Artificially Intelligent Tactile Ferroelectric Skin

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Recent advancements in implantable neural links based on organic synaptic transistors

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Porous nanocomposites with enhanced intrinsic piezoresistive sensitivity for bioinspired multimodal tactile sensors

In this work, we propose porous fluororubber/thermoplastic urethane nanocomposites (PFTNs) and explore their intrinsic piezoresistive sensitivity to pressure. Our experiments reveal that the intrinsic sensitivity of the PFTN-based sensor to pressure up to 10 kPa increases up to 900% compared to the porous thermoplastic urethane nanocomposite (PTN) counterpart and up to 275% compared to the porous fluororubber nanocomposite (PFN) counterpart. For pressures exceeding 10 kPa, the resistance-pressure relationship of PFTN follows a logarithmic function, and the sensitivity is 221% and 125% higher than that of PTN and PFN, respectively. With the excellent intrinsic sensitivity of the thick PFTN film, a single sensing unit with integrated electrode design can imitate human skin for touch detection, pressure perception and traction sensation. The sensing range of our multimodal tactile sensor reaches ~150 Pa, and it exhibits a linear fit over 97% for both normal pressure and shear force. We also demonstrate that an electronic skin, made of an array of sensing units, is capable of accurately recognizing complex tactile interactions including pinch, spread, and tweak motions.

Origami-Inspired Conductive Paper-Based Folded Pressure Sensor with Interconnection Scaling at the Crease for Novel Wearable Applications

Drawing inspiration from origami structures, a pressure sensor was developed with unique interconnection scaling at its creases crafted on a conductive paper substrate, paving the way for advanced wearable technology. Two screen-printed conductive paper substrates were combined face-to-face, and specific folds were introduced to optimize the sensor structure. The Electrical Contact Resistance (ECR) was systematically analyzed across different fold numbers and crease gaps, revealing a notable trade-off: while increasing the number of folds expanded the sensing area, it also influenced the ECR, reaching a performance plateau. Strategic modifications in the sensor's design, including refining interconnections at the crease, enhanced its sensitivity and stability, culminating in a remarkable sensitivity of 3.75 kPa-1 at subtle pressure levels (0-0.05 kPa). This sensor's real-world applications proved to be transformative, from detecting bruxism and aiding in neck posture correction to remotely sensing trigger finger locking phenomena, highlighting its potential as a pivotal tool in upcoming medical diagnostics and treatments.

Piezoelectricity, Pyroelectricity, and Ferroelectricity in Biomaterials and Biomedical Applications

Piezoelectric, pyroelectric, and ferroelectric materials are considered unique biomedical materials due to their dielectric crystals and asymmetric centers that allow them to directly convert various primary forms of energy in the environment, such as sunlight, mechanical energy, and thermal energy, into secondary energy, such as electricity and chemical energy. These materials possess exceptional energy conversion ability and excellent catalytic properties, which have led to their widespread usage within biomedical fields. Numerous biomedical applications have demonstrated great potential with these materials, including disease treatment, biosensors, and tissue engineering. For example, piezoelectric materials are used to stimulate cell growth in bone regeneration, while pyroelectric materials are applied in skin cancer detection and imaging. Ferroelectric materials have even found use in neural implants that record and stimulate electrical activity in the brain. This paper reviews the relationship between ferroelectric, piezoelectric, and pyroelectric effects and the fundamental principles of different catalytic reactions. It also highlights the preparation methods of these three materials and the significant progress made in their biomedical applications. The review concludes by presenting key challenges and future prospects for efficient catalysts based on piezoelectric, pyroelectric, and ferroelectric nanomaterials for biomedical applications.

