrable in higher-level systems, an adequate interface, such as a bus interface is generally conceived as an essential component of the sensor system itself.
In recent research, other functions, such as online selftest or self-calibration, are being embedded in the sensor structure during the design process. That way designed sensor systems have many advantages especially considering the system reliability and the cost reduction of 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 (fig. 4). Generally, specific relationships are required about the sensor behavior and the expected confidence limits of sensor properties. 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. For instance, by acceleration sensors with a closed-loop structure, the inertial force acting on the mass is compensated through an electrically generated restoring force (fig. 5). Through the application of restoring forces with well-known values, self-tests can be carried out.
A further potential of built-in self-tests is possible in multi-sensor systems, making use of more information provided by the individual sensors. For instance, through a comparison of the outputs redundantly available sensors, strongly deviating sensors can be detected and eliminated from the following signal processing. 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, sensor aging effects can be considered, so that defined measurement accuracy limits could be guaranteed during the operating time.
The trend towards built-in self-test or self-calibration function leads up to the design of totally calibration-free sensor systems. In recent research dealing with temperature measurement based on p-n junctions , a novel sensor principle have been developed, in which temperature can be calculated without needing any calibrations during production or maintenance processes.
The a priori-knowledge about the sensor behavior is represented in this case by the model of the i-u characteristic, in which temperature is one unknown parameter between others (fig. 6). The measured voltages at different supply currents are fitted to the i-u characteristic model, so that temperature is simultaneously calculated online together with all unknown parameters in the characteristic model. This calculation process is repeated at every temperature measurement process, so that no unknown parameters are needed to be determined before sensor operating. This sensor principle works without calibration by definite reference temperature. This fact helps to significantly reduce sensor production and maintenance costs.
Because of the fluctuation of several factors during the production process, sensor devices show generally a certain manufacturing variance. Influence factors, such as temperature, pressure and humidity can have an effect on the sensor behavior. Aging processes are in some cases considerable and affect sensor properties, such as the sensitivity or the zero point. The signal processing has the task to determine the measured quantity from the measured data in spite of all these effects, which are in some cases unavoidable and represent a systematical source of measurements errors (fig. 11). Making use of appropriate signal processing techniques, the properties of the sensor systems and especially the precision of measurement can be significantly improved.
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 a suitable values, in order to reach superior units undisturbed and to simplify the calculation of the measurement value. Therefore the sensor signal should be generally pre-processed. Thereby many important tasks could be realized (fig. 12), such as:
Special measures for the secure operation
Conjunctions with other components in a chain, parallel or closed-loop structure
Nowadays, a current practice is the local digitalization of the sensor signal. Through the increasing use of distributed systems with bus based networking, the signal digitalization is becoming more and more necessary. In addition to the disburden of the total system, the local signal digitalization have the advantage, that the measurement data could be transmitted without precision loss independently of the distance between the sensor and the higher processing unit.
By shifting of signal processing from hardware to software, the measurement precision can be simpler improved. The manufacturing variance can be considered by a simple parameterization in stead of mechanical or electrical trimming processes. Using physical or mathematical models describing the sensor behavior, a more precise measurement could be carried out, taking into account some influence effects. In this context, especially mathematical models making use of the principle of the basis function method have shown a special aptitude for the correction of influence effects. For instance, giant magneto resistance (GMR) elements are able to measure an angle with a high resolution . A particular property of these elements, is e. g. that they can measure the direction of a magnetic field independently of its amplitude .In this 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 magnetization. The possibility to use sophisticated signal processing methods leads to completely new sensors using principles, which are in fact already well known , but technological problems, like manufacturing variance, or the low level of the signal prevented their effective use for measurements. For instance inductive planar sensors can be used for a position detection. The measurement principle is that an approaching metallic sheet generates an eddy current, which depends on the distance to the coil. This signal only can be used for a distance measurement, if an adapted analog and digital signal processing is implemented.
FUTURE TRENDS IN SENSOR TECHNOLOGY
The development trends in sensor technology result from market-economical aspects, general customer requests and the requirements for an implementation in the target applications. Costs reductions, improvements of accuracy, reliability and speed are reached using measurement methods with a higher performance, new manufacturing technologies and sophisticated signal processing methods.
The main development trends in sensor technology go to-wards miniaturization and an increasing use of multi-sensor and wireless systems (fig. 16).
A. Trend to Miniaturization: Microsystem Technology
Miniaturization is an outstanding strategy of success in modern technologies. A reduction of characteristic dimensions usually results in shorter response times and higher resonance frequencies so that a correspondingly higher speed is achievable in signal generation and processing. It reduces production costs and increases performance by integration of microcomponents.
Miniaturization is gaining importance in all fields of application where smaller structures and greater precision are becoming decisive to the market acceptance of individual products. Even whole sensor systems are concerned by this trend, particularly in application fields where the acceptable volume or weight is limited. The development trend to miniaturization go on within the Nanotechnologies.
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 unconscious-ness of the observed person. For acceptance by users, the device should be light and provide a unhindered mobility. The user should be able to ignore it and to live normally without being obliged to take it down in any situation during the whole day.
The concept of the MIT-ring (figure 17), as a highly miniaturized solution, that can fulfill 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 may be the blood pressure. By means of an embedded antenna signals can be transmitted to a signal receiver in the surroundings.
B. Trend to the Use of Multi-Sensors
The use of multi-sensor systems is becoming more important in widespread application fields. Their applications reach from the monitoring and automation of manufacturing processes to robotics, automotive applications, smart home, 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 set of available information is managed using sophisticated signal processing techniques, so that the system can achieve 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, thereby improving their chances of survival . A directly related application is the electronic nose, which consists of an array of different sensors that have been shown to respond to a number of organic and inorganic compounds with low concentrations (fig. 18). The response of the sensor array is used in the identification of the constituents of a gaseous environment. 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 multi-sensor systems goes to the development of modular systems. The advantage thereby is the easy extendibility of the system with new units and the possibility of updating old system units without disturbing the functions of the total system. Energy autonomous sensors will gain a particular importance among wireless sensors, because in this case no wires are necessary anymore, even for electricity supply.
The sensor technology profits from synergetic concurrence of both of 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 in-dispensable. In the future sensor systems would be designed in an integrated design processes including not only the technological aspects, but also the design of the specific manufacturing steps.