Tactile Sensor Array Research Articles

A triboelectric nanogenerator-based tactile sensor array system for monitoring pressure distribution inside prosthetic limb

In the prosthetic field, quantifying interfacial pressure distribution between the amputee's residual limb and the socket’s internal environment is imperative for developing new technologies. However, the commercially available pressure sensors have several disadvantages, namely external power supply requirements, low stability, high cost, and complexities related to integrating the sensor system into prosthetic devices. Herein, we propose a self-powered triboelectric nanogenerator (TENG) based flexible tactile sensor array system to monitor prosthetic socket internal pressure distribution in real-time. The proposed sensor consists of biocompatible polydimethylsiloxane (PDMS) polymer and polycaprolactone (PCL) nanofiber membranes as triboelectric materials. The biodegradable PCL nanofiber membrane, fabricated via a scalable and cost-effective electrospinning process, possesses a high surface area-to-volume ratio, facilitating enhanced charge generation during triboelectrification. The as-fabricated TENG-based tactile sensor is highly stable up to 10,000 cycles. Furthermore, the voltage of the tactile sensor is independent of different temperature and humidity values, demonstrating the stability of the sensor under various environmental conditions. Finally, we successfully integrated the tactile sensor array within a prosthetic device to monitor real-time pressure distribution inside the prosthetic socket during gait simulation. We believe that the proposed novel design provides a new dimension for the rapid development of self-powered, low-cost, and highly stable pressure monitoring systems with considerable potential for commercialization.

Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot's end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.

Dual‐Function Tactile Sensor with Linear Pressure and Temperature Perception at Low Degree of Coupling

Multiinformation tactile perception is highly important for detecting the change of external environment and realizing the compliant control of the robot. However, the integration of dual‐mode or multimode sensors with low coupling remains a challenge. Herein, a dual‐function tactile sensor with linear pressure and temperature perception is reported. The pressure detection part contains the pyramid conductive film and flexible hemispherical electrode, which works as the piezoresistive material. A spiral nickel film works as a temperature‐sensitive material for sensing the temperature of objects during approaching and while contacting. As a result, the dual‐function sensor can detect the pressure in 0–100 kPa with a sensitivity of 11.5 kPa−1, while the temperature is in 25–65 °C with a sensitivity of 0.0032 °C −1. To be state of the art, the tactile sensor array is used for an active safety control of robotic arms, which can realize the perception and timely avoidance of sudden changes in external temperature and object collisions.

A New Force‐Decoupling Triaxial Tactile Sensor Based on Elastic Microcones for Accurately Grasping Feedback

Tactile sensor capable of detecting both normal and tangential forces is essential for the next‐generation robotic electronics. However, designing a reliable triaxial tactile sensor to decouple the normal and tangential forces is still a challenge for current research. Herein, a capacitive triaxial force sensor based on a microcone dielectric layer together with the force decoupling method is proposed. The lateral and vertical displacement of the top electrode relative to the bottom electrodes under the action of external forces can be perfectly detected according to the capacitance variations. The microcone‐structured sensor combined with the new decoupling method leads to a high sensitivity of 3.5 kPa−1 for normal force in a low‐pressure range of 0––50 Pa and 0.134 N−1 for tangential force in 0–0.5 N, and a fast response time of 26 ms. The sensors mounted on the fingers of a robotic arm can provide effective tactile information during the grasping task. Finally, a conceptual 3 × 3 tactile sensor array is fabricated and verified for spatial pressure mapping. This work provides new insight into piezocapacitive tactile sensor design and shows its great potential in robotic applications.

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.

Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human-machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array.

Object Recognition for Humanoid Robots Using Full Hand Tactile Sensor

With the development of human-like robotic technologies, robots have been advanced to sense and recognize objects, which is one branch of artificial intelligence to mimic human reactions. Although high-performance computer technologies are used, the ability of object recognition by touch is still low due to the lack of proper sensors. Therefore, this research studied the object learning and recognition system through robot touches by developing an artificial sensory system acting as an electronic skin with tactile sensors. The Tactile Sensor is developed in this research, consisting of 15 Tactile Sensor Arrays and the palm’s touchpoints. Furthermore, recognition analysis was developed on Bag of Word (BoW) and Convolution Neural Network (CNN) algorithms. With the BoW technique, using Support Vector Machine (SVM) as a classifier with Moment Analysis Descriptor (MA) provided the highest accuracy, showing more than 80.15% accuracy from five grasping of an object. With the CNN approach, InceptionNetV3 provided the highest accuracy of 98.28% from only one capture of an object.