High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors

The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3  S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Ion trap and release dynamics enables nonintrusive tactile augmentation in monolithic sensory neuron

An iontronic-based artificial tactile nerve is a promising technology for emulating the tactile recognition and learning of human skin with low power consumption. However, its weak tactile memory and complex integration structure remain challenging. We present an ion trap and release dynamics (iTRD)-driven, neuro-inspired monolithic artificial tactile neuron (NeuroMAT) that can achieve tactile perception and memory consolidation in a single device. Through the tactile-driven release of ions initially trapped within iTRD-iongel, NeuroMAT only generates nonintrusive synaptic memory signals when mechanical stress is applied under voltage stimulation. The induced tactile memory is augmented by auxiliary voltage pulses independent of tactile sensing signals. We integrate NeuroMAT with an anthropomorphic robotic hand system to imitate memory-based human motion; the robust tactile memory of NeuroMAT enables the hand to consistently perform reliable gripping motion.

Tactile Neuromorphic System: Convergence of Triboelectric Polymer Sensor and Ferroelectric Polymer Synapse

Sensory neuromorphic systems are a promising technology, because they can replicate the way the human peripheral nervous system processes signals from the five sensory organs. Despite this potential, there are limited studies on how to implement these systems on a hardware neural network platform. In our research, we propose a tactile neuromorphic system that uses a poly(dimethylsiloxane) (PDMS)-based triboelectric sensor and a molybdenum disulfide (MoS2)/poly(vinylidene fluoride-trifluoro ethylene) (P(VDF-TrFE)) heterostructure-based ferroelectric synapse. The triboelectric sensor mimics a human tactile organ by converting tactile stimuli into electrical signals in real time. The ferroelectric synapse we developed demonstrates exceptional long-term potentiation/depression characteristics with a maximum dynamic range of 78 and a symmetrical value of 4.7. To assess the practicality of our proposed system, we conducted training and recognition simulations using Morse code alphabets and MNIST handwritten digits. The maximum recognition rate that we achieved was 96.17%.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5 × 104 cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

Photonic synaptic transistors with new electron trapping layer for high performance and ultra-low power consumption

Photonic synaptic transistors are being investigated for their potential applications in neuromorphic computing and artificial vision systems. Recently, a method for establishing a synaptic effect by preventing the recombination of electron-hole pairs by forming an energy barrier with a double-layer consisting of a channel and a light absorption layer has shown effective results. We report a triple-layer device created by coating a novel electron-trapping layer between the light-absorption layer and the gate-insulating layer. Compared to the conventional double-layer photonic synaptic structure, our triple-layer device significantly reduces the recombination rate, resulting in improved performance in terms of the output photocurrent and memory characteristics. Furthermore, our photonic synaptic transistor possesses excellent synaptic properties, such as paired-pulse facilitation (PPF), short-term potentiation (STP), and long-term potentiation (LTP), and demonstrates a good response to a low operating voltage of - 0.1 mV. The low power consumption experiment shows a very low energy consumption of 0.01375 fJ per spike. These findings suggest a way to improve the performance of future neuromorphic devices and artificial vision systems.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Adaptive Memory and In Materia Reinforcement Learning Enabled by Flexoelectric-like Response from Ultrathin HfO2

Reinforcement learning (RL) is a mathematical framework of neural learning by trial and error that revolutionized the field of artificial intelligence. However, until now, RL has been implemented in algorithms with the compatibly of traditional complementary metal-oxide-semiconductor-based von Neumann digital platforms, which thus limits performance in terms of latency, fault tolerance, and robustness. Here, we demonstrate that nanocolumnar (∼12 nm) HfO₂ structures can be used as building blocks to conduct the RL within the material by combining its stress-adjustable charge transport and memory functions. Specifically, HfO₂ nanostructures grown by the sputtering method exhibit self-assembled vertical nanocolumnar structures that generate voltage depending on the impact of stress under self-biased conditions. The observed results are attributed to the flexoelectric-like response of HfO₂. Further, multilevel current (>10^-3 A) modulation with touch and controlled suspension (∼10^-12 A) with a short electric pulse (100 ms) were demonstrated, yielding a proof-of-concept memory with an on/off ratio greater than 10⁹. Utilizing multipattern dynamic memory and tactile sensing, RL was implemented to successfully solve a maze game using an array of 6 × 4. This work could pave the way to do RL within materials for a variety of applications such as memory storage, neuromorphic sensors, smart robots, and human-machine interaction systems.

