Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired Neuromorphic Computing

Vertically Integrated Monolithic Neuromorphic Nanowire Device for Physiological Information Processing

This study demonstrates the conceptual design and fabrication of a vertically integrated monolithic (VIM) neuromorphic device. The device comprises an n-type SnO2 nanowire bottom channel connected by a shared gate to a p-type P3HT nanowire top channel. This architecture establishes two distinct neural pathways with different response behaviors. The device generates excitatory and inhibitory postsynaptic currents, mimicking the corelease mechanism of bilingual synapses. To enhance the signal processing efficiency, we employed a bipolar spike encoding strategy to convert fluctuating sensory signals to spike trains containing positive and negative pulses. Utilizing the neuromorphic platform for synaptic processing, physiological signals featuring bidirectional fluctuations, including electrocardiogram and breathing signals, can be classified with an accuracy of over 90%. The VIM device holds considerable promise as a solution for developing highly integrated neuromorphic hardware for healthcare and edge intelligence applications.

Organic High-Temperature Synaptic Phototransistors for Energy-Efficient Neuromorphic Computing

Organic optoelectronic synaptic devices that can reliably operate in high-temperature environments (i.e., beyond 121°C) or remain stable after high-temperature treatments have significant potential in biomedical electronics and bionic robotic engineering. However, it is challenging to acquire this type of organic devices considering the thermal instability of conventional organic materials and the degradation of photoresponse mechanisms at high temperatures. Here, high-temperature synaptic phototransistors (HTSPs) based on thermally stable semiconductor polymer blends as the photosensitive layer are developed, successfully simulating fundamental optical-modulated synaptic characteristics at a wide operating temperature range from room temperature to 220°C. Robust optoelectronic performance can be observed in HTSPs even after experiencing 750 h of the double 85 testing due to the enhanced operational reliability. Using HTSPs, Morse-code optical decoding scheme and the visual object recognition capability are also verified at elevated temperatures. Furthermore, flexible HTSPs are fabricated, demonstrating an ultralow power consumption of 12.3 aJ per synaptic event at a low operating voltage of -0.05 mV. Overall, the conundrum of achieving reliable optical-modulated neuromorphic applications while balancing low power consumption can be effectively addressed. This research opens up a simple but effective avenue for the development of high-temperature and energy-efficient wearable optoelectronic devices in neuromorphic computing applications.

Stretchable Transistor-Structured Artificial Synapses for Neuromorphic Electronics

Stretchable synaptic transistors, a core technology in neuromorphic electronics, have functions and structures similar to biological synapses and can concurrently transmit signals and learn. Stretchable synaptic transistors are usually soft and stretchy and can accommodate various mechanical deformations, which presents significant prospects in soft machines, electronic skin, human-brain interfaces, and wearable electronics. Considerable efforts have been devoted to developing stretchable synaptic transistors to implement electronic device neuromorphic functions, and remarkable advances have been achieved. Here, this review introduces the basic concept of artificial synaptic transistors and summarizes the recent progress in device structures, functional-layer materials, and fabrication processes. Classical stretchable synaptic transistors, including electric double-layer synaptic transistors, electrochemical synaptic transistors, and optoelectronic synaptic transistors, as well as the applications of stretchable synaptic transistors in light-sensory systems, tactile-sensory systems, and multisensory artificial-nerves systems, are discussed. Finally, the current challenges and potential directions of stretchable synaptic transistors are analyzed. This review presents a detailed introduction to the recent progress in stretchable synaptic transistors from basic concept to applications, providing a reference for the development of stretchable synaptic transistors in the future.

Double-Floating-Gate van der Waals Transistor for High-Precision Synaptic Operations

Two-dimensional materials and their heterostructures have thus far been identified as leading candidates for nanoelectronics owing to the near-atom thickness, superior electrostatic control, and adjustable device architecture. These characteristics are indeed advantageous for neuro-inspired computing hardware where precise programming is strongly required. However, its successful demonstration fully utilizing all of the given benefits remains to be further developed. Herein, we present van der Waals (vdW) integrated synaptic transistors with multistacked floating gates, which are reconfigured upon surface oxidation. When compared with a conventional device structure with a single floating gate, our double-floating-gate (DFG) device exhibits better nonvolatile memory performance, including a large memory window (>100 V), high on-off current ratio (∼10⁷), relatively long retention time (>5000 s), and satisfactory cyclic endurance (>500 cycles), all of which can be attributed to its increased charge-storage capacity and spatial redistribution. This facilitates highly effective modulation of trapped charge density with a large dynamic range. Consequently, the DFG transistor exhibits an improved weight update profile in long-term potentiation/depression synaptic behavior for nearly ideal classification accuracies of up to 96.12% (MNIST) and 81.68% (Fashion-MNIST). Our work adds a powerful option to vdW-bonded device structures for highly efficient neuromorphic computing.

α-Fe2O3-based artificial synaptic RRAM device for pattern recognition using artificial neural networks

We report on theα-Fe₂O₃-based artificial synaptic resistive random access memory device, which is a promising candidate for artificial neural networks (ANN) to recognize the images. The device consists of a structure Ag/α-Fe₂O₃/FTO and exhibits non-volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation, long-term depression, and spike time-dependent plasticity. In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained fromα-Fe₂O₃based artificial synaptic device. The proposedα-Fe₂O₃-based device was tested with the FMNIST and MNIST datasets and obtained a high pattern recognition accuracy of 88.06% and 97.6% test accuracy respectively. Such a high pattern recognition accuracy is attributed to the combination of the synaptic device performance as well as the novel weight mapping strategy used in the present work. Therefore, the ideal device characteristics and high ANN performance showed that the fabricated device can be useful for practical ANN implementation.

Improving the linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistors via CdSe quantum dots decoration

The linearity of synaptic plasticity of single-walled carbon nanotube field-effect transistor (SWCNT FET) was improved by CdSe quantum dots decoration. The linearity of synaptic plasticity in SWCNT FET with decorating QDs was further improved by reducing the P-type doping level from the atmosphere. The synaptic behavior of SWCNT FET is found to be dominated by the charging and discharging processes of interface traps and surface traps, which are predominantly composed of H₂O/O₂redox couples. The improved synaptic behavior is mainly due to the reduction of the interface trap charging process after QDs decoration. The inherent correlation between the device synaptic behavior and the electron capture process of the traps are investigated through charging-based trap characterization. This study provides an effective scheme for improving linearity and designing new-type SWCNT synaptic devices.