BaroTac: Barometric Three-Axis Tactile Sensor with Slip Detection Capability.

Tactile sensors for robotic applications enhance the performance of robotic end-effectors as they ca n provide tactile information to operate various tasks. In particular, tactile sensors can measure multi-axial force and detect slip can aid the end-effectors in grasping diverse objects in an unstructured environment. We propose BaroTac, which measures three-axial forces and detects slip with a barometric pressure sensor chip (BPSC) for robotic applications. A BPSC is an off-the-shelf commercial sensor that is inexpensive, easy to customize, robust, and simple to use. While a single BPSC-based tactile sensor can measure pressure, an array of BPSC-based tactile sensors can measure multi-axial force through the reactivity of each sensor and detect slip by observing high frequency due to slip vibration. We first experiment with defining the fundamental characteristics of a single-cell BPSC-based sensor to set the design parameters of our proposed sensor. Thereafter, we suggest the sensing method of BaroTac: calibration matrix for three-axis force measurement and discrete wavelet transform (DWT) for slip detection. Subsequently, we validate the three-axis force measuring ability and slip detectability of the fabricated multi-cell BPSC-based tactile sensor. The sensor measures three-axis force with low error (0.14, 0.18, and 0.3% in the X-, Y- and Z-axis, respectively) and discriminates slip in the high-frequency range (75-150 Hz). We finally show the practical applicability of BaroTac by installing them on the commercial robotic gripper and controlling the gripper to grasp common objects based on our sensor feedback.

Flexible capacitive pressure sensor array using acoustic-assisted fabrication of microstructures as surface and dielectric layers

Flexible pressure sensors have attracted considerable interests in the fields of intelligent robotics, human-machine interfaces and health monitoring for their portability, flexibility and real-time sensing performance. The microstructures of dielectric layer significantly affect the sensing performance of capacitive pressure sensors, but the common methods to fabricate microstructures always need physical and/or chemical molds, thus may not suit for low-cost fabrication of sensors. The wavy-shaped microstructures on the surface of fluid can be generated with the excitation of standing surface acoustic waves (SSAWs), which is suitable to be used as dielectric layers. Here, we developed a 6 × 6 flexible capacitive pressure sensor array with fingerprint-like surface cover layer and latticed dielectric layer. Both two layers were fabricated through the novel and rapid acoustic-assisted process. The fabricated capacitive sensor array exhibits high sensitivity of 28.8%/N for normal force loading, with spatial resolution of 1.5 mm. It has been successfully used to monitor the vibration of the Adam’s apple during swallowing and detecting the wrist activities in cycles. These studies demonstrate the great potential of the pressure sensor array with acoustically fabricated microstructures in wearable electronics for health monitoring. • A novel flexible pressure sensor array with spatial resolution of 1.5 mm for three-axis distributed force sensing is developed. • A novel and rapid acoustic-assisted process based on SSAWs is used to fabricate the surface layer and dielectric layer with microstructures. • The tactile sensor array features high flexibility and can be easily worn on neck or wrist for health monitoring. • Both Adam’s apple vibration sensing and wrist activity detection can be achieved by the developed tactile sensor array.

Field effect transistor‐based tactile sensors: From sensor configurations to advanced applications

