Search Results (50)

Detecting volatile organic compounds (VOCs) is essential for health, environmental protection, and industrial safety. VOCs contribute to air pollution, pose health risks, and can indicate leaks or contamination in industries. Applications include air quality monitoring, disease diagnosis, and food safety. This paper focuses on polymer-based hybrid sensor arrays (HSAs) utilizing interdigitated electrode (IDE) geometries for VOC detection. Achieving high selectivity and sensitivity in gas sensing remains a challenge, particularly in complex environments. To address this, we propose HSAs as an innovative solution to enhance sensor performance. IDE-based sensors are designed and fabricated using the Polysilicon Multi-User MEMS process (PolyMUMPs). Experimental evaluations are performed by exposing sensors to VOCs under controlled conditions. Traditional multi-sensor arrays (MSAs) achieve 82% prediction accuracy, while virtual sensor arrays (VSAs) leveraging frequency dependence improve performance: PMMA-VSA and PVP-VSA predict compounds with 100% and 98% accuracy, respectively. The proposed HSA, integrating these VSAs, consistently achieves 100% accuracy in compound identification and concentration estimation, surpassing MSA and VSA performance. These findings demonstrate that proposed polymer-based HSAs and VSAs, particularly with advanced IDE geometries, significantly enhance selectivity and sensitivity, advancing e-Nose technology for more accurate and reliable VOC detection across diverse applications. Full article

During the thermal analysis of hazardous materials, the thermal instruments available may face the risk of contamination within heating chambers or damage to the instruments themselves. Herein, this work introduces an innovative detection technology that combines thermogravimetric and differential thermal analysis with an integrated MEMS cantilever. Integrating polysilicon thermocouples and a heat-driven resistor into a single resonant cantilever achieves remarkable precision with a mass resolution of 5.5 picograms and a temperature resolution of 0.0082 °C. Validated through the thermal analysis of nylon 6, the cantilever excels in detecting nanogram-level samples, making it ideal for analyzing hazardous materials like ammonium perchlorate and TNT. Notably, it has successfully observed the evaporation of TNT in an air atmosphere. The integrated MEMS cantilever detection chip offers a groundbreaking micro-quantification solution for hazardous material analysis, significantly enhancing safety and opening new avenues for application. Full article

On the way toward MEMS miniaturization, the quantification of geometric uncertainties stands as a primary challenge. In this paper, an approach that combines genetic algorithms and proper orthogonal decomposition with kriging surrogate modeling was proposed to accurately predict over-etch measures through an on-chip test device. Despite being fabricated on a single wafer under nominally identical manufacturing conditions, MEMS can display different responses under the same actuation, due to a different characteristic geometry. It is shown that the uncertainties, given in terms of over-etch values, were not only different from die to die but also within the same die, depending on the local geometric features of the device. Therefore, the proposed method provided an alternative solution to estimate the uncertainties in MEMS devices, relying only on the capacitance–voltage response. A statistical analysis was carried out based on a batch of devices tested in the laboratory. These tests and the estimation procedure allowed us to quantify the mean values of the over-etch relative to the target as +12.2 % at comb fingers, +10.0 % at the supporting springs, and −4.8 % at stoppers, showing noteworthy variability induced by the environment. Full article

The development of low-temperature piezoresistive materials provides compatibility with standard silicon-based MEMS fabrication processes. Additionally, it enables the use of such material in flexible substrates, thereby expanding the potential for various device applications. This work demonstrates, for the first time, the fabrication of a 200 nm polycrystalline silicon thin film through a metal-induced crystallization process mediated by an AlSiCu alloy at temperatures as low as 450 °C on top of silicon and polyimide (PI) substrates. The resulting polycrystalline film structure exhibits crystallites with a size of approximately 58 nm, forming polysilicon (poly-Si) grains with diameters between 1–3 µm for Si substrates and 3–7 µm for flexible PI substrates. The mechanical and electrical properties of the poly-Si were experimentally conducted using microfabricated test structures containing piezoresistors formed by poly-Si with different dimensions. The poly-Si material reveals a longitudinal gauge factor (GF) of 12.31 and a transversal GF of −4.90, evaluated using a four-point bending setup. Additionally, the material has a linear temperature coefficient of resistance (TCR) of −2471 ppm/°C. These results illustrate the potential of using this low-temperature film for pressure, force, or temperature sensors. The developed film also demonstrated sensitivity to light, indicating that the developed material can also be explored in photo-sensitive applications. Full article