A Flexible Tribotronic Artificial Synapse with Bioinspired Neurosensory Behavior

As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future.

Recent advances in neuromorphic transistors for artificial perception applications: FOCUS ISSUE REVIEW

Conventional von Neumann architecture is insufficient in establishing artificial intelligence (AI) in terms of energy efficiency, computing in memory and dynamic learning. Delightedly, rapid developments in neuromorphic computing provide a new paradigm to solve this dilemma. Furthermore, neuromorphic devices that can realize synaptic plasticity and neuromorphic function have extraordinary significance for neuromorphic system. A three-terminal neuromorphic transistor is one of the typical representatives. In addition, human body has five senses, including vision, touch, auditory sense, olfactory sense and gustatory sense, providing abundant information for brain. Inspired by the human perception system, developments in artificial perception system will give new vitality to intelligent robots. This review discusses the operation mechanism, function and application of neuromorphic transistors. The latest progresses in artificial perception systems based on neuromorphic transistors are provided. Finally, the opportunities and challenges of artificial perception systems are summarized.

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

Tuning Ambipolarity of the Conjugated Polymer Channel Layers of Floating-Gate Free Transistors: From Volatile Memories to Artificial Synapses

Three-terminal synaptic transistor has drawn significant research interests for neuromorphic computation due to its advantage of facile device integrability. Lately, bulk-heterojunction-based synaptic transistors with bipolar modulation are proposed to exempt the use of an additional floating gate. However, the actual correlation between the channel's ambipolarity, memory characteristic, and synaptic behavior for a floating-gate free transistor has not been investigated yet. Herein, by studying five diketopyrrolopyrrole-benzotriazole dual-acceptor random conjugated polymers, a clear correlation among the hole/electron ratio, the memory retention characteristic, and the synaptic behavior for the polymer channel layer in a floating-gate free transistor is described. It reveals that the polymers with balanced ambipolarity possess better charge trapping capabilities and larger memory windows; however, the high ambipolarity results in higher volatility of the memory characteristics, namely poor memory retention capability. In contrast, the polymer with a reduced ambipolarity possesses an enhanced memory retention capability despite showing a reduced memory window. It is further manifested that this enhanced charge retention capability enables the device to present artificial synaptic characteristics. The results highlight the importance of the channel's ambipolarity of floating-gate free transistors on the resultant volatile memory characteristics and synaptic behaviors.

Progress of Materials and Devices for Neuromorphic Vision Sensors

The latest developments in bio-inspired neuromorphic vision sensors can be summarized in 3 keywords: smaller, faster, and smarter. (1) Smaller: Devices are becoming more compact by integrating previously separated components such as sensors, memory, and processing units. As a prime example, the transition from traditional sensory vision computing to in-sensor vision computing has shown clear benefits, such as simpler circuitry, lower power consumption, and less data redundancy. (2) Swifter: Owing to the nature of physics, smaller and more integrated devices can detect, process, and react to input more quickly. In addition, the methods for sensing and processing optical information using various materials (such as oxide semiconductors) are evolving. (3) Smarter: Owing to these two main research directions, we can expect advanced applications such as adaptive vision sensors, collision sensors, and nociceptive sensors. This review mainly focuses on the recent progress, working mechanisms, image pre-processing techniques, and advanced features of two types of neuromorphic vision sensors based on near-sensor and in-sensor vision computing methodologies.

A Flexible Artificial Sensory Nerve Enabled by Nanoparticle-Assembled Synaptic Devices for Neuromorphic Tactile Recognition

Tactile perception enabled by somatosensory system in human is essential for dexterous tool usage, communication, and interaction. Imparting tactile recognition functions to advanced robots and interactive systems can potentially improve their cognition and intelligence. Here, a flexible artificial sensory nerve that mimics the tactile sensing, neural coding, and synaptic processing functions in human sensory nerve is developed to achieve neuromorphic tactile recognition at device level without relying on algorithms or computing resources. An interfacial self-assembly technique, which produces uniform and defect-less thin film of semiconductor nanoparticles on arbitrary substrates, is employed to prepare the flexible synaptic device. The neural facilitation and sensory memory characteristics of the proton-gating synaptic device enable this system to identify material hardness during robotic grasping and recognize tapping patterns during tactile interaction in a continuous, real-time, high-accuracy manner, demonstrating neuromorphic intelligence and recognition capabilities. This artificial sensory nerve produced in wearable and portable form can be readily integrated with advanced robots and smart human-machine interfaces for implementing human-like tactile cognition in neuromorphic electronics toward robotic and wearable applications.

Sputtered Electrolyte-Gated Transistor with Temperature-Modulated Synaptic Plasticity Behaviors

Temperature has always been considered as an essential factor for almost all kinds of semiconductor-based electronic components. In this work, temperature-dependent synaptic plasticity behaviors, which are mimicked by the indium-gallium-zinc oxide thin-film transistors gated with sputtered SiO2 electrolytes, have been studied. With the temperature increasing from 303 to 323 K, the electrolyte capacitance decreases from 0.42 to 0.11 μF cm-2. The mobility increases from 1.4 to 3.7 cm2 V-1 s-1, and the threshold voltage negatively shifts from -0.23 to -0.51 V. Synaptic behaviors under both a single pulse and multiple pulses are employed to study the temperature dependence. With the temperature increasing from 303 to 323 K, the post-synaptic current (PSC) at the resting state increases from 1.8 to 7.3 μA. Under a single gate pulse of 1 V and 1 s, the PSC signal altitude and the PSC retention time decrease from 2.0 to 0.7 μA and 5.1 × 102 to 2.5 ms, respectively. A physical model based on the electric field-induced ion drifting, ionic-electronic coupling, and gradient-coordinated ion diffusion is proposed to understand these temperature-dependent synaptic behaviors. Based on the experimental data on individual transistors, temperature-modulated pattern learning and memorizing behaviors are conceptually demonstrated. The in-depth investigation of the temperature dependence helps pave the way for further electrolyte-gated transistor-based neuromorphic applications.

Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications

Electrospun nanofibers have become the most promising building blocks for future high-performance electronic devices because of the advantages of larger specific surface area, higher porosity, more flexibility, and stronger mechanical strength over conventional film-based materials. Moreover, along with the properties of ease of fabrication and cost-effectiveness, a broad range of applications based on nanomaterials by electrospinning have sprung up. In this review, we aim to summarize basic principles, influence factors, and advanced methods of electrospinning to produce hundreds of nanofibers with different structures and arrangements. In addition, electrospun nanofiber based electronics composed of both two-terminal and three-terminal devices and their practical applications are discussed in the fields of sensing, storage, and computing, which give rise to the further integration to realize a comprehensive and brain-like system. Last but not least, the emulation of biological synapses through artificial synaptic transistors and additionally optoelectronics in recent years are included as an important step toward the construction of large-scale, multifunctional systems.

Emulation of Synaptic Plasticity on a Cobalt-Based Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing (NC), which emulates neural activities of the human brain, is considered for the low-power implementation of artificial intelligence. Toward realizing NC, fabrication, and investigations of hardware elements─such as synaptic devices and neurons─are crucial. Electrolyte gating has been widely used for conductance modulation by massive carrier injections and has proven to be an effective way of emulating biological synapses. Synaptic devices, in the form of synaptic transistors, have been studied using various materials. Despite the remarkable progress, the study of metallic channel-based synaptic transistors remains massively unexplored. Here, we demonstrated a three-terminal electrolyte gating-modulated synaptic transistor based on a metallic cobalt thin film to emulate biological synapses. We have realized gating-controlled, non-volatile, and distinct multilevel conductance states in the proposed device. The essential synaptic functions demonstrating both short-term and long-term plasticity have been emulated in the synaptic device. A transition from short-term to long-term memory has been realized by tuning the gate pulse parameters, such as amplitude and duration. The crucial cognitive behavior, including learning, forgetting, and re-learning, has been emulated, showing a resemblance to the human brain. Beyond that, dynamic filtering behavior has been experimentally implemented in the synaptic device. These results provide an insight into the design of metallic channel-based synaptic transistors for NC.

Electrolyte-Gated Vertical Synapse Array based on Van Der Waals Heterostructure for Parallel Computing

Recently, three-terminal synaptic devices, which separate read and write terminals, have attracted significant attention because they enable nondestructive read-out and parallel-access for updating synaptic weights. However, owing to their structural features, it is difficult to address the relatively high device density compared with two-terminal synaptic devices. In this study, a vertical synaptic device featuring remotely controllable weight updates via e-field-dependent movement of mobile ions in the ion-gel layer is developed. This synaptic device successfully demonstrates all essential synaptic characteristics, such as excitatory/inhibitory postsynaptic current (E/IPSC), paired-pulse facilitation (PPF), and long-term potentiation/depression (LTP/D) by electrical measurements, and exhibits competitive LTP/D characteristics with a dynamic range (G_max /G_min ) of 31.3, and asymmetry (AS) of 8.56. The stability of the LTP/D characteristics is also verified through repeated measurements over 50 cycles; the relative standard deviations (RSDs) of G_max /G_min and AS are calculated as 1.65% and 0.25%, respectively. These excellent synaptic properties enable a recognition rate of ≈99% in the training and inference tasks for acoustic and emotional information patterns. This study is expected to be an important foundation for the realization of future parallel computing networks for energy-efficient and high-speed data processing.

Triboelectric Nanogenerators as Active Tactile Stimulators for Multifunctional Sensing and Artificial Synapses

The wearable tactile sensors have attracted great attention in the fields of intelligent robots, healthcare monitors and human-machine interactions. To create active tactile sensors that can directly generate electrical signals in response to stimuli from the surrounding environment is of great significance. Triboelectric nanogenerators (TENGs) have the advantages of high sensitivity, fast response speed and low cost that can convert any type of mechanical motion in the surrounding environment into electrical signals, which provides an effective strategy to design the self-powered active tactile sensors. Here, an overview of the development in TENGs as tactile stimulators for multifunctional sensing and artificial synapses is systematically introduced. Firstly, the applications of TENGs as tactile stimulators in pressure, temperature, proximity sensing, and object recognition are introduced in detail. Then, the research progress of TENGs as tactile stimulators for artificial synapses is emphatically introduced, which is mainly reflected in the electrolyte-gate synaptic transistors, optoelectronic synaptic transistors, floating-gate synaptic transistors, reduced graphene oxides-based artificial synapse, and integrated circuit-based artificial synapse and nervous systems. Finally, the challenges of TENGs as tactile stimulators for multifunctional sensing and artificial synapses in practical applications are summarized, and the future development prospects are expected.

In-Materio Reservoir Computing in a Sulfonated Polyaniline Network

A sulfonated polyaniline (SPAN) organic electrochemical network device (OEND) is fabricated using a simple drop-casting method on multiple Au electrodes for use in reservoir computing (RC). The SPAN network has humidity-dependent electrical properties. Under high humidity, the SPAN OEND exhibits mainly ionic conduction, including charging of an electric double layer and ionic diffusion. The nonlinearity and hysteresis of the current-voltage characteristics progressively increase with increasing humidity. The rich dynamic output behavior indicates wide variations for each electrode, which improves the RC performance because of the disordered network. For RC, waveform generation and short-term memory tasks are realized by a linear combination of outputs. The waveform task accuracy and memory capacity calculated from a short-term memory task reach 90% and 33.9, respectively. Improved spoken-digit classification is realized with 60% accuracy by only 12 outputs, demonstrating that the SPAN OEND can manage time series dynamic data operation in RC owing to a combination of rich dynamic and nonlinear electronic properties. The results suggest that SPAN-based electrochemical systems can be applied for material-based computing, by exploiting their intrinsic physicochemical behavior.

Realization of an Artificial Visual Nervous System using an Integrated Optoelectronic Device Array

Human behavior (e.g., the response to any incoming information) has very complex forms and is based on the response to consecutive external stimuli entering varied sensory receptors. Sensory adaptation is an elementary form of the sensory nervous system known to filter out irrelevant information for efficient information transfer from consecutive stimuli. As bioinspired neuromorphic electronic system is developed, the functionality of organs shall be emulated at a higher level than the cell. Because it is important for electronic devices to possess sensory adaptation in spiking neural networks, the authors demonstrate a dynamic, real-time, photoadaptation process to optical irradiation when repeated light stimuli are presented to the artificial photoreceptor. The filtered electrical signal generated by the light and the adapting signal produces a specific range of postsynaptic states through the neurotransistor, demonstrating changes in the response according to the environment, as normally perceived by the human brain. This successfully demonstrates plausible biological sensory adaptation. Further, the ability of this circuit design to accommodate changes in the intensity of bright or dark light by adjusting the sensitivity of the artificial photoreceptor is demonstrated. Thus, the proposed artificial photoreceptor circuits have the potential to advance neuromorphic device technology by providing sensory adaptation capabilities.

