Tactile tribotronic reconfigurable p-n junctions for artificial synapses

Evolution of Tribotronics: From Fundamental Concepts to Potential Uses

The intelligent sensing network is one of the key components in the construction of the Internet of Things, and the power supply technology of sensor communication nodes needs to be solved urgently. As a new field combining tribo-potential with semiconductor devices, tribotronics, based on the contact electrification (CE) effect, realizes direct interaction between the external environment and semiconductor devices by combining triboelectric nanogenerator (TENG) and field-effect transistor (FET), further expanding the application prospects of micro/nano energy. In this paper, the research progress of tribotronics is systematically reviewed. Firstly, the mechanism of the CE effect and the working principles of TENG are introduced. Secondly, the regulation theory of tribo-potential on carrier transportation in semiconductor devices and the research status of tribotronic transistors are summarized. Subsequently, the applications of tribotronics in logic circuits and memory devices, smart sensors, and artificial synapses in recent years are demonstrated. Finally, the challenges and development prospects of tribotronics in the future are projected.

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Multimodal Artificial Synapses for Neuromorphic Application

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect

It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.

Pseudocapacitance-Induced Synaptic Plasticity of Tribo-Phototronic Effect Between Ionic Liquid and 2D MoS2

Contact-induced electrification, commonly referred to as triboelectrification, is the subject of extensive investigation at liquid-solid interfaces due to its wide range of applications in electrochemistry, energy harvesting, and sensors. This study examines the triboelectric between an ionic liquid and 2D MoS2 under light illumination. Notably, when a liquid droplet slides across the MoS2 surface, an increase in the generated current and voltage is observed in the forward direction, while a decrease is observed in the reverse direction. This suggests a memory-like tribo-phototronic effect between ionic liquid and 2D MoS2 . The underlying mechanism behind this tribo-phototronic synaptic plasticity is proposed to be ion adsorption/desorption processes resulting from pseudocapacitive deionization/ionization at the liquid-MoS2  interface. This explanation is supported by the equivalent electrical circuit modeling, contact angle measurements, and electronic band diagrams. Furthermore, the influence of various factors such as velocity, step size, light wavelength and intensity, ion concentration, and bias voltage is thoroughly investigated. The artificial synaptic plasticity arising from this phenomenon exhibits significant synaptic features, including potentiation/inhibition, paired-pulse facilitation/depression, and short-term memory (STM) to long-term memory (LTM) transition. This research opens up promising avenues for the development of synaptic memory systems and intelligent sensing applications based on liquid-solid interfaces.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

Laminating Structure for Interlayer Corona Discharge Treatment Toward Ion-Based Nanogenerators

A corona discharge treatment (CDT) is utilized to maximize the performance of triboelectric nanogenerators (TENGs) by injecting extra electrons into the negative tribomaterials. Increased performance of CDT TENGs, however, exhibits rapid degradation due to the electron dissipation by air moisture or thermal emission. To overcome such drawbacks and circumvent such dissipation, the source of charges should be replaced with ionic charges. This study reports a Ag nanowires (NWs)-embedded laminating structure (AeLS) with a unique fabrication procedure for ionic charge injection by CDT. The injection of ions is achieved by interlayer-CDT (i-CDT), in which positive charges are dissipated by Ag NWs, and the opposite negative ions can remain on the outmost surface. The AeLS TENGs with i-CDT exhibit high performance, long-term stability, and durability. It shows voltage, current, and maximum power outputs of 380 V, 15 µA, and 827 mW m^-2 , respectively. As a practical demonstration, rotational TENG integrated with a direct discharge system is realized, and its current and voltage reach 7.4 mA and 7800 V, respectively. This work can pave the way for the design of ion-based TENGs with high performance and long-lasting retention of triboelectric charges.

Reconfigurable Artificial Synapse Based on Ambipolar Floating Gate Memory

Artificial synapse networks capable of massively parallel computing and mimicking biological neural networks can potentially improve the processing efficiency of existing information technologies. Semiconductor devices functioning as excitatory and inhibitory synapses are crucial for developing intelligence systems, such as traffic control systems. However, achieving reconfigurability between two working modes (inhibitory and excitatory) and bilingual synaptic behavior in a single transistor remains challenging. This study successfully mimics a bilingual synaptic response using an artificial synapse based on an ambipolar floating gate memory comprising tungsten selenide (WSe₂)/hexagonal boron nitride (h-BN)/ molybdenum telluride (MoTe₂). In this WSe₂/h-BN/MoTe₂ structure, ambipolar semiconductors WSe₂ and MoTe₂ are inserted as channel and floating gates, respectively, and h-BN serves as the tunneling barrier layer. Using either positive or negative pulse amplitude modulations at the control gate, this device with bipolar channel conduction produced eight distinct resistance states. Based on this, we experimentally projected that we could achieve 490 memory states (210 hole-resistance states + 280 electron-resistance states). Using the bipolar charge transport and multistorage states of WSe₂/h-BN/MoTe₂ floating gate memory, we mimicked reconfigurable excitatory and inhibitory synaptic plasticity in a single device. Furthermore, the convolution neural network formed by these synaptic devices can recognize handwritten digits with an accuracy of >92%. This study identifies the unique properties of heterostructure devices based on two-dimensional materials as well as predicts their applicability in advanced recognition of neuromorphic computing.