In the case of repeated loadings, the reliability of inertial microelectromechanical systems (MEMS) can be linked to failure processes occurring within the movable structure or at the anchors. In this work, possible debonding mechanisms taking place at the interface between the polycrystalline silicon film constituting the movable part of the device and the silicon dioxide at the anchor points are considered. In dealing with cyclic loadings possibly inducing fatigue failure, a strategy is proposed to optimize the geometry of an on-chip testing device designed to characterize the strength of the aforementioned interface. Dynamic analyses are carried out to assess the deformation mode of the device and maximize the stress field leading to interface debonding. To cope with the computational costs of numerical simulations within the structural optimization framework, a reduced-order modeling procedure for nonlinear systems is discussed, based on the direct parametrization of invariant manifolds (DPIM). The results are reported in terms of maximum stress intensification for varying geometry of the testing device and actuation frequency to demonstrate the accuracy and computational efficiency of the proposed methodology. Full article

Helium is integral in several industries, including nuclear waste management and semiconductors. Thus, developing a sensing method for detecting helium is essential to ensure the proper operation of such facilities. Several approaches can be used for helium detection, including based on the high thermal conductivity of helium, which is several times higher than air. This work utilizes the high thermal conductivity of helium to design and analyze a bimorph MEMS sensor for helium sensing applications. COMSOL Multiphysics software (version 6.2) is used to carry out this investigation. The sensor is constructed from poly-silicon and SiO₂ materials with a trenched cantilever beam configuration. The sensor is electrically heated, and its morphed displacement depends on the surrounding gas’s composition, which decreases in the presence of helium. Several factors were investigated to probe their effect on the sensor’s sensitivity to helium, including the thickness of the poly-silicon layer, the configuration of the trench, and the thickness and location of SiO₂ layer. The simulations showed that the best performance, up to 2 ppm helium detection level, can be achieved with thinner beams and medium trench lengths. Full article

Microelectromechanical systems (MEMSs) are small-scale devices that combine mechanical and electrical components made through microfabrication techniques. These devices have revolutionized numerous technological applications, owing to their miniaturization and versatile functionalities. However, the reliability of MEMS devices remains a critical concern, especially when operating in harsh conditions like high temperatures and humidities. The unknown behavior of their structural parts under cyclic loading conditions, possibly affected by microfabrication defects, poses challenges to ensuring their long-term performance. This research focuses on addressing the reliability problem by investigating fatigue-induced delamination in polysilicon-based MEMS structures, specifically at the interface between SiO₂ and polysilicon. Dedicated test structures with piezoelectric actuation and sensing for closed-loop operation were designed, aiming to maximize stress in regions susceptible to delamination. By carefully designing these structures, a localized stress concentration is induced to facilitate the said delamination and help understand the underlying failure mechanism. The optimization was performed by taking advantage of finite element analyses, allowing a comprehensive analysis of the mechanical responses of the movable parts of the polysilicon MEMS under cyclic loading. Full article

Although one of the poster children of high-performance MEMS (Micro Electro Mechanical Systems) gyroscopes, the MEMS hemispherical resonator gyroscope (HRG) is faced with the barrier of technical and process limits, which makes it unable to form a resonator with the best structure. How to obtain the best resonator under specific technical and process limits is a significant topic for us. In this paper, the optimization of a MEMS polysilicon hemispherical resonator, designed by patterns based on PSO-BP and NSGA-II, was introduced. Firstly, the geometric parameters that significantly contribute to the performance of the resonator were determined via a thermoelastic model and process characteristics. Variety regulation between its performance parameters and geometric characteristics was discovered preliminarily using finite element simulation under a specified range. Then, the mapping between performance parameters and structure parameters was determined and stored in the BP neural network, which was optimized via PSO. Finally, the structure parameters in a specific numerical range corresponding to the best performance were obtained via the selection, heredity, and variation of NSGAII. Additionally, it was demonstrated using commercial finite element soft analysis that the output of the NSGAII, which corresponded to the Q factor of 42,454 and frequency difference of 8539, was a better structure for the resonator (generated by polysilicon under this process within a selected range) than the original. Instead of experimental processing, this study provides an effective and economical alternative for the design and optimization of high-performance HRGs under specific technical and process limits. Full article