Self-powered bifunctional sensor based on tribotronic planar graphene transistors

With the development of material science, micro-nano-fabrication and microelectronics, the higher level requirements are posed on the electronic skins (E-skin). The lower energy consumption and multiple functions are the imperative requirements to spurred scientists and mechanists to make joint efforts to meet. To achieve lower energy consumption, a promising energy-harvesting style of triboelectric nanogenerators (TENG) is incorporated into the field effect transistors (FETs) to play the important role for sensor. For bifunctional sensor, to harness the difficult for reflecting the magnitude of frequency, we resorted to synaptic transistors to achieve more intelligentization. Furthermore, with regards to the configuration of FET, we continued previous work: using the electrolyte gate dielectrics of FET-ion gel as the electrification layer to achieve high efficient, compact and extensively adoption for mechanosensation. The working principle of the GFET was based on the coupling effects of the FET and the TENG. This newly emerged self-powered sensor would offer a new platform for lower power consumption sensor for human-machine interface and intelligent robot.

Effect of Oxygen Vacancy on the Conduction Modulation Linearity and Classification Accuracy of Pr0.7Ca0.3MnO3 Memristor

An amorphous Pr_0.7Ca_0.3MnO₃ (PCMO) film was grown on a TiN/SiO₂/Si (TiN–Si) substrate at 300 °C and at an oxygen pressure (OP) of 100 mTorr. This PCMO memristor showed typical bipolar switching characteristics, which were attributed to the generation and disruption of oxygen vacancy (OV) filaments. Fabrication of the PCMO memristor at a high OP resulted in nonlinear conduction modulation with the application of equivalent pulses. However, the memristor fabricated at a low OP of 100 mTorr exhibited linear conduction modulation. The linearity of this memristor improved because the growth and disruption of the OV filaments were mostly determined by the redox reaction of OV owing to the presence of numerous OVs in this PCMO film. Furthermore, simulation using a convolutional neural network revealed that this PCMO memristor has enhanced classification performance owing to its linear conduction modulation. This memristor also exhibited several biological synaptic characteristics, indicating that an amorphous PCMO thin film fabricated at a low OP would be a suitable candidate for artificial synapses.

Enhancement of Synaptic Characteristics Achieved by the Optimization of Proton-Electron Coupling Effect in a Solid-State Electrolyte-Gated Transistor

Presently, the 3-terminal artificial synapse device has been in focus for neuromorphic computing systems owing to its excellent weight controllability. Here, an artificial synapse device based on the 3-terminal solid-state electrolyte-gated transistor is proposed to achieve outstanding synaptic characteristics with a human-like mechanism at low power. Novel synaptic characteristics are accomplished by precisely tuning the threshold voltage using the proton-electron coupling effect, which is caused by proton migration inside the electrolyte. However, these synaptic characteristics are degraded because traps at the interface of channel/electrolyte disturb the proton-electron coupling effect. To minimize degradation, the oxygen plasma treatment is performed to reduce interface traps. As a result, symmetric weight updates and outstanding synaptic characteristics are achieved. Furthermore, high repeatability and long-term plasticity are observed at low operating power, which is essential for artificial synapses. Therefore, this study shows the progress of artificial synapses and proposes a promising method, a low-power neuromorphic system, to achieve high accuracy.

Artificial Skin Perception

Skin is the largest organ, with the functionalities of protection, regulation, and sensation. The emulation of human skin via flexible and stretchable electronics gives rise to electronic skin (e-skin), which has realized artificial sensation and other functions that cannot be achieved by conventional electronics. To date, tremendous progress has been made in data acquisition and transmission for e-skin systems, while the implementation of perception within systems, that is, sensory data processing, is still in its infancy. Integrating the perception functionality into a flexible and stretchable sensing system, namely artificial skin perception, is critical to endow current e-skin systems with higher intelligence. Here, recent progress in the design and fabrication of artificial skin perception devices and systems is summarized, and challenges and prospects are discussed. The strategies for implementing artificial skin perception utilize either conventional silicon-based circuits or novel flexible computing devices such as memristive devices and synaptic transistors, which enable artificial skin to surpass human skin, with a distributed, low-latency, and energy-efficient information-processing ability. In future, artificial skin perception would be a new enabling technology to construct next-generation intelligent electronic devices and systems for advanced applications, such as robotic surgery, rehabilitation, and prosthetics.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

Degradable Photonic Synaptic Transistors Based on Natural Biomaterials and Carbon Nanotubes

Artificial synaptic devices have potential for overcoming the bottleneck of von Neumann architecture and building artificial brain-like computers. Up to now, developing synaptic devices by utilizing biocompatible and biodegradable materials in electronic devices has been an interesting research direction due to the requirements of sustainable development. Here, a degradable photonic synaptic device is reported by combining biomaterials chlorophyll-a and single-walled carbon nanotubes (SWCNTs). Several basic synaptic functions, including excitatory postsynaptic current (EPSC), paired pulse facilitation (PPF), transition from short-term memory (STM) to long-term memory (LTM), and learning and forgetting behaviors, are successfully emulated through the chlorophyll-a/SWCNTs synaptic device. Furthermore, decent synaptic behaviors can still be achieved at a low drain voltage of -0.0001 V, which results in quite low energy consumption of 17.5 fJ per pulse. Finally, the degradability of this chlorophyll-a/SWCNTs transistor array is demonstrated, indicating that the device can be environmentally friendly. This work provides a new guide to the development of next-generation green and degradable neuromorphic computing electronics.

Artificial visual systems enabled by quasi-two-dimensional electron gases in oxide superlattice nanowires

Rapid development of artificial intelligence techniques ignites the emerging demand on accurate perception and understanding of optical signals from external environments via brain-like visual systems. Here, enabled by quasi-two-dimensional electron gases (quasi-2DEGs) in InGaO₃(ZnO)₃ superlattice nanowires (NWs), an artificial visual system was built to mimic the human ones. This system is based on an unreported device concept combining coexistence of oxygen adsorption-desorption kinetics on NW surface and strong carrier quantum-confinement effects in superlattice core, to resemble the biological Ca²⁺ ion flux and neurotransmitter release dynamics. Given outstanding mobility and sensitivity of superlattice NWs, an ultralow energy consumption down to subfemtojoule per synaptic event is realized in quasi-2DEG synapses, which rivals that of biological synapses and now available synapse-inspired electronics. A flexible quasi-2DEG artificial visual system is demonstrated to simultaneously perform high-performance light detection, brain-like information processing, nonvolatile charge retention, in situ multibit-level memory, orientation selectivity, and image memorizing.

