Organic core-sheath nanowire artificial synapses with femtojoule energy consumption

ZnO nanowire optoelectronic synapse for neuromorphic computing

Artificial synapses that integrate functions of sensing, memory and computing are highly desired for developing brain-inspired neuromorphic hardware. In this work, an optoelectronic synapse based on the ZnO nanowire (NW) transistor is achieved, which can be used to emulate both the short-term and long-term synaptic plasticity. Synaptic potentiation is present when the device is stimulated by light pulses, arising from the light-induced O₂desorption and the persistent photoconductivity behavior of the ZnO NW. On the other hand, synaptic depression occurs when the device is stimulated by electrical pulses in dark, which is realized by introducing a charge trapping layer in the gate dielectric to trap carriers. Simulation of a neural network utilizing the ZnO NW synapses is carried out, demonstrating a high recognition accuracy over 90% after only 20 training epochs for recognizing the Modified National Institute of Standards and Technology digits. The present nanoscale optoelectronic synapse has great potential in the development of neuromorphic visual systems.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Flexible artificial Si-In-Zn-O/ion gel synapse and its application to sensory-neuromorphic system for sign language translation

We propose a flexible artificial synapse based on a silicon-indium-zinc-oxide (SIZO)/ion gel hybrid structure directly fabricated on a polyimide substrate, where the channel conductance is effectively modulated via ion movement in the ion gel. This synaptic operation is possible because of the low-temperature deposition process of the SIZO layer (<150°C) and the surface roughness improvement of the poly(4-vinylphenol) buffer layer (12.29→1.81 nm). The flexible synaptic device exhibits extremely stable synaptic performance under high mechanical (bending 1500 times with a radius of 5 mm) and electrical stress (application of voltage pulses 10⁴ times) without any degradation. Last, a sensory-neuromorphic system for sign language translation, which consists of stretchable resistive sensors and flexible artificial synapses, is designed and successfully evaluated via training and recognition simulation using hand sign patterns obtained by stretchable sensors (maximum recognition rate, 99.4%).

Electrolyte-gated transistors for enhanced performance bioelectronics

Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals and stably operating in aqueous environments, have emerged as fundamental building blocks in bioelectronics. In this Primer, the different EGT architectures are described with the fundamental mechanisms underpinning their functional operation, providing insight into key experiments including necessary data analysis and validation. Several organic and inorganic materials used in the EGT structures and the different fabrication approaches for an optimal experimental design are presented and compared. The functional bio-layers and/or biosystems integrated into or interfaced to EGTs, including self-organization and self-assembly strategies, are reviewed. Relevant and promising applications are discussed, including two-dimensional and three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic and neuromorphic bio-interfaces, prosthetics and robotics. Advantages, limitations and possible optimizations are also surveyed. Finally, current issues and future directions for further developments and applications are discussed.

UV light modulated synaptic behavior of MoTe2/BN heterostructure

Electrical synaptic devices are the basic components for the hardware based neuromorphic computational systems, which are expected to break the bottleneck of current von Neumann architecture. So far, synaptic devices based on three-terminal transistors are considered to provide the most stable performance, which usually use gate pulses to modulate the channel conductance through a floating gate and/or charge trapping layer. Herein, we report a three-terminal synaptic device based on a two-dimensional molybdenum ditelluride (MoTe₂)/hexagonal boron nitride (hBN) heterostructure. This structure enables stable and prominent conductance modulation of the MoTe₂channel by the photo-induced doping method through electron migration between the MoTe₂channel and ultraviolet (UV) light excited mid-gap defect states in hBN. Therefore, it is free of the floating gate and charge trapping layer to reduce the thickness and simplify the fabrication/design of the device. Moreover, since UV illumination is indispensable for stable doping in MoTe₂channel, the device can realize both short- (without UV illumination) and long- (with UV illumination) term plasticity. Meanwhile, the introduction of UV light allows additional tunability on the MoTe₂channel conductance through the wavelength and power intensity of incident UV, which may be important to mimic advanced synaptic functions. In addition, the photo-induced doping method can bidirectionally dope MoTe₂channel, which not only leads to large high/low resistance ratio for potential multi-level storage, but also implement both potentiation (n-doping) and depression (p-doping) of synaptic weight. This work explores alternative three-terminal synaptic configuration without floating gate and charge trapping layer, which may inspire researches on novel electrical synapse mechanisms.