AbstractThe past several decades have witnessed great progress in high‐performance field effect transistors (FET) as one of the most important electronic components. At the same time, due to their intrinsic advantages, such as multiparameter accessibility, excellent electric signal amplification function, and ease of large‐scale manufacturing, FET as tactile sensors for flexible wearable devices, artificial intelligence, Internet of Things, and other fields to perceive external stimuli has also attracted great attention and become a significant field of general concern. More importantly, FET has a unique three‐terminal structure, which enables its different components to detect external mechanics through different sensing mechanisms. On one hand, it provides an important platform to shed deep insights into the underlying mechanisms of the tactile sensors. On the other hand, these properties could in turn endow excellent components for the construction of tactile matrix sensor arrays with high quality. With special emphasis on the configuration of FETs, this review classified and summarized structure‐optimized FET tactile sensors with gate, dielectric layer, semiconductor layer, and source/drain electrodes as sensing active components, respectively. The working principles and the state‐of‐the‐art protocols in terms of high‐performance tactile sensors are detail discussed and highlighted, the innovative pixel distribution and integration analysis of the transistor sensor matrix array concerning flexible electronics are also introduced. We hope that the introduction of this review can provide some inspiration for future researchers to design and fabricate high‐performance FET‐based tactile sensor chips for flexible electronics and other fields.image

Matrix-addressed crosstalk-free self-powered pressure sensor array based on electrospun isolated PVDF-TrFE cells

Flexible pressure sensor array has attracted much attention in wearable electronics, but still lacks self-power, high resolution, large-area fabrication, and low crosstalk. Herein, we present a self-powered pressure sensor array via patterned piezoelectric polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE) nanofibrous. The PVDF-TrFE nanofibers were collected by patterned metal ground electrodes that isolated each piezoelectric cell. The isolated sensing cells can effectively reduce the crosstalk caused by additional deformation. The potential from individual cell is detected through the row+column electrode configuration to determine the corresponding pressure point and trajectory information. The as-fabricated pressure sensor array showed an excellent linear piezoelectric response (2.5 mV/kPa), a fast response time (5 ms), and good repeatability (25,000 cycles). Furthermore, our piezoelectric devices with constructed PVDF-TrFE layer are feasible in scaling up to a large-area sensor array. Accurate recognitions of the letter "h" trajectory tracking and tactile sensation by our self-powered tactile sensor array show potential applications in electronic skin and artificial intelligence. • The piezoelectric sensor array with isolated PVDF-TrFE cell is constructed to cancel out the voltage crosstalk. • The electrospinning is a versatile and low-cost method to produce self-patterned and polarized nanofibers. • The accurate trajectory recognition based on our sensor array is successfully demonstrated.

Low-cost fabrication of flexible tactile sensor arrays

While for vision and audio the same mass-produced units can be embedded in many different systems from smartphones to robots, tactile sensors have to be built in application-specific shapes and sizes. To use a commercially available tactile sensor, it can be necessary to develop the entire system around an existing sensor model. We present a set of open-source solutions for designing, manufacturing, reading and integrating custom application-specific tactile matrix sensors. Our manufacturing process only requires an off-the-shelf cutting plotter and widely available plastic and metal foils. This allows creating sensors of diverse sizes, shapes, and layouts, which can be adapted to various specific use cases as demonstrated with exemplary robot integrations. For interfacing and readout, we develop an Arduino-like prototype board (Tacduino) with amplifier circuits to ensure good resolution and to suppress crosstalk. As an example, we give step-by-step instructions to build tactile fingertips for the RobotiQ 3-Finger Gripper, and we provide design files for the readout circuit board together with Arduino firmware and driver software. Both, wired and wireless communication between the sensors and a host PC are supported by this system. The hardware was originally presented and investigated in [1].

Large-Scale Integrated Flexible Tactile Sensor Array for Sensitive Smart Robotic Touch.

In the long pursuit of smart robotics, it has been envisioned to empower robots with human-like senses, especially vision and touch. While tremendous progress has been made in image sensors and computer vision over the past decades, tactile sense abilities are lagging behind due to the lack of large-scale flexible tactile sensor array with high sensitivity, high spatial resolution, and fast response. In this work, we have demonstrated a 64 × 64 flexible tactile sensor array with a record-high spatial resolution of 0.9 mm (equivalently 28.2 pixels per inch) by integrating a high-performance piezoresistive film (PRF) with a large-area active matrix of carbon nanotube thin-film transistors. PRF with self-formed microstructures exhibited high pressure-sensitivity of ∼385 kPa-1 for multi-walled carbon nanotubes concentration of 6%, while the 14% one exhibited fast response time of ∼3 ms, good linearity, broad detection range beyond 1400 kPa, and excellent cyclability over 3000 cycles. Using this fully integrated tactile sensor array, the footprint maps of an artificial honeybee were clearly identified. Furthermore, we hardware-implemented a smart tactile system by integrating the PRF-based sensor array with a memristor-based computing-in-memory chip to record and recognize handwritten digits and Chinese calligraphy, achieving high classification accuracies of 98.8% and 97.3% in hardware, respectively. The integration of sensor networks with deep learning hardware may enable edge or near-sensor computing with significantly reduced power consumption and latency. Our work could empower the building of large-scale intelligent sensor networks for next-generation smart robotics.