Perovskite/Organic Semiconductor-Based Photonic Synaptic Transistor for Artificial Visual System

Artificial visual system with information sensing, processing, and memory function is promoting the development of artificial intelligence techniques. Photonic synapse as an essential component can enhance the visual information processing efficiency owing to the high propagation speed, low latency, and large bandwidth. Herein, photonic synaptic transistors based on organic semiconductor poly[2,5-(2-octyldodecyl)-3,6-diketopyrrolopyrrole-alt-5,5-(2,5-di(thien-2-yl)thieno [3,2-b]thiophene)] (DPPDTT) and perovskite CsPbBr₃ quantum dots are fabricated by a simple solution process. The device can simulate fundamental synaptic behaviors, including excitatory postsynaptic current, pair-pulse facilitation, the transition of short-term memory to long-term memory, and "learning experience" behavior. Combining the advantages of the high photosensitivity of perovskites and relatively high conductivity of DPPDTT, the device can exhibit excellent synaptic performances at a low voltage of -0.2 V. Even under an ultralow operation voltage of -0.0005 V, the device can still show obvious synaptic responses. Tunable synaptic integration behaviors including "AND" and "OR" light logic functions can be realized. An artificial visual system is successfully emulated by illuminating the synaptic arrays employing light of different densities. Therefore, low-voltage synaptic devices based on organic semiconductor and CsPbBr₃ quantum dots with a simple fabrication technique present high potential to mimic human visual memory.

Solution-Processable Anion-doped Conjugated Polymer for Nonvolatile Organic Transistor Memory with Synaptic Behaviors

Brain-inspired synaptic transistors have been considered as a promising device for next-generation electronics. To mimic the behavior of a biological synapse, both data processing and nonvolatile memory capability are simultaneously required for a single electronic device. In this work, a simple approach to realize a synaptic transistor with improved memory characteristics is demonstrated by doping an ionic additive, tetrabutylammonium perchlorate (TBAP), into an active polymer semiconductor without using any extra charge storage layer. TBAP doping is first revealed to improve the memory window of a derived transistor memory device from 19 to 32 V (∼68% enhancement) with an on/off current ratio over 10³ at V_G = -10 V. Through morphological analysis and theoretical calculations, it is revealed that the association of anion with polymers enhances the charge retention capability of the polymer and facilitates the interchain interactions to result in improved memory characteristics. More critically, the doped device is shown to successfully mimic the synaptic behaviors, such as paired-pulse facilitation (PPF), excitatory and inhibitory postsynaptic currents, and spike-rate dependent plasticity. Notably, the TBAP-doped device is shown to deliver a PPF index of up to 204% in contrast to the negligible value of an undoped device. This study describes a novel approach to prepare a synaptic transistor by doping conjugated polymers, which can promote the future development of artificial neuromorphic systems.

Ultralow Power Wearable Heterosynapse with Photoelectric Synergistic Modulation

Although the energy consumption of reported neuromorphic computing devices inspired by biological systems has become lower than traditional memory, it still remains greater than bio-synapses (≈10 fJ per spike). Herein, a flexible MoS2-based heterosynapse is designed with two modulation modes, an electronic mode and a photoexcited mode. A one-step mechanical exfoliation method on flexible substrate and low-temperature atomic layer deposition process compatible with flexible electronics are developed for fabricating wearable heterosynapses. With a pre-spike of 100 ns, the synaptic device exhibits ultralow energy consumption of 18.3 aJ per spike in long-term potentiation and 28.9 aJ per spike in long-term depression. The ultrafast speed and ultralow power consumption provide a path for a neuromorphic computing system owning more excellent processing ability than the human brain. By adding optical modulation, a modulatory synapse is constructed to dynamically control correlations between pre- and post-synapses and realize complex global neuromodulations. The novel wearable heterosynapse expands the accessible range of synaptic weights (ratio of facilitation ≈228%), providing an insight into the application of wearable 2D highly efficient neuromorphic computing architectures.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

Silver Nanorings Fabricated by Glycerol-Based Cosolvent Polyol Method

The urgent demand for transparent flexible electrodes applied in wide bandgap devices has promoted the development of new materials. Silver nanoring (AgNR), known as a special structure of silver nanowire (AgNW), exhibits attractive potential in the field of wearable electronics. In this work, an environmentally friendly glycerol-based cosolvent polyol method was investigated. The Taguchi design was utilized to ascertain the factors that affect the yield and ring diameter of AgNRs. Structural characterization showed that AgNR seeds grew at a certain angle during the early nucleation period. The results indicated that the yield and ring diameter of AgNRs were significantly affected by the ratio of cosolvent. Besides, the ring diameter of AgNRs was also tightly related to the concentration of polyvinylpyrrolidone (PVP). The difference of reducibility between glycerol, water, and ethylene glycol leads to the selective growth of (111) plane and is probably the main reason AgNRs are formed. As a result, AgNRs with a ring diameter range from 7.17 to 42.94 μm were synthesized, and the quantity was increased significantly under the optimal level of factors.

Threshold-Tunable, Spike-Rate-Dependent Plasticity Originating from Interfacial Proton Gating for Pattern Learning and Memory

Recently, neuromorphic devices have been receiving increasing interest in the field of artificial intelligence (AI). Realization of fundamental synaptic plasticities on hard-ware devices would endow new intensions for neuromorphic devices. Spike-rate-dependent plasticity (SRDP) is one of the most important synaptic learning mechanisms in brain cognitive behaviors. It is thus interesting to mimic the SRDP behaviors on solid-state neuromorphic devices. In the present work, nanogranular phosphorus silicate glass (PSG)-based proton conductive electrolyte-gated oxide neuromorphic transistors have been proposed. The oxide neuromorphic transistors have good transistor performances and frequency-dependent synaptic plasticity behavior. Moreover, the neuromorphic transistor exhibits SRDP activities. Interestingly, by introducing priming synaptic stimuli, the modulation of threshold frequency value distinguishing synaptic potentiation from synaptic depression is realized for the first time on an electrolyte-gated neuromorphic transistor. Such a mechanism can be well understood with interfacial proton gating effects of the nanogranular PSG-based electrolyte. Furthermore, the effects of SRDP learning rules on pattern learning and memory behaviors have been conceptually demonstrated. The proposed neuromorphic transistors have potential applications in neuromorphic engineering.