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.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

Ti2C3Tx/Polyurethane Constructed by Gas-Liquid Interface Self-Assembly for Underwater Sensing

Nowadays, multifunctional, easily prepared, and highly sensitive flexible sensors have attracted extensive attention and are gradually used in various scenarios. Here, we report the design of the Ti₂C₃T_x/polyurethane composites prepared by a facile gas-liquid interface self-assembly. The obtained flexible sensor has a wide detection range (∼900%), a low-stress detection limit (<1%), a high sensitivity (GF = 1.3, strain from 0 to 100%), and a fast response time (<140 ms). The multifunctional stress sensor can be applied to not only wearable motion monitoring and detection of various signals but also the detection of underwater human motion, as well as different motion states and swimming frequencies of toy fish in water, demonstrating its great prospects in a variety of applications, such as human movement monitoring and marine biological detection and research.

An Artificial Tactile Neuron Enabling Spiking Representation of Stiffness and Disease Diagnosis

Mechanical properties of biological systems provide useful information about the biochemical status of cells and tissues. Here, an artificial tactile neuron enabling spiking representation of stiffness and spiking neural network (SNN)-based learning for disease diagnosis is reported. An artificial spiking tactile neuron based on an ovonic threshold switch serving as an artificial soma and a piezoresistive sensor as an artificial mechanoreceptor is developed and shown to encode the elastic stiffness of pressed materials into spike frequency evolution patterns. SNN-based learning of ultrasound elastography images abstracted by spike frequency evolution rate enables the classification of malignancy status of breast tumors with a recognition accuracy up to 95.8%. The stiffness-encoding artificial tactile neuron and learning of spiking-represented stiffness patterns hold a great promise for the identification and classification of tumors for disease diagnosis and robot-assisted surgery with low power consumption, low latency, and yet high accuracy.

Machine-Learning-Assisted Recognition on Bioinspired Soft Sensor Arrays

Soft interfaces with self-sensing capabilities play an essential role in environment awareness and reaction. The growing overlap between materials and sensory systems has created a myriad of challenges for sensor integration, including the design of a multimodal sensory, simplified system design capable of high spatiotemporal sensing resolution and efficient processing methods. Here we report a bioinspired soft sensor array (BOSSA) that integrates pressure and material sensing capabilities based on the triboelectric effect. Cascaded row + column electrodes embedded in low-modulus porous silicone rubber allow rich information to be captured from the environment and further analyzed by data-driven algorithms (multilayer perceptrons) to extract higher level features. BOSSA demonstrates the ability to identify 10 users (98.9%) and identify the placement or extraction of 10 objects (98.6%). Moreover, its scalable fabrication facilitates large-area sensor arrays with high spatiotemporal resolution and multimodal sensing abilities yet with a less complex system architecture. These features may be promising in the development of immersive sensing networks for intelligent monitoring and stimuli response in smart home/industry applications.

Stretchable Neuromorphic Transistor That Combines Multisensing and Information Processing for Epidermal Gesture Recognition

We fabricated a nanowire-channel intrinsically stretchable neuromorphic transistor (NISNT) that perceives both tactile and visual information and emulates neuromorphic processing capabilities. The device demonstrated excellent stretching endurance of 1000 stretch cycles while retaining stable electrical properties. The device was then applied as a multisensitive afferent nerve that processes information in parallel. Compatible with skin deformation, the devices are attached to fingers to serve as conformal strain sensors and neuromorphic information-processing units for gesture recognition. The excitatory postsynaptic current in each device represents shape changes and is then analyzed using softmax activation processing of the neural network to recognize gestures. A multistage neural network that uses NISNT was used to further confirm the gestures. This work demonstrated an idea toward multisensory artificial nerves and neuromorphic systems.