Near-Infrared Artificial Synapses for Artificial Sensory Neuron System

The computing based on artificial neuron network is expected to break through the von Neumann bottleneck of traditional computer, and to greatly improve the computing efficiency, displaying a broad prospect in the application of artificial visual system. In the specific structural layout, it is a common method to connect the discrete photodetector with the artificial neuron in series, which enhances the complexity of signal recognition, conversion and storage. In this work, organic small molecule IR-780 iodide is inserted into the memory device as both the charge trapping layer and near-infrared (NIR) photoresponsive film. Through electrical and optical regulation, artificial synaptic functions including short-term plasticity, long-term plasticity, and spike rate dependence are realized. In the established artificial sensory neuron system, NIR optical pulses can significantly improve the spiking rate. Moreover, the spiking neural networks are further constructed by simulation for handwritten digit classification. This research may contribute to the development of light driven neural robots, optical signal encryption, and neural computing.

Printable Smart Materials and Devices: Strategies and Applications

Smart materials are a kind of functional materials which can sense and response to environmental conditions or stimuli from optical, electrical, magnetic mechanical, thermal, and chemical signals, etc. Patterning of smart materials is the key to achieving large-scale arrays of functional devices. Over the last decades, printing methods including inkjet printing, template-assisted printing, and 3D printing are extensively investigated and utilized in fabricating intelligent micro/nano devices, as printing strategies allow for constructing multidimensional and multimaterial architectures. Great strides in printable smart materials are opening new possibilities for functional devices to better serve human beings, such as wearable sensors, integrated optoelectronics, artificial neurons, and so on. However, there are still many challenges and drawbacks that need to be overcome in order to achieve the controllable modulation between smart materials and device performance. In this review, we give an overview on printable smart materials, printing strategies, and applications of printed functional devices. In addition, the advantages in actual practices of printing smart materials-based devices are discussed, and the current limitations and future opportunities are proposed. This review aims to summarize the recent progress and provide reference for novel smart materials and printing strategies as well as applications of intelligent devices.

Ultralow-power flexible transparent carbon nanotube synaptic transistors for emotional memory

Emulating the biological behavior of the human brain with artificial neuromorphic devices is essential for the future development of human-machine interactive systems, bionic sensing systems and intelligent robotic systems. In this paper, artificial flexible transparent carbon nanotube synaptic transistors (F-CNT-STs) with signal transmission and emotional learning functions are realized by adopting the poly(vinyl alcohol) (PVA)/SiO2 proton-conducting electrolyte. Synaptic functions of biological synapses including excitatory and inhibitory behaviors are successfully emulated in the F-CNT-STs. Besides, synaptic plasticity such as spike-duration-dependent plasticity, spike-number-dependent plasticity, spike-amplitude-dependent plasticity, paired-pulse facilitation, short-term plasticity, and long-term plasticity have all been systematically characterized. Moreover, the F-CNT-STs also closely imitate the behavior of human brain learning and emotional memory functions. After 1000 bending cycles at a radius of 3 mm, both the transistor characteristics and the synaptic functions can still be implemented correctly, showing outstanding mechanical capability. The realized F-CNT-STs possess low operating voltage, quick response, and ultra-low power consumption, indicating their high potential to work in low-power biological systems and artificial intelligence systems. The flexible artificial synaptic transistor enables its potential to be generally applicable to various flexible wearable biological and intelligent applications.