Artificial 2D van der Waals Synapse Devices via Interfacial Engineering for Neuromorphic Systems

Despite extensive investigations of a wide variety of artificial synapse devices aimed at realizing a neuromorphic hardware system, the identification of a physical parameter that modulates synaptic plasticity is still required. In this context, a novel two-dimensional architecture consisting of a NbSe₂/WSe₂/Nb₂O₅ heterostructure placed on an SiO₂/p+ Si substrate was designed to overcome the limitations of the conventional silicon-based complementary metal-oxide semiconductor technology. NbSe₂, WSe₂, and Nb₂O₅ were used as the metal electrode, active channel, and conductance-modulating layer, respectively. Interestingly, it was found that the post-synaptic current was successfully modulated by the thickness of the interlayer Nb₂O₅, with a thicker interlayer inducing a higher synapse spike current and a stronger interaction in the sequential pulse mode. Introduction of the Nb₂O₅ interlayer can facilitate the realization of reliable and controllable synaptic devices for brain-inspired integrated neuromorphic systems.

Oxide Neuromorphic Transistors Gated by Polyvinyl Alcohol Solid Electrolytes with Ultralow Power Consumption

Neuromorphic devices and systems with ultralow power consumption are important in building artificial intelligent systems. Here, indium tin oxide (ITO)-based oxide neuromorphic transistors are fabricated using poly(vinyl alcohol) (PVA)-based proton-conducting electrolytes as gate dielectrics. The electrical performances of the transistors can be modulated with the ITO channel thickness. Fundamental synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, and multistore memory, are successfully emulated. Most importantly, the PVA-gated neuromorphic devices demonstrate ultralow energy consumption of ∼1.16 fJ with ultrahigh sensitivity of ∼5.4 dB, as is very important for neuromorphic engineering applications. Because of the inherent environmental-friendly characteristics of PVA, the devices possess security biocompatibility. Thus, the proposed PVA-gated oxide neuromorphic transistors may find potential applications in "green" ultrasensitive neuromorphic systems and efficient electronic biological interfaces.

Recent Advances in Electric-Double-Layer Transistors for Bio-Chemical Sensing Applications

As promising biochemical sensors, ion-sensitive field-effect transistors (ISFETs) are used widely in the growing field of biochemical sensing applications. Recently, a new type of field-effect transistor gated by ionic electrolytes has attracted intense attention due to the extremely strong electric-double-layer (EDL) gating effect. In such devices, the carrier density of the semiconductor channel can be effectively modulated by an ion-induced EDL capacitance at the semiconductor/electrolyte interface. With advantages of large specific capacitance, low operating voltage and sensitive interfacial properties, various EDL-based transistor (EDLT) devices have been developed for ultrasensitive portable sensing applications. In this article, we will review the recent progress of EDLT-based biochemical sensors. Starting with a brief introduction of the concepts of EDL capacitance and EDLT, we describe the material compositions and the working principle of EDLT devices. Moreover, the biochemical sensing performances of several important EDLTs are discussed in detail, including organic-based EDLTs, oxide-based EDLTs, nanomaterial-based EDLTs and neuromorphic EDLTs. Finally, the main challenges and development prospects of EDLT-based biochemical sensors are listed.

Solution-processed metal-oxide thin-film transistors: a review of recent developments

Driven by the rapid development of novel active-matrix displays, thin-film transistors (TFTs) based on metal-oxide (MO) semiconductors have drawn great attention during recent years. N-type MO TFTs manufactured through vacuum-based processes have the advantages of higher mobility compared to the amorphous silicon TFTs, better uniformity and lower processing temperature compared to the polysilicon TFTs, and visible light transparency which is suitable for transparent electronic devices, etc. However, the fabrication cost is high owing to the expensive and complicated vacuum-based systems. In contrast, solution process has the advantages of low cost, high throughput, and easy chemical composition control. In the first part of this review, a brief introduction of solution-processed MO TFTs is given, and the main issues and challenges encountered in this field are discussed. The recent advances in channel layer engineering to obtain the state-of-the-art solution-processed MO TFTs are reviewed and summarized. Afterward, a detailed discussion of the direct patterning methods is presented, including the direct photopatterning and printing techniques. Next, the effect of gate dielectric materials and their interfaces on the performance of the resulting TFTs are surveyed. The last topic is the various applications of solution-processed MO TFTs, from novel displays to sensing, memory devices, etc. Finally, conclusions are drawn and future expectations for solution-processed MO TFTs and their applications are described.

Recent Progress in Three-Terminal Artificial Synapses: From Device to System

Synapses are essential to the transmission of nervous signals. Synaptic plasticity allows changes in synaptic strength that make a brain capable of learning from experience. During development of neuromorphic electronics, great efforts have been made to design and fabricate electronic devices that emulate synapses. Three-terminal artificial synapses have the merits of concurrently transmitting signals and learning. Inorganic and organic electronic synapses have mimicked plasticity and learning. Optoelectronic synapses and photonic synapses have the prospective benefits of low electrical energy loss, high bandwidth, and mechanical robustness. These artificial synapses provide new opportunities for the development of neuromorphic systems that can use parallel processing to manipulate datasets in real time. Synaptic devices have also been used to build artificial sensory systems. Here, recent progress in the development and application of three-terminal artificial synapses and artificial sensory systems is reviewed.

Field-Driven Athermal Activation of Amorphous Metal Oxide Semiconductors for Flexible Programmable Logic Circuits and Neuromorphic Electronics

Despite extensive research, large-scale realization of metal-oxide electronics is still impeded by high-temperature fabrication, incompatible with flexible substrates. Ideally, an athermal treatment modifying the electronic structure of amorphous metal oxide semiconductors (AMOS) to generate sufficient carrier concentration would help mitigate such high-temperature requirements, enabling realization of high-performance electronics on flexible substrates. Here, a novel field-driven athermal activation of AMOS channels is demonstrated via an electrolyte-gating approach. Facilitating migration of charged oxygen species across the semiconductor-dielectric interface, this approach modulates the local electronic structure of the channel, generating sufficient carriers for charge transport and activating oxygen-compensated thin films. The thin-film transistors (TFTs) investigated here depict an enhancement of linear mobility from 51 to 105.25 cm² V^-1 s^-1 (ionic-gated) and from 8.09 to 14.49 cm² V^-1 s^-1 (back-gated), by creating additional oxygen vacancies. The accompanying stochiometric transformations, monitored via spectroscopic measurements (X-ray photoelectron spectroscopy) corroborate the detailed electrical (TFT, current evolution) parameter analyses, providing critical insights into the underlying oxygen-vacancy generation mechanism and clearly demonstrating field-induced activation as a promising alternative to conventional high-temperature annealing strategies. Facilitating on-demand active programing of the operation modes of transistors (enhancement vs depletion), this technique paves way for facile fabrication of logic circuits and neuromorphic transistors for bioinspired computing.

