The intrinsic stresses introduced during the MEMS multilayer deposition process and the thermal stresses introduced during operation can significantly impact the performance, reliability, and lifetime of the product. The mechanical properties of the MEMS multilayer system are analyzed. An accurate closed-form solution is obtained for the multilayer system, including both intrinsic stresses and thermal stresses, and validated by numerical FEM simulation. Thin films are deposited on silicon substrates, and wafer bending measurements are carried out. Based on this formalism, the residual stresses in various multilayer systems are extracted and analyzed using parameter estimation theory.
The size-of-source effect (SSE) is a major source of uncertainty in radiation thermometers and thermal imaging cameras. This effect is considered a systematic error and is typically evaluated by measuring changes in the detected signal as the size of the radiant source is varied using a set of circular apertures of different diameters. The accurate characterization of the SSE requires measurements over a wide range of object sizes with well-distributed sample points, which is both time-consuming and labor-intensive. This paper proposes a new approach in which the aperture of an iris diaphragm is continuously adjusted to vary the size of the radiant source. An experimental study was conducted to compare the results obtained with individual fixed apertures and to assess whether the camera frame rate and the iris-driving period influence the measured temperature. The proposed approach reproduced the SSE with the same accuracy as measurements made with individual apertures while providing a significantly larger number of measurement points and a notable reduction in the measurement time. Furthermore, the method showed no significant sensitivity to the driving speed of the iris or the camera's frame rate within the investigated range. However, temporal effects arising from the optical components were found to disturb the measurement.
Abstract. High-precision analysis of the stable isotopic composition of atmospheric methane (CH4) is essential for attributing its sources and sinks and for a more precise understanding of the global methane cycle. Although conventional isotope ratio mass spectrometry (IRMS) provides high accuracy, it lacks in situ capabilities and provides only low data rates. Tunable laser spectrometers offer higher acquisition rates and high sensitivity. In accordance with the Beer–Lambert law, the absorption signal increases proportionally with the optical path length, requiring kilometer-scale paths for atmospheric CH4 detection and thus the use of optical cavities. Here, we present an optical feedback cavity-enhanced absorption spectroscopy (OF-CEAS) system for determining the isotopic ratio (δ13C) of the stable isotopologues 12CH4 and 13CH4 in the mid-infrared range at 3000 cm−1 (3333 nm wavelength). The optical setup comprises a high-finesse V-shaped cavity with highly reflective mirrors (r=0.999893), resulting in a theoretically effective path length of 22.53 km, which is integrated into an Invar cell with active temperature and pressure stabilization. Allan deviation analysis of the temperature regulation shows a minimum of σ=8.85 µK at an integration time of τ=1.6×104s, and for the gas temperature inside the cell, it gives a minimum of σ=600 µK at τ=6 s, which shows thermal stability that is compatible with an uncertainty in the isotopic ratio of ≤0.1 ‰ (Bergamaschi et al., 1994). Spectral measurements confirm active cavity locking, a symmetric mode structure and frequency calibration using a germanium etalon and a methane reference cell. The spectral resolution of the measurement is given by the free spectral range (FSR) of the cavity with FSR≈84 MHz. The system presented here demonstrates the stability and resolution required for high-precision isotopic methane analysis.
High-precision analysis of the stable isotopic composition of atmospheric methane (CH4) is essential for attributing its sources and sinks and for a more precise understanding of the global methane cycle. Although conventional isotope ratio mass spectrometry (IRMS) provides high accuracy, it lacks in situ capabilities and provides only low data rates. Tunable laser spectrometers offer higher acquisition rates and high sensitivity. In accordance with the Beer–Lambert law, the absorption signal increases proportionally with the optical path length, requiring kilometer-scale paths for atmospheric CH4 detection and thus the use of optical cavities. Here, we present an optical feedback cavity-enhanced absorption spectroscopy (OF-CEAS) system for determining the isotopic ratio (δ13C) of the stable isotopologues 12CH4 and 13CH4 in the mid-infrared range at 3000 cm−1 (3333 nm wavelength). The optical setup comprises a high-finesse V-shaped cavity with highly reflective mirrors (r=0.999893), resulting in a theoretically effective path length of 22.53 km, which is integrated into an Invar cell with active temperature and pressure stabilization. Allan deviation analysis of the temperature regulation shows a minimum of σ=8.85 µK at an integration time of τ=1.6×104s, and for the gas temperature inside the cell, it gives a minimum of σ=600 µK at τ=6 s, which shows thermal stability that is compatible with an uncertainty in the isotopic ratio of ≤0.1 ‰ (Bergamaschi et al., 1994). Spectral measurements confirm active cavity locking, a symmetric mode structure and frequency calibration using a germanium etalon and a methane reference cell. The spectral resolution of the measurement is given by the free spectral range (FSR) of the cavity with FSR≈84 MHz. The system presented here demonstrates the stability and resolution required for high-precision isotopic methane analysis.
