Piezoresistive Stress Sensors Research Articles

Piezoresistive and capacitive cement-based stress sensors designed by incorporating carbon nanotubes doped with graphene nanopowder for structural health monitoring

Piezoresistive and capacitive cement-based smart composites are promising candidates for stress monitoring applications in buildings. However, the drawbacks of designing cement-based smart composites with a piezoresistive mechanism sensitive to temperature variations or a capacitive mechanism sensitive to vibration can be solved with sensor designs based on two principles. To address this issue, cement-based smart composites with piezoresistive and capacitive stress sensitivity and significantly improved compressive strength were designed using carbon nanotubes (CNT) doped with graphene nanopowder. A maximum fractional change in electrical resistivity of 90.08% with a high linearity of 98% was obtained with a blend of 0.01 wt%, and the maximum fractional change in capacitance was 163.11% with a high linearity of 97% at 0.05 wt%. Cement-based smart composites have absolute piezoresistive stress sensitivities that vary from 1.38% MPa−1 to 4.16% MPa−1 and 2.54% MPa−1 to 2.57% MPa−1 for blended and impregnated low doses of 0.01 wt% to 0.05 wt% CNT doped with graphene. In addition, capacitive stress sensitivities of 22.74% MPa−1 and 5.1% MPa−1 were achieved by blending and impregnating 0.01 wt% CNT doped with graphene in cementitious composites. Furthermore, the conductive path, overlapping, and tunneling effect created by CNT doped with graphene in cementitious composites and the porous structure in the dielectric layer are responsible for the increased stress sensitivity.

Exploring potential of MXenes in smart sensing and energy harvesting

In present work, MXene based piezo-resistive stress sensor device is fabricated using a freeze-dry method. The device was tested to measure the mechanical stress, sensitivity, and triboelectric response by converting them into electrical signals as readout. With high electrical conductivity, mechanical flexibility, and piezo-resistive characteristics, MXene-based device offers novel sensing and triboelectric-related applications. This study showed an extensive characterization after fabricating the sensor using a simple and scalable process to ensure their performance in terms of sensitivity, stability, and repeatability. Using pure MXene layer as the core material, this paper pioneers an interesting investigation focusing on stress monitoring by piezoresistive stress sensor and triboelectric nanogenerator.

Graphene nanoplatelet/polypropylene pellets in cement-based composites: Mechanical, piezoresistive and microstructural properties

Graphene nanoplatelet–reinforced polypropylene is considered a promising conductive polymer-based additive to provide unique features to cementitious composites. This study comprehensively investigated the collective effects of graphene and polypropylene in the form of graphene nanoplatelet/polypropylene pellets (GrPp). Adding GrPp increased the flexural strength of the cementitious composites due to its high tensile strength. Moreover, the GrPp-enhanced cementitious composites show a high piezoresistive absolute maximum fractional change in electrical resistivity (FCR) of 96.50 %, stress sensitivity of 4.13 %/MPa, and repeatability of 90.81 % with the addition of 2–3.5 wt% GrPp on average. Furthermore, adding GrPp improved the piezoresistive behavior by forming a conductive path and increasing the deformability of the cementitious composites. These findings demonstrate that Gr-reinforced Pp pellets present an economical and efficient way to develop piezoresistive stress sensors for smart cement-based composites.

Influence of styrene-acrylic emulsion additions on the electrical and self-sensing properties of CNT cementitious composites

Carbon nanotube (CNT) cementitious composite sensors are considered as an effective method for monitoring the health of building structures owing to their good self-sensing properties. However, previous studies have shown that the high electrical conductivity imparted by CNTs to cement-based materials can increase the risk of steel corrosion. To overcome this drawback and further enhance the piezoresistive performance of the material, a new method has been proposed in this study to improve the self-sensing properties of CNT-cement composites by incorporating styrene-acrylic emulsion (SAE) in them. The results show that the electrical resistivity of CNT-cement composites increases significantly with the addition of SAE, which is attributed to the polymer film hindering the mutual contact of the CNTs and ion migration. In addition, the piezoresistive properties of the composite sensor are significantly improved owing to the CNT/SAE three- dimensional network structure of the conductive system established in the cement matrix. The CNT-cement composite sensor containing 10 wt% SAE exhibits the best self-sensing properties and can reach a stress sensitivity of 22.1 × 10–3 MPa−1. The utilization of polymers as reinforcement for CNT-cementitious materials presents an economical and efficient approach for fabricating piezoresistive stress sensors intended for SHM applications.