Face mediated human–robot interaction for remote medical examination

Realtime visual feedback from consequences of actions is useful for future safety-critical human–robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human–robot interaction in remote examination remains understudied. Here we describe a face mediated human–robot interaction approach for remote palpation. It builds upon a robodoctor–robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human–robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human–robot interaction in remote medical examinations.

Tactile Near-Sensor Analogue Computing for Ultrafast Responsive Artificial Skin.

Ultrafast artificial skin enables unprecedented tactile internet applications in prosthetics, robotics, and human-machine interactions. However, current artificial skin systems that rely on front-end interface electronics typically perform redundant data transfer and analogue-to-digital conversions for decision-making, causing long latency (milliseconds). Here, a near-sensor analogue computing system based on a flexible memristor array for artificial skin applications is reported. This system, which seamlessly integrates a tactile sensor array with a flexible hafnium oxide memristor array, can simultaneously sense and compute raw multiple analogue pressure signals without interface electronics. As a proof-of-concept, the system is used for real-time noise reduction and edge detection of tactile stimuli. One sensing-computing operation of this system takes about 400ns and consumes on average 1000 times less power than a conventional interface electronic system. The results demonstrate that near-sensor analogue computing offers an ultrafast and energy-efficient route to large-scale artificial skin systems.

Magnetostrictive Tactile Sensor Array Based on L-Shaped Galfenol Wire and Application for Tilt Detection

A magnetostrictive tactile sensor unit with a large force measurement range and high sensitivity is designed using L-shaped Galfenol wires, tunnel magnetoresistive elements, and cylindrical permanent magnets. The output voltage model of the sensor unit is established based on the inverse magnetostrictive effect, tunnel magnetoresistance, and elastodynamic theory. Structures of the sensor unit and array are optimized using COMSOL. The static, dynamic, and temperature characteristics are tested. The interaction between the output voltages of each unit in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> 3 tactile sensor array is described. The experimental results show that the sensitivity of the sensor unit is 85.5 mV/N at 0-10 N and 28.4 mV/N at 10-16 N under a biased magnetic field of 1.671 kA/m. The dynamic force sensitivity of the sensor unit does not vary by more than 1.4%. The magnetic field lateral coupling and longitudinal coupling of the sensor array do not exceed a maximum of 2.3%. The array can accurately distinguish the force distribution when multiple contacts are subjected to force at the same time. The sensitivity does not vary by more than 3.9% over the ambient temperature range of 23- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$59~^{\circ }\text{C}$ </tex-math></inline-formula> . The tactile sensor array is mounted on the mechanical fingertips to grasp bar-shaped objects with different tilt angles. The output voltage of the sensor array obtained from the robotic grasping experiments is analyzed using Probabilistic Neural Network, which can determine the tilt angle of the grasped objects, and the accuracy rate of object tilt recognition is 96.74%.

Three-Dimensional Printed Bimodal Electronic Skin with High Resolution and Breathability for Hair Growth.

People with neurological deficits face difficulties perceiving their surroundings, resulting in an urgent need for wearable electronic skin (e-skin) that can monitor external stimuli and temperature changes. However, the monolithic structure of e-skin is not conducive to breathability and hinders hair growth, limiting its wearing comfort. In this work, we prepared fully three-dimensional (3D) printed e-skin that allowed hair penetration and growth. This e-skin also achieved simultaneous pressure and temperature detection and a high tactile resolution of 100 cm-2, which is close to that of human fingertips. The temperature sensor maintained linear measurements within 10-60 °C. The pore microstructure prepared by a sacrificial template method helped the pressure-sensing unit achieve a high sensitivity of 0.213 kPa-1. Considering the distribution of human hair, the design of the main structure of the e-skin was studied to realize hair penetration and growth. High-performance pressure-sensitive inks and transparent flexible substrate inks for 3D printing were developed, and e-skins combining these functions were realized through multimaterial in situ 3D printing with high accuracy and high consistency. The temperature and pressure sensors separately performed simultaneous detection without interference, and the tactile sensor array accurately identified stimuli at different locations.

