Sensor Pattern Research Articles

Design Rules for 3D Printing‐Assisted Pressure Sensor Manufacturing: Achieving Broad Pressure Range Linearity

AbstractRecent advancements in 3D printing technology have expanded its application to manufacturing pressure sensors. By harnessing the cost‐effectiveness, streamlined processes, and design flexibility of 3D printing, sensor fabrication can be customized to meet specific performance needs. Thus far, 3D printing in pressure sensor development has been primarily limited to creating molds for transferring patterns onto flexible substrates, restricting both material selection and sensor performance. To fully unlock the potential of 3D printing in advanced pressure sensor fabrication, it is crucial to establish effective design rules focused on enhancing the figure of merit performance. This study introduces a universal design strategy aimed at maintaining high sensitivity across a wide pressure range—a challenging feat, as sensitivity significantly decreases at higher pressures. Our approach centers on engineering the deformability of 3D‐printed structures, achieving a linear increase in contact area between sensor patterns and electrodes without reaching saturation. Sensors designed with high elongation and low stiffness exhibit consistent sensitivity of 162.5 kPa⁻¹ across a broad pressure range (0.05–300 kPa). Mechanistic investigations through finite element analysis confirm that engineered deformability is key to achieving this enhanced linear response, offering robust sensing capabilities for demanding applications such as deep‐sea and space exploration.

An RFID smart structure using inkjet-printed additive manufacturing technology for metal crack characterization

Abstract Developing an indicator for metal materials to characterize cracks is urgent. However, traditional sensor-based technology has drawbacks such as high costs for installation and maintenance when using wired connections. In this paper, we studied the Radio Frequency Identification (RFID) sensors created through 3D printing technology to characterize surface cracks in metals, this approach simplifies the manufacturing process, silver nanoparticles are printed layer by layer on substrate to form the sensor pattern. The functionality of the sensor is verified through simulations and experiments involving samples with various crack sizes. Our findings demonstrate that when cracks pass over the sensor, there is a distinct response in terms of a shift in resonant frequency, moreover, the sensor offers a reading range greater than 0.7 m at resonance frequency without requiring power supply or wired connection for data transmission purposes. This research showcases the design of a smart structure that is compact, easy-to-fabricate, and potential for applications related to structural health monitoring (SHM) and crack sensing.

Percolation network study of highly sensitive buckled elastomeric piezoresistive sensors

Abstract Flexible pressure sensors are needed for future artificial electronic skin applications. Carbon black (CB)-enhanced elastomers are known for their unique conductivity, allowing for special uses in sensor technology. This research analyzes the sensitivity of elastomeric sensors reinforced with CB, under a pre-strained buckle, using a modified percolation network model to enhance performance in sensing applications. The finite element method is employed to analyze the piezoresistive characteristics of the sensors across various thicknesses. The research involves analyzing the strain patterns of buckled piezoresistive sensors when an indenter applies a load, and how this influences the sensors’ resistivity. The mechanical parameter is directly correlated to the sensor sensitivity through the maximum principal strain. The model shows a good agreement with the experimental data. The pressure sensitivity resulting from indenter compressive contact is 0.03 and 0.0061 kPa−1 in the pressure range of 0–1 and 0–5 kPa for wavy and straight 1000 μm buckled sensors, respectively. The results show that the film with 50% taller waves has a 40%–60% narrower pressure sensing ranges. Moreover, results indicate that adding waves to the geometry of the sensor improves the piezoresistive behavior by increasing the relative displacements of edges. Results also reveal more stable performance from fewer waves and a higher working range by thicker sensors.

Research on dynamic urine volume detection system based on smart flexible textile sensors

Urine leakage volume is an important indicator reflecting the severity of incontinence in patients. Currently, there are limited smart diapers capable of continuous dynamic monitoring of urine volume. This study developed two types of urine volume sensors, resistive and capacitive, which were integrated with traditional diapers to assess urine leakage levels: mild leakage (0–5 mL), moderate leakage (6–12 mL), and severe leakage (above 12 mL). Three patterns of resistive urine volume sensors were designed, and the results showed that the A pattern could accurately determine urine volume and frequency levels. Additionally, three electrode spacing designs were tested for the capacitive urine volume sensors. The results indicated that the sensor with a 1 cm electrode spacing could determine the urine volume range, with each 1 mL increase in urine causing a capacitance rise of approximately 1.5–1.8 pF, with an error of about ± 0.5 mL per increment. Both resistive and capacitive methods showed high accuracy in monitoring urine volume and frequency. This study validated the feasibility of smart flexible fabric sensors in detecting urine volume and frequency, providing a potential solution for better assessing and managing the condition of incontinence patients.

Thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) with distance-probability joint decision SVM algorithm: A novel system for Daqu Grade identification

Electronic nose is a bionic technology that uses sensor arrays and pattern recognition algorithms to mimic the human olfactory system. This study developed a thermal desorption-photoionization ion mobility-electronic nose (TD-PIM-Nose) system, employing thermal desorption for direct sampling and humidity control, with a photoionization ion mobility tube as virtual sensor array for component separation and detection, and pattern recognition algorithms for signal processing to differentiate and identify samples. Furthermore, it was applied to assess four quality grades of Daqu samples (“Excellent+”, “Excellent”, “Grade I”, and “Grade II”) determined by the Check-All-That-Apply (CATA) method. Characteristic compound differences among these grades were identified using fingerprint spectra and reduced mobility values. A distance-probability joint decision support vector machine (SVM) algorithm model was established, validated against sensory CATA standards. Results showed identification accuracies: 90 %, 90 %, 96.88 %, and 100 % for respective grades. These findings demonstrated the promising potential of the TD-PIM-Nose system in Daqu quality grading.

Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry.

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.

Efficient Fabrication of Bioinspired Flexible Pressure Sensors via Electrohydrodynamic Jet Printing Method.

Bioinspired microdevices have made significant strides in various applications including human motion and health detection. However, facile and highly efficient fabrication approach of flexible pressure sensors remains a great challenge. Herein, inspired by the gecko's foot structure, a flexible pressure sensor with microdomes structure is fabricated by tip-assisted on-demand electrohydrodynamic jet (EHD-jet) printing method. Ascribed to the interlocking electrodes with microdome structure, 3Ddeformation rates are substantially enlarged. When the microdromes structure is under pressure, the resistivity of carbon nanotubes film coated on the surface of microdomes structure will change remarkably. By using the combined effect of assisted tip and ring focusing electrode, the influence and constraints on microstructure fabrication caused by substrate material and morphology are minimized. The desired uniform structures can be adjusted rapidly by changing the printing parameters and liquid properties. High length-height ratio (0.64) of microdomes enhances sensitivity, with minimum detection limit is 2Pa and response time is 40ms. Finally, the bionic flexible sensor indicated excellent performance in capable of detecting pressure, sound vibrations and human motion. This work presents a new method for high-efficiency fabrication micro-nano patterns for flexible sensors inspired, which could be used in wearable tech and health monitoring.

56‐4: A Design of High‐Performance Touch Sensor Pattern for OLED On‐cell Structure

We describe an OLED on‐cell structure touch panel, which is indispensable for flexible and foldable devices. Our unique sensor pattern design has realized high signal without affecting the reflection appearance. The signal is three times higher than that of conventional patterns, making it easy to tune and realize flexible and foldable devices with high operability.

74‐4: Research of FMLOC Visibility Phenomena Based on Huygens Point Spread Function

This paper analyzed the visibility phenomenon caused by the metal mesh of FMLOC under the point source illumination. We built a simulation model based on PSF of Huygens integral mode, processed the simulation results with background filtration algorithm. When there is only one graphic unit and no gap spacing between single‐cycle graphic units in FMLOC sensor pattern, the phenomena were significantly improved.

Flexible and freestanding temperature sensors based on laser carbonization of carbon nanofibers

A new single-step, low-cost method for producing a wearable, flexible temperature sensor has been developed using laser writing technology. The process involves carbonizing and patterning polymeric nanofibers to create a standalone laser-carbonized nanofibers (LCNFs) substrate. A large-area polyacrylonitrile nanofibers mat is first prepared using electrospinning and then carbonized with a high-power CO2 laser beam. The resulting LCNFs are highly conductive and retain their fibrous structure. The near-complete disappearance of polyacrylonitrile peaks in the FTIR measurements and the high-intensity characteristic peaks of carbon materials obtained in the Raman scattering measurements of the LCNFs confirm their complete carbonization. SEM microscopy showed that the fibrous structure of the LCNFs remained intact after the laser carbonization process with no significant damage. Additionally, TEM microscopy revealed that the LCNFs are made up of axially ordered graphene sheets, which form their unique fibrous structure. LCNFs' temperature sensor exhibits >87% higher temperature coefficient of resistance (TCR) than laser-induced graphene (LIG) temperature sensor on a polyamide substrate when tested under the same experimental conditions. The LCNFs sensor also boasts excellent resistance stability, mechanical stability, and flexibility. To fit various wearable applications and enhance the temperature-sensing capabilities, three different sensor patterns of LCNFs were created: square, serpentine, and heater. Among these patterns, the heater design exhibited superior TCR and temperature response. However, it also had a higher thermal mass compared to the other patterns.