Convex Microarrays-Based Liquid Metal Soft Piezoresistive Stress Sensor With High Sensitivity and Large Measurement Range

Liquid metal (LM) soft piezoresistive sensors-that is, devices that change resistance in response to mechanical forces have low hysteresis and high stability, which exhibit great promise for human health monitoring. Applying stress reduces the cross-sectional area of the LM conductive path in the sensor, increasing the resistance and enabling the measurement of stress. However, increasing the sensitivity of the sensor results in a reduced measurement range and an increase in the cost. To solve this problem, this paper presents a convex microarrays (CMs)-based LM soft piezoresistive stress sensor with high sensitivity and large measurement range. The CMs are located inside the microchannel, which improves the sensitivity of the sensor without reducing the measurement range. The CMs also enable the sensor to monitor bending. The mechanism of the CMs is verified by theoretical analysis, finite element simulation and experiments. The relationship between the height of the CMs and the sensitivity is investigated. The sensor can be used in the field of human health monitoring.

Analysis of the stress state in QFN package during four point bending and temperature experiments utilizing piezoresistive stress sensor

This article reports a comprehensive study on four point bending and temperature experiments and their correlation with numerical simulation. The test vehicle is a printed circuit board (PCB) with 16 Quad Flat No Leads Packages (QFNs) components mounted on top and bottom side. Each QFN has 120 stress sensing cells distributed over the surface of the silicon die. In our experiments we have investigated the stress state in the QFN utilizing these piezoresisitve stress sensors for thermal, four point bending and combined loading conditions. In our study we compare these measurements with the numerical model. Stress analysis for the isothermal bending case showed that components very close to the edge are exposed to slightly higher stresses (20 %) compared to the components placed in the center of the PCB.

Silicon MEMS Nanomechanical Membrane Flexure Sensor With Integrated High Gauge Factor ITO

Introduction of structural modification and novel transduction materials to nanomechanical cantilever (NMC) sensor can bring considerable improvement in sensor performance leading to ultra-sensitive nanomechanical sensor platforms. This work reports development of an optimized and highly sensitive silicon MEMS Nanomechanical Membrane-Flexure (NMF) piezoresistive surface stress sensor. This MEMS structure consists of a circular adsorbate membrane suspended by four inverse trapezoidal flexures. The electromechanical transduction of the sensor is performed by integrating a high gauge factor low temperature sputter deposited Indium Tin Oxide (ITO) thin film as piezoresistor. The ITO thin film was experimentally characterized for extracting its electrical, mechanical, and electromechanical properties to assess its candidature in integrating as strain sensing element with nanomechanical sensors. The gauge factor of ITO thin film was measured using a high precision four-point bending fixture experimental setup. An ultra-thin ITO film deposited at room temperature with no oxygen flow and post annealing treatments exhibited a high negative gauge factor of −430, making it an excellent candidate in nanomechanical sensor platform. The incorporation of sputter deposited ITO thin film as piezoresistive layer obviated the need for doping in silicon which further reduced the fabrication process complexity for NMF sensor. The NMF sensor fabrication following SOI based silicon MEMS process along with device characterization have also been carried out as part of this work. The optimized design of piezoresistive NMF sensor exhibited an improved surface stress sensitivity of nearly 15 times higher than conventional cantilever sensor and thereby opening new possibilities in bio-chemical and environmental sensing applications. [2021-0127]

Neural networks for enhanced stress prognostics for encapsulated electronic packages - A comparison

The prediction of high-resolution mechanical stress distributions in electronic chips with a view to improving prognostic and health management in electronics and N/MEMS via artificial intelligence-based processing of measurement data is the focus of this study. Temperature, shear, and differential stress time-series data acquired through piezo-resistive silicon-based stress sensors on multiple electronic packages inside a thermal shock chamber were monitored, recorded, and subsequently analyzed by various neural network models to create a better understanding of the failure behavior over time including failure mechanisms, the delamination process in particular and the related stress distribution. Monitoring stress changes via continuous observation of material stiffness and interface integrity as factors influencing the local boundary conditions on chip cells, conveys pivotal information concerning the degradation progression. Deep neural networks empowered by backpropagation were trained to predict the stress distributions and ultimately monitor the degradation based on time-series data and were subsequently assessed for their performance to reliably predict in-plane stress developments and distributions on chips. In this study, long-short-term-memory- and gated-recurrent-unit-based networks could accurately predict the behavior of a single chip with smallest error.