Emission measurement of on-road vehicles in traffic is an important step for air pollution control and, thus, the reduction of negative effects on public health. Remote emission sensing (RES) is a state-of-the-art technology to detect high emitters by monitoring thousands of vehicles in traffic continuously. State-of-the-art (SOTA) RES systems use optical techniques to measure the ratio of specific pollutants to CO2 in vehicle exhaust plumes in order to determine emission factors. Highly accurate SOTA systems use laser absorption spectroscopy for measurement of the pollutant ratio in vehicle exhaust plumes. To obtain the absolute concentration of the single pollutants in the exhaust plume, the absorption path length must be known. In this work we present a 3D gas schlieren imaging sensor (GSIS) system which allows the geometrical reconstruction of 3D density fields of vehicle exhaust plumes in RES applications. Thus, it allows us to obtain the vehicle exhaust plume size and thereby enables estimation of the absorption path length from any direction. Furthermore, it is possible to determine where the laser intersects with the exhaust plume and, thus, to assess if the measurement is valid. The 3D-GSIS system consists of an array of low-cost digital cameras operating in the range of visible light. By means of advanced image processing and tomographic reconstruction techniques, the 3D displacement and density fields of vehicle exhaust plumes can be reconstructed. For validation, we characterized the 3D-GSIS system in the lab using hot air and CO2 plumes. Moreover, the 3D density fields of on-road passing vehicles are estimated and reconstructed using the 3D-GSIS system. The 3D-GSIS system is to be combined with an advanced RES system to measure the direct concentration of pollutants in vehicle exhaust plumes in the future.
In this study, a non-contact, optical methodology based on laser-Doppler vibrometry (LDV) is presented to directly determine the piezoelectric constant d33 of LiTaO3 in a temperature range from room temperature to 400 degrees C as a proof of concept for high-temperature piezoelectric characterization. LiTaO3 is chosen as a model material as it is a representative piezoelectric material with applications in sensors and surface acoustic wave devices; however, reliable high-temperature data for d33 are only available to a limited extent, and even room temperature data, often derived from indirect or contact-based methods, show large variations.The presented approach employs freely vibrating LiTaO3 disks mounted in a minimal-contact holder, enabling off-resonant and resonant LDV measurements with temperature control in a furnace. The measured displacements, together with the applied excitation and the measured resonance behavior, yield d33 values in the off-resonant regime that range between approximately 12 and 15 pm V-1 at 21 up to 400 degrees C, with indications of a slight temperature dependence that remains within experimental uncertainty.The LDV technique demonstrated here provides (i) non-contact measurement virtually free of clamping effects, (ii) access to high-temperature operation limited only by the furnace, (iii) the ability to map surface distribution, and (iv) the detection of resonances, thereby enabling off-resonant frequency ranges to be determined and to compare with literature values obtained by indirect or contact methods. While this study focuses on LiTaO3, the method is applicable to systems that exhibit displacements down to a few picometers.