Source Camera Identification Techniques: A Survey.

The successful investigation and prosecution of significant crimes, including child pornography, insurance fraud, movie piracy, traffic monitoring, and scientific fraud, hinge largely on the availability of solid evidence to establish the case beyond any reasonable doubt. When dealing with digital images/videos as evidence in such investigations, there is a critical need to conclusively prove the source camera/device of the questioned image. Extensive research has been conducted in the past decade to address this requirement, resulting in various methods categorized into brand, model, or individual image source camera identification techniques. This paper presents a survey of all those existing methods found in the literature. It thoroughly examines the efficacy of these existing techniques for identifying the source camera of images, utilizing both intrinsic hardware artifacts such as sensor pattern noise and lens optical distortion, and software artifacts like color filter array and auto white balancing. The investigation aims to discern the strengths and weaknesses of these techniques. The paper provides publicly available benchmark image datasets and assessment criteria used to measure the performance of those different methods, facilitating a comprehensive comparison of existing approaches. In conclusion, the paper outlines directions for future research in the field of source camera identification.

A Novel Deep Learning-Based Anomaly Detection Pipeline Optimized for Dark Blemish Pattern of Image Sensor

A Novel Deep Learning-Based Anomaly Detection Pipeline Optimized for Dark Blemish Pattern of Image Sensor

Magnetic tunnel junction platforms for linear positioning and nanoscale displacement sensing

An array of magnetic tunnel junctions (MTJs), connected in a Wheatstone bridge, is characterized as a positioning sensor to detect spatial displacement of a magnetized object. To perform the experiment, the sensor was mounted on a fixed frame, and the magnet was installed on top of nano-positioning piezo-stage, operating in a closed-loop feedback circuit. The sensor signal was recorded in time domain, to detect displacement of the magnet, defined by commands to the stage. The positioning transfer curves were obtained for different orientations of magnetization with respect to the direction of the magnet displacement and the field sensing direction of the MTJs in the arrays. For configurations with linear transfer curve between positioning displacements and the sensor signal, the effects of magnet size, sensor pattern dimensions and interspace distance between the sensor and magnet positioning trajectories, are characterized. In the optimized experimental configuration, the displacement steps down to 10 nm were detected.

Environment-Adaptable Edge-Computing Gas-Sensor Device With Analog-Assisted Continual Learning Scheme

This paper presents a multi-gas sensor device whose structure is optimized for edge computing capability under internet of things (IoT) environments. Considering inherent sensor device characteristics susceptible to environmental factors like temperature and humidity, edge-computing capability for the on-site sensor calibration and pattern recognition (PR) is facilitated through a proposed analog-assisted continual learning scheme. An environment-adaptable continual learning (EACL) is proposed to combine multiple learning processes under different environments including chamber and on-site. Its computation burden is much relieved to be integrated into the edge device by adopting the analog-assisted structure, where a designed readout integrated circuit (ROIC) for automatic calibration normalizes gas-sensor data. For functional feasibility, an edge-computing IoT device prototype is manufactured with a fabricated ROIC and an in-house semiconductor-type sensor array, supporting wireless on-site monitoring platform interfaces. The environment-adaptable edge-computing capability is functionally verified through EACL-PR experiments on hazardous gases such as NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and CO under environmental factor variations. The average PR accuracy of 97% is achieved on several kinds of mixture gas patterns. The analog-assisted operation is verified to reduce the training cycles by 3 times while the EACL itself achieves 25% better efficiency.

GPU Rasterization-Based 3D LiDAR Simulation for Deep Learning.