Towards virtual twin for electronic packages in automotive applications

The piezoresistive silicon based stress sensor has the potential to be part of the Digital Twin implementation in automotive electronics. One solution to enforce reliability in digital twins is the use of Machine Learning (ML). One or more physical parameters are being monitored, while other parameters are projected with surrogate models, just like virtual sensors. Piezo-resistive stress sensors are employed to measure the internal stresses of electronic packages, an Acquisition Unit (AU) to read out sensor data and a Raspberry Pi to perform evaluation. Accelerated tests in air thermal chamber are performed to get time series data of the stress sensor signals, with which we can know better about how delamination develops inside the package. In this study stress measurements are performed in several electronic packages during the delamination. The delamination is detected by the stress sensor due to the continuous change of the stiffness and the local boundary conditions causing the stresses to change. Although, the stress change in multiple cells can give enough information if it is delaminated or not, its delamination area location is unknown. Surrogate models built upon Neural Networks (NN) and Finite Element Method (FEM) are developed to predict the out of plane stresses at the delaminated layer. FEM simulation models are calibrated with Moiré measurements and validated at the component and PCB level with stress difference measurements. Simulation delamination areas are constructed based on the Scanning Acoustic Microscope (SAM) images, and are also validated with the equivalent stress measurements. In the end the surrogate model is predicting the out of plane stress in the adhesive layer. The results show good correlation when compared to the SAM images.

Degradation Estimation and Prediction of Electronic Packages Using Data-Driven Approach

Recent trends in automotive electronics such as automated driving will increase the number and complexity of electronics used in safety-relevant applications. Applications in logistics or ridesharing will require a specific year of service rather than the conventional mileage usage. Reliable operations of the electronic systems must be assured at all times, regardless of the usage condition. A more dynamic and on-demand way of assuring the system availability will have to be developed. This article proposes a thermomechanical stress-based prognostics method as a potential solution. The goal is achieved by several novel advancements. On the experimental front, a key microelectronics package is developed to directly apply the prognostics and health management concept using a piezoresistive silicon-based stress sensor. Additional hardware for safe and secure data transmission and data processing is also developed, which is critically required for recording <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> and real-time data. On the data management front, proper data-driven approaches have to be identified to handle the unique dataset from the stress sensor employed in this study. The approaches effectively handle the massive amount of data that reveals the important information and automation of the prognostic process and thus to be able to detect, classify, locate, and predict the failure. The statistical techniques for diagnostics and the machine learning algorithms for health assessment and prognostics are also determined to implement the approaches in a simple, fast, but accurate way within the capacity of limited computing power. The proposed prognostics approach is implemented with actual microelectronics packages subjected to harsh accelerated testing conditions. The results corroborate the validity of the proposed prognostics approach.

Hybrid Smart Temperature Compensation System for Piezoresistive 3D Stress Sensors

This paper proposes a new hybrid temperature compensation system for doped silicon-based piezoresistive 3D stress sensors. The developed compensation system integrates a temperature sensor, placed in close proximity to the stress sensing rosettes, with the artificial neural networks (ANNs). In this work, the n-type circular piezoresistor featured over (111) silicon plane was employed to capture the local temperature variations, within the sensing chip. The extracted temperature changes, along with the resistance changes, are fed, as inputs, to the ANNs to compensate the temperature effect on the acquired signals for more accurate stress measurement. The proposed compensation system was experimentally evaluated while extracting stress applied up to 60 MPa at different temperatures within a range from 0 °C to 50 °C. The developed system was successfully able reduce the maximum full scale error, obtained from using only a temperature sensor for compensation, by ~55%. The new system has merit since it has the capability to compensate for both resistance and sensitivity, for 3D stress sensor, with no need for additional circuitry. Moreover the employed temperature sensor shares the same thermal environment with the stress sensing rosette.

Temperature drift-aware material selection of composite piezoresistive micro-cantilevers using Ashby’s methodology

In this paper, we devise an approach for material selection of constituent layers of a composite piezoresistive cantilever sensor using Ashby’s methodology. A rational choice of the constituent materials of a piezoresistive cantilever sensor becomes critical not only due to its multi-layered hetero structure but also due to the interdependence of material properties and geometrical parameters in governing its performance and reliability. Material selection is performed by identifying the performance metrics that govern the sensor performance and depict the interplay between the material constants and geometrical parameters. Here, apart from considering the coupled effects of electro-mechanical parameters we have also incorporated the impact of joule heating induced self-heating effects in the material selection process. Further, the material selection process is validated by computing the performance metrics of different sensors with same resonant frequency using analytical models. Results show that a silicon cantilever with a doped p-type piezoresistor, a silicon dioxide isolation layer and a gold immobilization layer is relatively better considering performance characteristics and reliability as compared to an SU-8 polymeric and other solid-state semiconductor based piezoresistive cantilever surface stress sensors, especially when joule heating induced inaccuracy is taken into account.