We present an eight-channel evaluation board that enables fast data acquisition for ultrasound sonography (USG) and photoacoustic imaging (PAI). High frame rates enable averaging, resulting in an improved signal-to-noise ratio (SNR). A high SNR is becoming increasingly important in photoacoustic imaging, as the trend in the development of new systems is shifting from powerful solid-state lasers (pulse repetition frequency approximate to 10-100 Hz, pulse energy approximate to 10-100 mJ) to integrated compact pulsed laser diodes (PLDs) offering higher pulse repetition frequencies (approximate to 1-10 kHz) at reduced pulse energies (approximate to 1 & micro;J-2 mJ). The board is designed for processing high data rates and a robust analog signal chain. It integrates a programmable preamplifier (preamp) with adjustable gain followed by an integrated circuit comprising low-noise amplification, low-pass filtering, and 12-bit analog-to-digital (ADC) conversion. An AMD Zynq system-on-chip (SoC) handles processing on the board. The ADC is readout with low-voltage differential signal (LVDS) and the captured data are transmitted via Ethernet. Evaluation and image processing is done on a PC. The scientific contribution is the quantitative characterization of gain distribution and its effect on SNR in a multichannel photoacoustic receive chain. A sustained acquisition rate of 540 frames per second is demonstrated, validating frame averaging as an effective method to compensate for reduced pulse energy of compact pulsed laser diodes.
The implementation of automation in the metrological traceability of measurements has been demonstrated to possess considerable potential for enhancing the effectiveness of quality management. This enhancement is achieved by decreasing the necessity for human interaction and reducing the risks associated with manual data processing. For this purpose, it is imperative that all metrological and administrative information in quality certificates be provided in a fully machine-readable and machine-interpretable form. The transition from paper-based calibration certificates to Digital Calibration Certificates (DCCs), which meet these requirements, enables a fully automatic process conformity monitoring system. A demonstrator that monitors the temperature of a process has been developed as an example to illustrate the potential for automation that accompanies the use of DCCs, including in security. The programming code is also published to give a starting point for the reader's own implementation.
The recent bridged EGFET (extended-gate field effect transistor) sensor design is the most user-friendly potentiometric transducer concept to date. Manufacturing of the sensor, the introduction of a sensitised phase transfer membrane by in situ casting, and transduction of the electric potential resulting from analyte-sensitiser binding are remarkably simple. However, so far, the immobilisation of the sensitiser has only been demonstrated within an agar hydrogel phase transfer membrane, the same material used for the so-called bridge that defines the concept. Here, we demonstrate in situ casting of a plasticised polyvinyl chloride (PVC) membrane onto the agar bridge as an alternative. We compare the performance of different types of sensitisers - an organic dye and an ion-exchanging clay - for the same target analyte, Cr(VI) oxyanions, when the sensitiser is immobilised in either an agar hydrogel or a plasticised PVC membrane. We find superior performance, as quantified by sensor response at the Cr(VI) maximum contaminant limit, when the membrane and processing solvent match the solubility of the sensitiser. We unite the simplicity of the bridged EGFET, the convenience of in situ membrane casting, and the performance advantage of organic-solvent-processed phase transfer membranes for organic sensitisers.
The first choice in science and industry to image surfaces down to the sub-nm range are scanning electron microscopy (SEM) and atomic force microscopy (AFM). Both techniques have specific disadvantages which can be compensated by the other method. Therefore, the implementation of AFM inside an SEM vacuum chamber provides the user with the best of both worlds. When operated under vacuum, AFM cantilevers have larger Q factors than in air and thus a lower scanning speed. In this work, an electrical circuit and a piezoelectrically driven micro-electromechanical system (MEMS) cantilever is developed to tune the Q factor of the cantilever using a feedback system, with the goal of replacing air damping. In doing so, it is demonstrated that AFM measurements in vacuum with scanning speeds as under ambient air pressure are feasible. The cantilever features an electrically driven integrated piezoelectric transducer, which is used to excite the oscillation while the piezoelectric current serves as a feedback signal for a closed-loop feedback approach. In vacuum, the Q factor is reduced by a factor of 4. Hence, the cantilever oscillation and step response show a damping behaviour equivalent to an operation in air.
An algorithm for adaptive accuracy enhancement of lateral position sensing based on quadrature spatio-temporal modulation is presented, and its application in a prototype micropower optical position sensor with simultaneously firing infrared emitters is reported. Substantial (4 & times;) improvement in measurement accuracy over a basic detection method has been observed using an automated test stand where partial incapacity on one of the emitter channels has been simulated.