High-quality data are of utmost importance for any deep-learning application. However, acquiring such data and their annotation is challenging. This paper presents a GPU-accelerated simulator that enables the generation of high-quality, perfectly labelled data for any Time-of-Flight sensor, including LiDAR. Our approach optimally exploits the 3D graphics pipeline of the GPU, significantly decreasing data generation time while preserving compatibility with all real-time rendering engines. The presented algorithms are generic and allow users to perfectly mimic the unique sampling pattern of any such sensor. To validate our simulator, two neural networks are trained for denoising and semantic segmentation. To bridge the gap between reality and simulation, a novel loss function is introduced that requires only a small set of partially annotated real data. It enables the learning of classes for which no labels are provided in the real data, hence dramatically reducing annotation efforts. With this work, we hope to provide means for alleviating the data acquisition problem that is pertinent to deep-learning applications.

(Invited) CMOS-Based Multimodal Sensing

In the coming Society 5.0, information will be collected, aggregated, and utilized in a wide range of areas through the IoT, and as the concept of "Trillion Sensors" states, the importance of "sensors" as an interface between physical space and cyberspace is increasing. In the development of sensor devices to meet this demand attempts to realize the sensing of multiple items by integrating single-function sensors with high detection selectivity have been the main focus in the past. On the other hand, our research group has proposed the measurement concept of multimodal sensing, in which sensors with broad detection characteristics are used to realize the sensing of multiple items, and has developed semiconductor CMOS-based sensors to realize this concept. In this talk, we introduce ion image sensors and odor sensors as examples of CMOS-based sensors suitable for multimodal sensing, and data analysis using machine learning, which is important for this measurement concept will be mentioned and introduce an example of machine learning-based sensing. The application of these sensors to smart agriculture and other fields will also be discussed, as well as the current status and issues for the practical application of sensor systems.An ion-sensitive transistor (ISFET) can be fabricated by changing the MOSFET's gate to an ion-sensitive membrane. An ion image sensor can be constructed by arraying ion-sensitive elements that operate based on the same principle using CMOS image sensor technology. This sensor visualizes the solution's hydrogen ion concentration (pH) and enables real-time observation of ion dynamics in the solution. By changing the ion-sensitive membrane of the sensor, it is possible to image specific ions such as potassium and calcium, and by adding an enzyme-immobilized membrane, it is also possible to capture chemical substances such as lactic acid. By combining these methods, the development of a multimodal sensor that can simultaneously image multiple ions and chemical substances is in progress as an approach from the hardware side. The future approach from the software side is expected to include measurement data processing by machine learning, thereby expanding the range of substances to be measured. In addition, making this sensor extremely thin makes it possible to perform ion imaging by inserting it into the object to be measured. Functional demonstration of visualization of ion distribution in a plant stem has been performed successfully, and we are currently studying its application in the field of smart agriculture.Since the ion image sensor mentioned above captures minute changes in electric potential generated by ion-sensitive membranes, it can be operated as a gas sensor by forming a membrane on the sensor whose electric properties change depending on gas adsorption. Certain polymer membranes respond to a wide range of gases, they can be used as odor-sensitive membranes. When multiple gas- and odor-sensitive membranes with different response characteristics are formed on a CMOS sensor, gas- and odor-specific response patterns can be obtained. Machine learning is effective in analyzing these patterns, and this presentation will introduce an example of gas-type discrimination.

An electrochemical biosensor based on graphene intercalated functionalized black phosphorus/gold nanoparticles nanocomposites for the detection of bacterial enzyme

Sortase A (Srt A) is a key enzyme that catalyzes the covalent anchoring of surface proteins to the bacterial cell wall and plays a dominant role in the late stage of Gram-positive bacterial infections. Achieving activity detection of Srt A and inhibitors screening is important for the treatment of Gram-positive bacterial infections and novel antibacterial drugs research. Here, an electrochemical sensor pattern is proposed. Black phosphorus (BP) is a novel graphite-like two-dimensional material with excellent electrochemical properties. However, the extremely poor environmental stability limits its practical application. The surface modification of BP using graphene oxide (GO) significantly improves the stability, and obtained partially reduced graphite oxide functionalized BP composites combine both electrical conductivity and stability. In this paper, we develop an electrochemical biosensor based on partially reduced graphite oxide functionalized BP /gold nanoparticles (p-rGO-BP/AuNPs) nanocomposites for detecting Srt A activity. The assay utilizes two peptide chains, P1 (CLPETG) and P2 (GGGGG), which can be linked by the presence of Srt A. At the same time, the process is monitored by differential pulse voltammetry (DPV). The sensor showed good selectivity and stability with a linear dynamic range of 1 pM-100 nM and a detection limit (LOD) of 0.65 pM. In addition, the method was successfully used to detect Srt A in complex samples and estimate the half-inhibitory concentration (IC50) of Srt A inhibitors (curcumin, dihydromyricetin and rutin).