Development of a Self-Monitored 3D Stress Sensor for Adhesive Degradation Detection in Multilayer Assemblies

In this paper, a novel technique is proposed to provide a 3D piezoresistive MEMS stress sensor with a real-time self-monitoring of its bonding status. Perceiving the degradation of this interlayer due to different environmental parameters is crucial for reliable electronics and sensors assembly. The utilized sensor is featuring strain technology to fully extract the six stress components. Whenever a multilayer assembly, such as a sensor on a structure, is subjected to thermal or mechanical load, out-of-plane shear stress will accumulate at its edge as a reaction for peeling. As any degradation in the adhesion layer causes a significant reduction for out-of-plane shear stress, the capability of the 3D chip to measure this stress is employed to detect the degradation of the adhesive layer. To verify the capability of the current concept to experimentally quantify this loss, thermal energy is exploited for softening the bonding film. Losses up to 28 % for the out-of-plane shear stress are detected at a temperature equal to 80°c, which causes 71.9 percent decrease in the modulus of elasticity of the utilized adhesive. This significant correlation, between the out-of-plane shear stress loss and the bonding layer stiffness, is used to obtain a full picture of the adhesive deterioration in an early phase. The same technique can be utilized as a low profile detector for the debonding in multilayer electronics.

In-situ service load monitoring of automotive electronic systems using silicon-based piezoresistive stress sensor

It is expected that more and more complex (integrated) automotive electronics will be adopted in systems. These advanced electronics are critical for passenger safety. Documenting the in-situ loading is, thus, very important for accurate reliability prognostics. In this study, we propose a piezoresistive stress sensor to detect the in-situ loading of an automotive electronic control unit (ECU). A load metric directly related to the in-situ loading is defined by considering the in-situ stress values of twelve measurement cells in each sensor. The sensor and the proposed load metric are implemented to document the in-situ loading that results from the local coefficient of thermal expansion mismatch between the electrical components and printed circuit board (PCB) in advanced ECUs. The validity of the proposed metric is corroborated by failure analysis of solder joints of multilayer ceramic capacitors (MLCCs).

Development of Doped Silicon Multi-Element Stress Sensor Rosette With Temperature Compensation

In this paper, a developed n-type piezoresistive three dimensional (3D) stress sensor with full temperature compensation is presented. The proposed sensing rosette benefits from the stress insensitivity of the full-circular n-type piezoresistor, oriented over (111) silicon plane, to detect only temperature changes for compensation. Moreover, the unique behavior of shear piezoresistive coefficient (π44) of n-Si is utilized to construct a piezorestive stress sensing rosette over (111) silicon plane that is capable of extracting 3D stress components. A prototype stress sensing chip was microfabricatedto test the capabilityof the developedsensing rosette to accurately extract stress applied on structures at different thermal environments. The fabricated sensing chip was subjected to different mechanical loads using a loading rig with four-point bending fixture. The testing was carried out over a temperature range of 0-50 °C. The results showed that the proposed sensing chip has the capability of capturing the stress applied at different temperatures. Also, the developed sensing rosette showed less sensitivity to the uncertainty in piezoresistive coefficients' values compared to the other developed 3D piezoresistive stress sensors.

Accuracy of CMOS-Based Piezoresistive Stress Sensor for Engineering Applications of Thermal Loading Condition: Theoretical Review and Experimental Validation

Measurement uncertainties of a CMOS-based piezoresistive stress sensor are studied for low cycle thermal loading applications. After the fundamentals of the sensor are reviewed briefly, the random uncertainties associated with the data acquisition unit are evaluated first using raw current signals obtained from uniquely fabricated free-standing stress sensor chips. The free-standing sensor chips are tested further for systematic uncertainties associated with the manufacturing-induced residual stresses by subjecting them to a thermal cycle. Finally, the stress measurement accuracy of the sensor chip under an in-situ thermal loading is quantified by a numerical model verified by a sub-micron sensitivity optical technique while incorporating the quantified uncertainties.

Superelastic, fatigue resistant and heat insulated carbon nanofiber aerogels for piezoresistive stress sensors

Ultralight flexible carbon aerogels have been applied in many fields but are always hampered due to their weak mechanical stability, large energy dissipation, and complex preparation methods. Here, a novel polyimide-derived carbon nanofiber aerogel with high fatigue resistance and excellent flexibility is prepared by using the designed “fiber gluing” to construct the elastic continuous fibrous network. The as-prepared carbon nanofiber aerogels exhibit ultralow density of 6.6 mg cm−3, excellent fire-resistant performance when exposing to alcohol flames (650 °C), and robust elastic resilience even under 55% compressive strain. In particular, the carbon nanofiber aerogels show ultralow plastic deformation (only 1.3%) and low energy dissipation (<0.22) after 1000 cyclic loading-unloading tests at 55% compressive strain. Benefiting from their excellent flexibility and robust mechanical stability, the as-prepared carbon nanofiber aerogel are a promising candidate in the application of wearable devices and piezoresistive stress sensors.