This article investigates methods for sampling volatiles on a compounding extruder to enable the development of a quasi-continuous automated sampling and measurement system for odorous contaminants. A first prototype of a bespoke extraction system was presented in earlier work, comprising four sequential cooling traps and one subsequent sorption trap. This setup allows us to obtain samples from the vacuum degassing port of a Coperion ZSK extruder. Preliminary results indicated that samples contain a plethora of odor-active compounds. This study assesses samples from the processing of post-consumer recycled polypropylene by means of gas chromatography-mass spectrometry/olfactory detection (GC-MS/O), focusing on comparing the composition of condensed and adsorbed samples. The results give an overview of the degassing atmosphere, listing more than 108 volatile compounds including odorants. Qualitative comparison of the sampling techniques indicates significant fractioning between condensates and adsorbates, which is illustrated on an orientation plot for water solubility versus volatility. Based on the results, guidelines for the design of sampling units for automated use in the aspired online monitoring application are proposed to transfer a broad spectrum of relevant contaminants to an attached measurement system.
Wildlife-related traffic accidents represent a persistent hazard on rural roads in Germany and beyond. Current electronic wildlife warning systems typically monitor only very short distances and therefore cannot provide large-area coverage. This paper presents a novel multisensor approach that integrates radar and infrared (IR) technology into existing roadside delineators. Due to regulatory requirements, delineators are placed at intervals of 50 m on German country roads. Integrating sensors into these delineators thus provides a uniform infrastructure that can be utilised. The radial extension of the sensor range allows a monitoring zone to be formed along the road. We evaluate thermal infrared arrays and high-resolution 60 GHz radar sensors for range, resolution and robustness under varying environmental conditions. Field measurements in wildlife parks demonstrate that the system can reliably detect deer at distances of up to 30 m and evaluate their moving speed as well. Challenges such as ambient temperature effects, optical dispersion in IR detection and resolution limits are discussed. The results highlight the potential of multisensor systems to reduce wildlife accidents and improve road safety.
In times of digital transformation, machines are expected to operate exclusively using the International System of Units (SI), something that humans have not yet fully achieved. The same should include sensor systems feeding data to those machines. This requires an internationally authoritative database for converting the non-SI units that humans enter to SI units at the level of the human-machine interface or human-sensor interface.
The fabrication of microelectromechanical systems (MEMS) devices comprises many steps, each of which adds to the tolerance, resulting in device performances that may fall outside the defined limits in the design process. Hence, it is important to know local thin film properties most accurately, directly affecting the performance of the MEMS device. Furthermore, the capability of monitoring and mapping the thin film thickness and stress across a wafer enables device statistics and the strengthening of scientific statements. Within this study, we used standard MEMS structures consisting of a cantilever and a step profile to perform automated and contactless characterization of the local thin film thickness and stress across six 4-inch (100 mm) wafers. For this purpose, we constructed a measurement setup combining white light interferometry (WLI) to measure the static deflection of the cantilevered beams and plates and the thickness of the thin film through a step profile etched into the thin film. Even more, an XYZ-stage positions hundreds of devices below the objective lens of the WLI. This leads to precise maps of the local thin film thickness and to the extraction of a mean stress and a gradient stress from the static deflection of slender beams. The beams are oriented parallel and perpendicular to the wafer flat so that the measurement of orientation-dependent stress values is possible.
Power transformers are an integral part of our electric power system. Parasitic direct currents in the alternating-current grid cause inefficient transformer operation and demand mitigation measures to protect the grid’s constituents. In particular, geomagnetically induced currents (GICs) arising from solar activity prove to be an unpredictable risk for grid operators. Within this paper, we present an interferometric fiber-optic current sensor system designed for long-term monitoring of GICs, allowing fully remote sensor control and data access. The developed sensor possesses a noise-limited threshold sensitivity of 2.64 mA Hz-1. We successfully demonstrate the first optical measurement of GICs on a single phase of the power grid during two distinct geomagnetic events, on both the low-voltage and the high-voltage sides.