Bird-Inspired Self-Navigating Artificial Synaptic Compass

Extrasensory neuromorphic devices that can recognize, memorize, and learn stimuli imperceptible to human beings are of considerable interest in interactive intelligent electronics research. This study presents an artificially intelligent magnetoreceptive synapse inspired by the magnetocognitive ability used by birds for navigation and orientation. The proposed synaptic platform is based on arrays of ferroelectric field-effect transistors with air-suspended magneto-interactive top-gates. A suspended gate of an elastomeric composite with superparamagnetic particles laminated with an electrically conductive polymer is mechanically deformed under a magnetic field, facilitating control of the magnetic-field-dependent contact area of the suspended gate with an underlying ferroelectric layer. The remanent polarization of the ferroelectric layer is electrically programmed with the deformed suspended gate, resulting in analog conductance modulation as a function of the magnitude, number, and time interval of the input magnetic pulses. The proposed extrasensory magnetoreceptive synapse may be used as an artificially intelligent synaptic compass that facilitates barrier-adaptable navigation and mapping of a moving object.

Research of a Novel Ag Temperature Sensor Based on Fabric Substrate Fabricated by Magnetron Sputtering

TPU-coated polyester fabric was used as the substrate of a flexible temperature sensor and Ag nanoparticles were deposited on its surface as the temperature sensing layer by the magnetron sputtering method. The effects of sputtering powers and heat treatment on properties of the sensing layers, such as the temperature coefficient of resistance (TCR), linearity, hysteresis, drift, reliability, and bending resistance, were mainly studied. The results showed that the TCR (0.00234 °C-1) was the highest when sputtering power was 90 W and sputtering pressure was 0.8 Pa. The crystallinity of Ag particles would improve, as the TCR was improved to 0.00262 °C-1 under heat treatment condition at 160°. The Ag layer obtained excellent linearity, lower hysteresis and drift value, as well as good reliability and bending resistance when the sputtering power was 90 W. The flexible temperature sensor based on the coated polyester fabric improved the softness and comfortableness of sensor, which can be further applied in intelligent wearable products.

Progress in the Applications of Smart Piezoelectric Materials for Medical Devices

Smart piezoelectric materials are of great interest due to their unique properties. Piezoelectric materials can transform mechanical energy into electricity and vice versa. There are mono and polycrystals (piezoceramics), polymers, and composites in the group of piezoelectric materials. Recent years show progress in the applications of piezoelectric materials in biomedical devices due to their biocompatibility and biodegradability. Medical devices such as actuators and sensors, energy harvesting devices, and active scaffolds for neural tissue engineering are continually explored. Sensors and actuators from piezoelectric materials can convert flow rate, pressure, etc., to generate energy or consume it. This paper consists of using smart materials to design medical devices and provide a greater understanding of the piezoelectric effect in the medical industry presently. A greater understanding of piezoelectricity is necessary regarding the future development and industry challenges.

Advancing Versatile Ferroelectric Materials Toward Biomedical Applications

Ferroelectric materials (FEMs), possessing piezoelectric, pyroelectric, inverse piezoelectric, nonlinear optic, ferroelectric-photovoltaic, and many other properties, are attracting increasing attention in the field of biomedicine in recent years. Because of their versatile ability of interacting with force, heat, electricity, and light to generate electrical, mechanical, and optical signals, FEMs are demonstrating their unique advantages for biosensing, acoustics tweezer, bioimaging, therapeutics, tissue engineering, as well as stimulating biological functions. This review summarizes the current-available FEMs and their state-of-the-art fabrication techniques, as well as provides an overview of FEMs-based applications in the field of biomedicine. Challenges and prospects for future development of FEMs for biomedical applications are also outlined.