Influence of surface layer properties on the thermo-electro-mechanical characteristics of a MEMS/NEMS piezoresistive cantilever surface stress sensor

In the past decade, micro/nano cantilever platforms have been actively explored for realizing variety of MEMS/NEMS sensor and actuator systems. In treatise, a majority of the cantilever platform based devices reported are non-monolithic structures with additional functional layers for improving their performance metrics. In this paper, for the first time, we elucidate the impact of an additional thin film Au layer which is typically used in the case of piezoresistive micro/nano cantilever surface stress sensors as a bio-interface for improving their chemical/biological selectivity and sensitivity. In this study, unlike the earlier reports in addition to the electro-mechanical response, we have also considered the phenomenon of thermal drift inaccuracy which has been seldom considered in the modeling and design stages of piezoresistive cantilever devices. The major component of inaccuracy i.e. multi-morph deflection induced due to the combined effect of multi-layered structure of a piezoresistive micro/nano cantilever sensor with a thin Au film and joule heating in the dc-biased piezoresistor is modeled and investigated. Here, the prime focuses are the following: (i) to investigate the effect of Au immobilization layer thickness and coverage on the thermo-electro-mechanical response and (ii) for a fixed sensor geometry, to identify an optimal Au immobilization layer thickness and coverage to nullify the multi-morph induced thermal drift. Premise has been investigated using a multi-physics modeling and simulation software in two phases. To validate the modeling approach, simulation results have been compared with experimental data reported in the literature. In phase-I, influence of the addition of Au immobilization layer on the sensor response is investigated. Results show that an addition of a 50 nm Au immobilization layer atop silicon monolithic structural layer with an integrated piezoresistor and a silicon dioxide isolation layer reduces resonant frequency by 15.23%, causes a change in multi-morph deflection by 2.83 times with a new behavior of reversal in cantilever bending direction and change in thermal sensitivity by 41.47%. In phase-II, impact of Au layer thickness and coverage on the sensor is analyzed. Results depict that apart from Au layer thickness, Au layer coverage area is also critical in governing the sensor response and by careful selection of the two parameters thermal drift component due to multi-morph deflection can be completely nullified.

Superelastic Hard Carbon Nanofiber Aerogels.

Superelastic carbon aerogels have been widely explored by graphitic carbons and soft carbons. These soft aerogels usually have delicate microstructures with good fatigue resistance but ultralow strength. Hard carbon aerogels show great advantages in mechanical strength and structural stability due to the sp3 -C-induced turbostratic "house-of-cards" structure. However, it is still a challenge to fabricate superelastic hard carbon-based aerogels. Through rational nanofibrous structural design, the traditional rigid phenolic resin can be converted into superelastic hard carbon aerogels. The hard carbon nanofibers and abundant welded junctions endow the hard carbon aerogels with robust and stable mechanical performance, including superelasticity, high strength, extremely fast recovery speed (860 mm s-1 ), low energy-loss coefficient (<0.16), long cycle lifespan, and heat/cold-endurance. These emerging hard carbon nanofiber aerogels hold a great promise in the application of piezoresistive stress sensors with high stability and wide detection range (50 kPa), as well as stretchable or bendable conductors.

Hybrid Approach to Conduct Failure Prognostics of Automotive Electronic Control Unit Using Stress Sensor as &lt;italic&gt;In Situ&lt;/italic&gt; Load Counter

A model/sensor hybrid approach is implemented to conduct failure prognostics of an automotive electronic control unit (ECU). A 3-D finite-element model simulating a complex ECU is built, and its predictability is calibrated and verified by an optical displacement measurement technique called moire interferometry. On the sensor front, a silicon-based piezoresistive stress sensor is embedded into the ECU to provide in situ stress measurements during operations. A stress metric is defined using the stress values of 12 cells in each sensor, and it is converted to in situ loading histories using the calibrated finite-element model. The modeling and verification steps that lead to the predictive finite-element model are described. The proposed hybrid approach is implemented using the data obtained from a molded ECU subjected to thermal cycling conditions, and the forces at an interface between an aluminum wire bond and metal pad are investigated.