In Situ Observation of Low-Power Nano-Synaptic Response in Graphene Oxide Using Conductive Atomic Force Microscopy

Multiple studies have reported the observation of electro-synaptic response in different metal/insulator/metal devices. However, most of them analyzed large (>1 µm² ) devices that do not meet the integration density required by industry (10¹⁰  devices/mm² ). Some studies emploied a scanning tunneling microscope (STM) to explore nano-synaptic response in different materials, but in this setup there is a nanogap between the insulator and one of the metallic electrodes (i.e., the STM tip), not present in real devices. Here, it is demonstrated how to use conductive atomic force microscopy to explore the presence and quality of nano-synaptic response in confined areas <50 nm² . Graphene oxide (GO) is selected due to its easy fabrication. Metal/GO/metal nano-synapses exhibit potentiation and paired pulse facilitation with low write current levels <1 µA (i.e., power consumption ≈3 µW), controllable excitatory post-synaptic currents, and long-term potentiation and depression. The results provide a new method to explore nano-synaptic plasticity at the nanoscale, and point to GO as an important candidate for the fabrication of ultrasmall (<50 nm² ) electronic synapses fulfilling the integration density requirements of neuromorphic systems.

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.

Dendritic Network Implementable Organic Neurofiber Transistors with Enhanced Memory Cyclic Endurance for Spatiotemporal Iterative Learning

Dendritic network implementable organic neurofiber transistors with enhanced memory cyclic endurance for spatiotemporal iterative learning are proposed. The architecture of the fibrous organic electrochemical transistors consisting of a double-stranded assembly of electrode microfibers and an iongel gate insulator enables the highly sensitive multiple implementation of synaptic junctions via simple physical contact of gate-electrode microfibers, similar to the dendritic connections of a biological neuron fiber. In particular, carboxylic-acid-functionalized polythiophene as a semiconductor channel material provides stable gate-field-dependent multilevel memory characteristics with long-term stability and cyclic endurance, unlike the conventional poly(alkylthiophene)-based neuromorphic electrochemical transistors, which exhibit short retention and unstable endurance. The dissociation of the carboxylic acid of the polythiophene enables reversible doping and dedoping of the polythiophene channel by effectively stabilizing the ions that penetrate the channel during potentiation and depression cycles, leading to the reliable cyclic endurance of the device. The synaptic weight of the neurofiber transistors with a dendritic network maintains the state levels stably and is independently updated with each synapse connected with the presynaptic neuron to a specific state level. Finally, the neurofiber transistor demonstrates successful speech recognition based on iterative spiking neural network learning in the time domain, showing a substantial recognition accuracy of 88.9%.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

Artificial Neuron and Synapse Devices Based on 2D Materials

Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next-generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material-based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling-bond-free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials-based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all-or-nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials-based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials-based neuromorphic devices are discussed.

Nanofiber Channel Organic Electrochemical Transistors for Low-Power Neuromorphic Computing and Wide-Bandwidth Sensing Platforms

Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low-impedance sensing are desired. However, it has been a struggle to find materials and fabrication methods that satisfy both of these properties simultaneously in a single substrate. Here, nanofiber channels with a self-formed ion-blocking layer are fabricated to create organic electrochemical transistors (OECTs) that can be tailored to achieve low-power neuromorphic computing and fast-response sensing by transferring different amounts of electrospun nanofibers to each device. With their nanofiber architecture, the OECTs exhibit a low switching energy of 113 fJ and operate within a wide bandwidth (cut-off frequency of 13.5 kHz), opening a new paradigm for energy-efficient neuromorphic computing/sensing platforms in a biological environment without the leakage of personal information.

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.

Functional Fibers and Fabrics for Soft Robotics, Wearables, and Human-Robot Interface

Soft robotics inspired by the movement of living organisms, with excellent adaptability and accuracy for accomplishing tasks, are highly desirable for efficient operations and safe interactions with human. With the emerging wearable electronics, higher tactility and skin affinity are pursued for safe and user-friendly human-robot interactions. Fabrics interlocked by fibers perform traditional static functions such as warming, protection, and fashion. Recently, dynamic fibers and fabrics are favorable to deliver active stimulus responses such as sensing and actuating abilities for soft-robots and wearables. First, the responsive mechanisms of fiber/fabric actuators and their performances under various external stimuli are reviewed. Fiber/yarn-based artificial muscles for soft-robots manipulation and assistance in human motion are discussed, as well as smart clothes for improving human perception. Second, the geometric designs, fabrications, mechanisms, and functions of fibers/fabrics for sensing and energy harvesting from the human body and environments are summarized. Effective integration between the electronic components with garments, human skin, and living organisms is illustrated, presenting multifunctional platforms with self-powered potential for human-robot interactions and biomedicine. Lastly, the relationships between robotic/wearable fibers/fabrics and the external stimuli, together with the challenges and possible routes for revolutionizing the robotic fibers/fabrics and wearables in this new era are proposed.