In this study, a subminiature implantable capacitive pressure sensor is proposed for biomedical applications. The proposed pressure sensor comprises an array of elastic silicon nitride (SiN) diaphragms formed by the application of a polysilicon (p-Si) sacrificial layer. In addition, using the p-Si layer, a resistive temperature sensor is also integrated into one device without additional fabrication steps or extra cost, thus enabling the device to measure pressure and temperature simultaneously. The sensor with a size of 0.5 × 1.2 mm was fabricated using microelectromechanical systems (MEMS) technology and was packaged in needle-shaped metal housing that is both insertable and biocompatible. The packaged pressure sensor immersed in a physiological saline solution exhibited excellent performance without leakage. The sensor achieved a sensitivity of approximately 1.73 pF/bar and a hysteresis of about 1.7%, respectively. Furthermore, it was confirmed that the pressure sensor operated normally for 48 h without experiencing insulation breakdown or degradation of the capacitance. The integrated resistive temperature sensor also worked properly. The response of the temperature sensor varied linearly with temperature variation. It had an acceptable temperature coefficient of resistance (TCR) of approximately 0.25%/°C. Full article

This paper demonstrates a fully integrated vacuum microelectronic NOR logic gate fabricated using microfabricated polysilicon panels oriented perpendicular to the device substrate with integrated carbon nanotube (CNT) field emission cathodes. The vacuum microelectronic NOR logic gate consists of two parallel vacuum tetrodes fabricated using the polysilicon Multi-User MEMS Processes (polyMUMPs). Each tetrode of the vacuum microelectronic NOR gate demonstrated transistor-like performance but with a low transconductance of 7.6 × 10⁻⁹ S as current saturation was not achieved due to a coupling effect between the anode voltage and cathode current. With both tetrodes working in parallel, the NOR logic capabilities were demonstrated. However, the device exhibited asymmetric performance due to differences in the CNT emitter performance in each tetrode. Because vacuum microelectronic devices are attractive for use in high radiation environments, to test the radiation survivability of this device platform, we demonstrated the function of a simplified diode device structure during exposure to gamma radiation at a rate of 45.6 rad(Si)/second. These devices represent a proof-of-concept for a platform that can be used to build intricate vacuum microelectronic logic devices for use in high-radiation environments. Full article

Polycrystalline silicon is a brittle material, and its strength results are stochastically linked to microscale (or even nanoscale) defects, possibly dependent on the grain size and morphology. In this paper, we focus on the out-of-plane tensile strength of columnar polysilicon. The investigation has been carried out through a combination of a newly proposed setup for on-chip testing and finite element analyses to properly interpret the collected data. The experiments have aimed to provide a static loading to a stopper, exploiting electrostatic actuation to move a massive shuttle against it, up to failure. The failure mechanism observed in the tested devices has been captured by the numerical simulations. The data have been then interpreted by the Weibull theory for three different stopper sizes, leading to an estimation of the reference out-of-plane strength of polysilicon on the order of 2.8–3.0 GPa, in line with other results available in the literature. Full article

Data-driven formulations are currently developed to deal with the complexity of the multi-physics governing the response of microelectromechanical systems (MEMS) to external stimuli and can be extremely helpful. Such devices are in fact characterized by a hierarchy of length and timescales, which are difficult to fully account for in a purely model-based approach. In this work, we specifically refer to a (single-axis) Lorentz force micro-magnetometer designed for navigation purposes. Due to an alternating current flowing in a slender mechanical part (beam) and featuring an ad hoc set frequency, the microsystem is driven into resonance so that its sensitivity to the magnetic field is improved. A reduced-order physical model was formerly developed for the aforementioned movable part of the device; this model was then used to feed and speed up a multi-physics and multi-objective topology optimization procedure, aiming to design a robust and performing magnetometer. The stochastic effects, which are responsible for the scattering in the experimental data at the microscale, were not accounted for in such a model-based approach. A recently proposed formulation is here discussed and further extended to allow for such stochastic effects. The proposed multi-scale deep learning approach features: at the material scale, a convolutional neural network adopted to learn the scattering in the mechanical properties of polysilicon, induced by its morphology; and, at the device scale, two feedforward neural networks, one adopted to upscale the mechanical properties, while the other learns a microstructure-informed mapping between the geometric imperfections induced by the microfabrication process and the effective response of the movable part of the magnetometer. The data-driven models are linked through the physical model to provide a kind of hybrid solution to the problem. Results relevant to different neural network architectures are here discussed, along with a proposal to frame the approach as a multi-fidelity, uncertainty quantification procedure. Full article