An Open-Environment Tactile Sensing System: Toward Simple and Efficient Material Identification.

Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.

Active Perception in Non-Visual Recognition Environments by Stretchable Tentacle Sensor Arrays.

Smoke fog or other light-interference environments have intrinsic obstruction for visual recognition techniques to explore objects and surroundings. Alternatively, tactile perceptions, rather than visual observations, are commonly used by burrowing or deep-sea animals to communicate with environments. Bio-inspired by this natural wisdom, here, we demonstrate stretchable tentacle sensor arrays, which can recognize surrounding objects located in non-visual conditions such as smoke fog or dark environment. Each tentacle sensor is composed of two functional parts: a retractable tentacle with a magnetic top and an elastomer bottom containing copper coils. Different from traditionally passive tactile sensors, these tentacle sensors can actively stretch under the control of a syringe pump, yielding different electrical signals when in contact with the objects. Analyzing collected sensing signals of those tactile sensor arrays by the feature analysis model, complex morphological information of irregular objects in the smoke fog can be recognized. Our study reveals a fundamental connection between stretchable tactile sensors and feature analysis and demonstrates its practical potential for active perception in a non-visual recognition environment.

Integrated Piezoresistive Normal Force Sensors Fabricated Using Transfer Processes with Stiction Effect Temporary Handling.

Tactile sensation is a highly desired function in robotics. Furthermore, tactile sensor arrays are crucial sensing elements in pulse diagnosis instruments. This paper presents the fabrication of an integrated piezoresistive normal force sensor through surface micromachining. The force sensor is transferred to a readout circuit chip via a temporary stiction effect handling process. The readout circuit chip comprises two complementary metal-oxide semiconductor operational amplifiers, which are redistributed to form an instrumentation amplifier. The sensor is released and temporarily bonded to the substrate before the transfer process due to the stiction effect to avoid the damage and movement of the diaphragm during subsequent flip-chip bonding. The released sensor is pulled off from the substrate and transferred to the readout circuit chip after being bonded to the readout circuit chip. The size of the transferred normal force sensor is 180 μm × 180 μm × 1.2 μm. The maximum misalignment of the flip-chip bonding process is approximately 1.5 μm, and sensitivity is 93.5 μV/μN/V. The routing of the piezoresistive Wheatstone bridge can be modified to develop shear force sensors; consequently, this technique can be used to develop tactile sensors that can sense both normal and shear forces.

A poling-free PVDF nanocomposite via mechanically directional stress field for self-powered pressure sensor application

It is well known that Poly(vinylidene fluoride) (PVDF) polymer and its composites exhibit limited piezoelectricity only after strong electric field poling (SEFP) to align randomly oriented molecular dipoles inside. Here, we report that a (Pb, Zr)TiO3 (PZT) particles doped PVDF-polymer nanocomposite shows a large poling-free piezoelectric (PFP) coefficient and strong electromechanical coupling after experiencing mechanically directional stress field (MDSF). Analyses based on WAXD, FTIR, and HRTEM reveal that the MDSF actives and then induces a crystal phase transformation (CPT) from disordered star-shape nanocrystals to ordered, self-poled chain-shape high-β nanocrystalline fibers. PFM scanning images further show the existence of well-defined polarization. Furthermore, a 7-layer series-connected, self-powered circular pressure sensor was fabricated using multi-material 3D-printing technology, which exhibits a high sensitivity of 235 mV/kPa and a high-power density of 0.9 mW/cm2 under a dynamic pressure of 255 kPa, and it is near 8 times higher than that of a conventional, poled single-layer PVDF sensor. Finally, a (3 × 3) real-time lighting tactile sensor array is 3D printed, confirming its feasibility for practical application. The MDSF-induced CPT and large PFP effect are significant because it may open a way to fabricate piezopolymer integrated devices without SEFP.