In an era of rapid technological advancement, object detection has become essential for enhancing efficiency and safety in various fields. Although significant progress has been made in air-based applications, underwater object detection remains relatively unexplored, especially in shallow aquatic environments such as coastal zones, harbors, and aquaculture facilities, due to its unique challenges. Ultrasonic technology, with its ability to travel long distances underwater and perform well in turbid and low-light conditions, stands out as a promising solution. Recent advances in micro-electromechanical system (MEMS) technology, particularly capacitive micromachined ultrasonic transducers (CMUTs), offer new opportunities for underwater detection. CMUTs are compact and highly sensitive and operate with a wide bandwidth, making them ideal for underwater applications. This research explores the use of CMUTs, fabricated by Fraunhofer ENAS with a resonant frequency of 1.5 MHz, for underwater object detection. Initial experiments confirmed their feasibility for detecting submerged objects of various sizes, shapes, and materials. Further investigations assessed the resolution of CMUTs in detecting minimum object sizes using an automated XYZ stage. Finally, the technology was used to map the topography of test objects, including intricate 3D structures and alphabet shapes, demonstrating its potential for high-resolution mapping. These results highlight the promising capabilities of CMUTs for underwater sensing applications, with substantial potential for further development.
A novel concept of an acoustic flowmeter, based on single-mode waveguides, is proposed, implemented, and analysed in this work. Instead of transmitting a pulse diagonally across the duct's cross-section, this device operates with two ducts that operate simultaneously as pipes and as waveguides. Below the frequency threshold for single-mode propagation, acoustic waves are forced to traverse the waveguides with a plane front, precluding the possibility of beam drifting, inner reflections, and spreading losses. This enables the designer to flexibly increase the sound path and perform a highly sensitive measurement of the flow velocity and speed of sound, even if the excitation frequency is required to be kept below a relatively low value. A device based on this principle was constructed and tested for flow measurements in air. It consists of two waveguides of a circular cross-section (5 mm diameter) coupled to electroacoustic transducers for the transmission of a wideband chirp (9.8–18.2 kHz). Usage of a wideband signal was possible due to the combined frequency response of a special kind of micromachined ultrasound transducer (MUT) and a commercial micro-electromechanical system (MEMS) microphone. The constructed flowmeter was capable of measuring flow velocities up until the transition to turbulent flow at 16 L min−1 with a resolution of 0.3 L min−1, and it also detected changes of less than 0.2 m s−1 in the speed of sound. This topology for flow measurement could prove advantageous for applications where gases of variable composition are conducted in ducts of diameters in the millimetre range.
This research presents a capacitive displacement sensor concept, designed for integration into a pin array gripper. The sensor employs a plate capacitor structure to measure the displacement of individual pins, with each pin positioned to move between the electrodes. The sensor is designed with sensing, guiding and shielding electrodes to maintain a homogeneous electric field between the capacitor plates and a linear capacitance response. We implemented a shielding strategy with the objective of minimising external interference and reducing mutual interference between individual displacement sensors. This ensures stable operation and reliable measurements, which are crucial for the reliable functioning in dynamic environments. The design is optimised for additive manufacturing, offering advantages in customisation, adaptability to various pin gripping systems and a compact form factor. It also opens up new possibilities for integrating sensing elements directly into the structure of the gripper. A prototype sensor was fabricated using additive manufacturing and tested in an experimental setup to validate its functionality and to enable a comparison of its performance against the results of numerical simulations.
Selecting an appropriate model for industrial condition monitoring is challenging due to various factors. Typically, industrial datasets are small and lack statistical independence because experimental coverage of all possible operational variations is costly and sometimes practically impossible. Consequently, the resulting domain shifts pose a significant challenge. Although deep learning (DL) methods have frequently been regarded as the primary and optimal choice in many applications, they often lack major success factors in condition monitoring tasks. In this study, we benchmark the robustness of typical DL architectures against classical feature extraction and selection followed by classification (FESC) methods under domain shifts commonly encountered in industrial condition monitoring. Both DL and FESC methods are employed within an automated machine learning framework. We benchmarked these methods on seven publicly available datasets, and to simulate domain shifts, we employed leave-one-group-out validation on those datasets. Our experiments demonstrate high accuracy across all tested models for random K-fold cross-validation. However, the overall performance significantly decreases when faced with domain shifts, such as transferring the trained model from one machine to another. In four out of seven datasets, FESC methods showed better results in the presence of domain shifts. Furthermore, we also show that FESC methods are easier to interpret than DL methods. Finally, our results suggest that deep neural networks are not universally preferred over classical, low-capacity models for such tasks, as typically only a limited number of features from the input signal are needed.