This work describes the fabrication and characterization of a Micro-Electro-Mechanical System (MEMS) sensor for gas sensing applications. The sensor is based on standard PolyMUMPs (Polysilicon Multi-Users MEMS Process) technology to control the temperature over the sensing layer. Due to its compact size and low power consumption, micro-structures enable a well-designed gas-sensing-layer interaction, resulting in higher sensitivity compared to the ordinary materials. The aim of conducting the characterization is to compare the measured and calculated resistance values of the micro-heater and the temperature sensor. The temperature coefficient of resistance (TCR) of the temperature sensor has been estimated by raising and dropping the temperature throughout a 25–110 °C range. The sensitivity of these sensors is dependent on the TCR value. The temperature sensor resistance was observed to rise alongside the rising environmental temperatures or increasing voltages given to the micro-heater, with a correlation value of 0.99. When compared to the TCR reported in the literature for the gold material 0.0034 °C${}^{-1}$, the average TCR was determined to be 0.00325 °C${}^{-1}$ and 0.0035 °C${}^{-1}$, respectively, indicating inaccuracies of 4.6% and 2.9%, respectively. The variation between observed and reported values is assumed to be caused by the fabrication tolerances of the design dimensions or material characteristics. Full article

Audio applications such as mobile phones, hearing aids, true wireless stereo earphones, and Internet of Things devices demand small size, high performance, and reduced cost. Microelectromechanical system (MEMS) capacitive microphones fulfill these requirements with improved reliability and specifications related to sensitivity, signal-to-noise ratio (SNR), distortion, and dynamic range when compared to their electret condenser microphone counterparts. We present the design and modeling of a semiconstrained polysilicon diaphragm with flexible springs that are simply supported under bias voltage with a center and eight peripheral protrusions extending from the backplate. The flexible springs attached to the diaphragm reduce the residual film stress effect more effectively compared to constrained diaphragms. The center and peripheral protrusions from the backplate further increase the effective area, linearity, and sensitivity of the diaphragm when the diaphragm engages with these protrusions under an applied bias voltage. Finite element modeling approaches have been implemented to estimate deflection, compliance, and resonance. We report an 85% increase in the effective area of the diaphragm in this configuration with respect to a constrained diaphragm and a 48% increase with respect to a simply supported diaphragm without the center protrusion. Under the applied bias, the effective area further increases by an additional 15% as compared to the unbiased diaphragm effective area. A lumped element model has been also developed to predict the mechanical and electrical behavior of the microphone. With an applied bias, the microphone has a sensitivity of −38 dB (ref. 1 V/Pa at 1 kHz) and an SNR of 67 dBA measured in a 3.25 mm × 1.9 mm × 0.9 mm package including an analog ASIC. Full article

Microelectromechanical systems (MEMS) are often affected in their operational environment by different physical phenomena, each one possibly occurring at different length and time scales. Data-driven formulations can then be helpful to deal with such complexity in their modeling. By referring to a single-axis Lorentz force micro-magnetometer, characterized by a current flowing inside slender mechanical parts so that the system can be driven into resonance, it has been shown that the sensitivity to the magnetic field may become largely enhanced through proper (topology) optimization strategies. In our previous work, a reduced-order physical model for the movable structure was developed; such a model-based approach did not account for all the stochastic effects leading to the measured scattering in the experimental data. A new formulation is here proposed, resting on a two-scale deep learning model designed as follows: at the material level, a deep neural network is used a priori to learn the scattering in the mechanical properties of polysilicon induced by its morphology; at the device level, a further deep neural network is used to account for the effects on the response induced by etch defects, learning on-the-fly relevant geometric features of the movable parts. Some preliminary results are here reported, and the capabilities of the learning models at the two length scales are discussed. Full article