A Ferrite Synaptic Transistor with Topotactic Transformation

Hardware implementation of artificial synaptic devices that emulate the functions of biological synapses is inspired by the biological neuromorphic system and has drawn considerable interest. Here, a three-terminal ferrite synaptic device based on a topotactic phase transition between crystalline phases is presented. The electrolyte-gating-controlled topotactic phase transformation between brownmillerite SrFeO_2.5 and perovskite SrFeO_{3- δ} is confirmed from the examination of the crystal and electronic structure. A synaptic transistor with electrolyte-gated ferrite films by harnessing gate-controllable multilevel conduction states, which originate from many distinct oxygen-deficient perovskite structures of SrFeO_x induced by topotactic phase transformation, is successfully constructed. This three-terminal artificial synapse can mimic important synaptic functions, such as synaptic plasticity and spike-timing-dependent plasticity. Simulations of a neural network consisting of ferrite synaptic transistors indicate that the system offers high classification accuracy. These results provide insight into the potential application of advanced topotactic phase transformation materials for designing artificial synapses with high performance.

Atomic Layer Deposited Hf0.5Zr0.5O2-based Flexible Memristor with Short/Long-Term Synaptic Plasticity

Artificial synapses are the fundamental of building a neuron network for neuromorphic computing to overcome the bottleneck of the von Neumann system. Based on a low-temperature atomic layer deposition process, a flexible electrical synapse was proposed and showed bipolar resistive switching characteristics. With the formation and rupture of ions conductive filaments path, the conductance was modulated gradually. Under a series of pre-synaptic spikes, the device successfully emulated remarkable short-term plasticity, long-term plasticity, and forgetting behaviors. Therefore, memory and learning ability were integrated to the single flexible memristor, which are promising for the next-generation of artificial neuromorphic computing systems.

Light-Stimulated Synaptic Transistors Fabricated by a Facile Solution Process Based on Inorganic Perovskite Quantum Dots and Organic Semiconductors

Implementation of artificial intelligent systems with light-stimulated synaptic emulators may enhance computational speed by providing devices with high bandwidth, low power computation requirements, and low crosstalk. One of the key challenges is to develop light-stimulated devices that can response to light signals in a neuron-/synapse-like fashion. A simple and effective solution process to fabricate light-stimulated synaptic transistors (LSSTs) based on inorganic halide perovskite quantum dots (IHP QDs) and organic semiconductors (OSCs) is reported. Blending IHP QDs and OSCs not only improves the charge separation efficiency of the photoexcited charges, but also induces delayed decay of the photocurrent in the IHP QDs/OSCs hybrid film. The enhanced charge separation efficiency results in high photoresponsivity, while the induced delayed decay of the photocurrent is critical to achieving light-stimulating devices with a memory effect, which are important for achieving high synaptic performance. The LSSTs can respond to light signals in a highly neuron-/synapse-like fashion. Both short-term and long-term synaptic behaviors have been realized, which may lay the foundation for the future implementation of artificial intelligent systems that are enabled by light signals. More significantly, LSSTs are fabricated by a facile solution process which can be easily applied to large-scale samples.

Ionotronic Halide Perovskite Drift-Diffusive Synapses for Low-Power Neuromorphic Computation

Emulation of brain-like signal processing is the foundation for development of efficient learning circuitry, but few devices offer the tunable conductance range necessary for mimicking spatiotemporal plasticity in biological synapses. An ionic semiconductor which couples electronic transitions with drift-diffusive ionic kinetics would enable energy-efficient analog-like switching of metastable conductance states. Here, ionic-electronic coupling in halide perovskite semiconductors is utilized to create memristive synapses with a dynamic continuous transition of conductance states. Coexistence of carrier injection barriers and ion migration in the perovskite films defines the degree of synaptic plasticity, more notable for the larger organic ammonium and formamidinium cations than the inorganic cesium counterpart. Optimized pulsing schemes facilitates a balanced interplay of short- and long-term plasticity rules like paired-pulse facilitation and spike-time-dependent plasticity, cardinal for learning and computing. Trained as a memory array, halide perovskite synapses demonstrate reconfigurability, learning, forgetting, and fault tolerance analogous to the human brain. Network-level simulations of unsupervised learning of handwritten digit images utilizing experimentally derived device parameters, validates the utility of these memristors for energy-efficient neuromorphic computation, paving way for novel ionotronic neuromorphic architectures with halide perovskites as the active material.

Ultralow Power Dual-Gated Subthreshold Oxide Neuristors: An Enabler for Higher Order Neuronal Temporal Correlations

Inspired by neural computing, the pursuit of ultralow power neuromorphic architectures with highly distributed memory and parallel processing capability has recently gained more traction. However, emulation of biological signal processing via artificial neuromorphic architectures does not exploit the immense interplay between local activities and global neuromodulations observed in biological neural networks and hence are unable to mimic complex biologically plausible adaptive functions like heterosynaptic plasticity and homeostasis. Here, we demonstrate emulation of complex neuronal behaviors like heterosynaptic plasticity, homeostasis, association, correlation, and coincidence in a single neuristor via a dual-gated architecture. This multiple gating approach allows one gate to capture the effect of local activity correlations and the second gate to represent global neuromodulations, allowing additional modulations which augment their plasticity, enabling higher order temporal correlations at a unitary level. Moreover, the dual-gate operation extends the available dynamic range of synaptic conductance while maintaining symmetry in the weight-update operation, expanding the number of accessible memory states. Finally, operating neuristors in the subthreshold regime enable synaptic weight changes with high gain while maintaining ultralow power consumption of the order of femto-Joules.