TelecomTM

We introduce the TelecomTM system that uses pre-existing telecommunication fiber-optic cables as virtual strain sensors to sense vehicle-induced ground vibrations for fine-grained and ubiquitous traffic monitoring and characterization. Here we call it a virtual sensor because it is a software-based representation of a physical sensor. Due to the extensively installed telecommunication fiber-optic cables at the roadside, our system using redundant dark fibers enables to monitor traffic at low cost with low maintenance. Many existing traffic monitoring approaches use cameras, piezoelectric sensors, and smartphones, but they are limited due to privacy concerns and/or deployment requirements. Previous studies attempted to use telecommunication cables for traffic monitoring, but they were only exploratory and limited to simple tasks at a coarse granularity, e.g., vehicle detection, due to their hardware constraints and real-world challenges. In particular, those challenges are 1) unknown and heterogeneous properties of virtual sensors and 2) large and complex noise conditions. To this end, our TelecomTM system first characterizes the geographic location and analyzes the signal pattern of each virtual sensor through driving tests. We then develop a spatial-domain Bayesian filtering and smoothing algorithm to detect, track, and characterize each vehicle. Our approach uses the spatial dependency of multiple virtual sensors and Newton's laws of motion to combine the distributed sensor data to reduce uncertainties in vehicle detection and tracking. In our real-world evaluation on a two-way traffic road with 1120 virtual sensors, TelecomTM achieved 90.18% vehicle detection accuracy, 27x and 5x error reduction for vehicle position and speed tracking compared to a baseline method, and ±3.92% and ±11.98% percent error for vehicle wheelbase and weight estimation, respectively.

31‐4: Sharp Force Touch for On‐Screen User Interface in LCD and Foldable OLED Display Application

We describe ultra‐high sensitive force sensor on flat display that can detect and differentiate between feather touch and press touch or tapping. The proposed unique sensor pattern design and pressure‐sensitive material enables ultra‐high force sensitivity, multi force detection (10 points). Our force touch technology with button‐shaped bumps cover enables blind input operations and usability as good as mechanical buttons. Therefore, our technology provides reduced input errors and yield more intuitive and safedrive. In addition, the proposed novel algorithm enables an operating temperature range for automotive applications.

Single-Step Transfer-Printing Microfabrication of Soft Bioelectronics Using PDMS with Tuned Adhesion Forces

Recent soft and stretchable bioelectronics for various wearable applications generally require special equipment and facilities for microelectromechanical systems (MEMS). As an alternative, simple photolithography-free microfabrication methods have been proposed based on transfer printing onto soft substrates; however, limitations remain in that precise control is involved for modulating adhesion forces. In this study, a simple, rapid, cost-effective transfer-printing-based microfabrication process is demonstrated without requiring any MEMS process or sophisticated transfer control. We utilized ethoxylated polyethylenimine (PEIE) to tune the adhesion properties of polydimethylsiloxane (PDMS) such that thin sensor patterns fabricated by laser-machined gold leaves are easily transferred from the low-adhesion donor PDMS onto the high-adhesion receiver PDMS layer. The microfabrication steps were optimized based on the electrical and mechanical analysis of the transferred patterns, thereby enabling stable fabrication of thin gold lines of 100 μm width. An additional advantage of the sticky PDMS (sPDMS) is presented for stronger bonding of the cover layer and gold layer around the opening windows, which may allow for greater long-term stability in aqueous conditions. The sPDMS also enables adhesive-free attachment to the skin without losing its adhesion forces over repeated applications. The feasibility of the simple microfabrication process is verified by successfully demonstrating a multifunctional wearable patch for electrical (electromyography) and mechanical (strain) monitoring. This process provides a complete set of efficient microfabrication procedures for soft bioelectronics, from metal deposition to patterning and selective encapsulation, which can be utilized in a wide range of multifunctional and wearable physiological monitoring.