Wood-Derived Nanopaper Dielectrics for Organic Synaptic Transistors

The use of biocompatible and biodegradable materials in electronic devices can be an important trend in the development of the next-generation green electronics. In addition, by integrating the advantages of low power consumption, low-cost processing, and flexibility, organic synaptic devices will be promising elements for the construction of brain-inspired computers. However, previously reported electrolyte-gated synaptic transistors are mainly made of non-biocompatible and non-biodegradable electrolytes. Woods are widely considered as one kind of sustainable and renewable materials. We found that wood-derived cellulose nanopapers have ionic conductivity and, therefore, can be used as dielectric materials for organic synaptic transistors. The fabricated wood-derived cellulose nanopapers exhibit decent ionic conductivity of 7.3 × 10^-4 S m^-1 and a high lateral coupling effective capacitance of 18.65 nF cm^-2 at 30 Hz. The laterally coupled organic synaptic transistors using wood-derived cellulose nanopapers as the dielectric layer present excellent transistor performances at the operating voltage below 1.5 V. More significantly, some important synaptic behaviors, such as excitatory postsynaptic current, signal-filtering characteristics, and dendritic integration are successfully simulated in our synaptic transistors. Because the development of electronic devices with biocompatible and biodegradable materials is essential, this work may inspire new directions for the development of "green" neuromorphic electronics.

Indium Tungsten Oxide Thin Films for Flexible High-Performance Transistors and Neuromorphic Electronics

Thin-film transistors (TFTs) with high electrical performances (mobility > 10 cm²/V s, V_th < 1 V, SS < 1 V/decade, on/off ratio ≈ 10⁶) obtained from the silicon- and oxide-based single-crystalline semiconductor materials require high processing temperature and hence are not suitable for flexible electronics. Amorphous oxide-based transparent electronic devices are attractive to meet emerging technological demands where crystalline oxide-/silicon-based architectures cannot provide a solution. Here, we tackle this problem by using a novel amorphous oxide semiconducting material-namely, indium tungsten oxide (IWO)-as the active channel in flexible TFTs (FTFTs). Post-annealing temperature as low as 270 °C for amorphous IWO thin films deposited by radio frequency sputtering at room temperature could result in smooth morphology ( R_rms ≈ 0.42 nm), good adhesion, and high carrier density ( n ≈ 7.19 × 10¹⁸ cm^-3). Excellent TFT characteristics of flexible devices could be achieved with linear field effect mobility μ_FE ≈ 25.86 cm²/V s, subthreshold swing SS ≈ 0.30 V/decade, threshold voltage V_th ≈ -1.5 V, and on/off ratio I_on/ I_off ≈ 5.6 × 10⁵ at 3 V and stable operation during bending of the FTFT. Additionally, IWO TFTs were implemented as synapses, the building block for neuromorphic computing. Paired-pulse facilitation up to 138% was observed and showed an exponential decay resembling chemical synapses. Utilizing this characteristic, a high-pass dynamic temporal filter was devised providing increased gain from 1.55 to 21 when frequency was raised from 22 to 62 Hz. The high performance and stability of flexible TFTs obtained with IWO films demonstrate their promise for low-voltage electronic applications.

Transient security transistors self-supported on biodegradable natural-polymer membranes for brain-inspired neuromorphic applications

Transient electronics, a new generation of electronics that can physically or functionally vanish on demand, are very promising for future "green" security biocompatible electronics. At the same time, hardware implementation of biological synapses is highly desirable for emerging brain-like neuromorphic computational systems that could look beyond the conventional von Neumann architecture. Here, a hardware-security physically-transient bidirectional artificial synapse network based on a dual in-plane-gate Al-Zn-O neuromorphic transistor was fabricated on free-standing laterally-coupled biopolymer electrolyte membranes (sodium alginate). The excitatory postsynaptic current, paired-pulse-facilitation, and temporal filtering characteristics from high-pass to low-pass transition were successfully mimicked. More importantly, bidirectional dynamic spatiotemporal learning rules and neuronal arithmetic were also experimentally demonstrated using two lateral in-plane gates as the presynaptic inputs. Most interestingly, excellent physically-transient behavior could be achieved with a superfast water-soluble speed of only ∼120 seconds. This work represents a significant step towards future hardware-security transient biocompatible intelligent electronic systems.

Flexible Neuromorphic Architectures Based on Self-Supported Multiterminal Organic Transistors

Because of the fast expansion of artificial intelligence, development and applications of neuromorphic systems attract extensive interest. In this paper, a highly interconnected neuromorphic architecture (HINA) based on flexible self-supported multiterminal organic transistors is proposed. Au electrodes, poly(3-hexylthiophene) active channels, and ion-conducting membranes were combined to fabricate organic neuromorphic devices. Especially, freestanding ion-conducting membranes were used as gate dielectrics as well as support substrates. Basic neuromorphic behavior and four forms of spike-timing-dependent plasticity were emulated. The fabricated neuromorphic device showed excellent electrical stability and mechanical flexibility after 1000 bends. Most importantly, the device structure is interconnected in a way similar to the neural architecture of the human brain and realizes not only the structure of the multigate but also characteristics of the global gate. Dynamic processes of memorizing and forgetting were incorporated into the global gate matrix simulation. Pavlov's learning rule was also simulated by taking advantage of the multigate array. Realization of HINAs would open a new path for flexible and sophisticated neural networks.

Light-Stimulated Synaptic Devices Utilizing Interfacial Effect of Organic Field-Effect Transistors

Synaptic transistors stimulated by light waves or photons may offer advantages to the devices, such as wide bandwidth, ultrafast signal transmission, and robustness. However, previously reported light-stimulated synaptic devices generally require special photoelectric properties from the semiconductors and sophisticated device's architectures. In this work, a simple and effective strategy for fabricating light-stimulated synaptic transistors is provided by utilizing interface charge trapping effect of organic field-effect transistors (OFETs). Significantly, our devices exhibited highly synapselike behaviors, such as excitatory postsynaptic current (EPSC) and pair-pulse facilitation (PPF), and presented memory and learning ability. The EPSC decay, PPF curves, and forgetting behavior can be well expressed by mathematical equations for synaptic devices, indicating that interfacial charge trapping effect of OFETs can be utilized as a reliable strategy to realize organic light-stimulated synapses. Therefore, this work provides a simple and effective strategy for fabricating light-stimulated synaptic transistors with both memory and learning ability, which enlightens a new direction for developing neuromorphic devices.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS₂ ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.