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.

A Hippocampus-Inspired Dual-Gated Organic Artificial Synapse for Simultaneous Sensing of a Neurotransmitter and Light

Organic neuromorphic devices and sensors that mimic the functions of chemical synapses and sensory perception in humans have received much attention for next-generation computing and integrated logic circuits. Despite recent advances, organic artificial synapses capable of detecting both neurotransmitters in liquid environments and light are not reported. Herein, inspired by hippocampal synapses, a dual-gate organic synaptic transistor platform with a photoconductive polymer semiconductor, a ferroelectric insulator of P(VDF-TrFE), and an extended-gate electrode functionalized with boronic acid is developed to simultaneously detect the neurotransmitter dopamine and light. The developed synaptic transistor enables memory consolidation upon repetitive exposure to dopamine and polychromatic light, exhibiting effectively modulated postsynaptic currents. This proof-of-concept hippocampal-synapse-mimetic organic neuromorphic system combining a chemical sensor and a photosensor opens new possibilities for developing low-power organic artificial synaptic multisensors and light-induced memory consolidative artificial synapses, and can also contribute to the development of human-machine interfaces.

Contact-electrification-activated artificial afferents at femtojoule energy

Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Highly distributed synaptic sensory neurons are more readily driven by portable, distributed, and ubiquitous power sources. Here, we report a contact-electrification-activated artificial afferent at femtojoule energy. Upon the contact-electrification effect, the induced triboelectric signals activate the ion-gel-gated MoS₂ postsynaptic transistor, endowing the artificial afferent with the adaptive capacity to carry out spatiotemporal recognition/sensation on external stimuli (e.g., displacements, pressures and touch patterns). The decay time of the synaptic device is in the range of sensory memory stage. The energy dissipation of the artificial afferents is significantly reduced to 11.9 fJ per spike. Furthermore, the artificial afferents are demonstrated to be capable of recognizing the spatiotemporal information of touch patterns. This work is of great significance for the construction of next-generation neuromorphic sensory network, self-powered biomimetic electronics and intelligent interactive equipment.

Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Here, the authors report contact-electrification-activated artificial afferent at femtojoule energy, which is able to carry out spatiotemporal recognition on external stimuli.

Memristive Artificial Synapses for Neuromorphic Computing

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms

Artificial synapses (ASs) are electronic devices emulating important functions of biological synapses, which are essential building blocks of artificial neuromorphic networks for brain-inspired computing. A human brain consists of several quadrillion synapses for information storage and processing, and massively parallel computation. Neuromorphic systems require ASs to mimic biological synaptic functions, such as paired-pulse facilitation, short-term potentiation, long-term potentiation, spatiotemporally-correlated signal processing, and spike-timing-dependent plasticity, etc. Feature size and energy consumption of ASs need to be minimized for high-density energy-efficient integration. This work reviews recent progress on ASs. First, synaptic plasticity and functional emulation are introduced, and then synaptic electronic devices for neuromorphic computing systems are discussed. Recent advances in flexible artificial synapses for artificial sensory nerves are also briefly introduced. Finally, challenges and opportunities in the field are discussed.

Selective Release of Different Neurotransmitters Emulated by a p-i-n Junction Synaptic Transistor for Environment-Responsive Action Control

The design of the first p-i-n junction synaptic transistor (JST) based on n-type TiO2 film covered with poly(methyl methacrylate) (PMMA) and with a p-type P3HT/PEO nanowire (NW) on top. Except for basic synaptic functions that can be realized by a single neurotransmitter, the electronic device emulates the multiplexed neurotransmission of different neurotransmissions, i.e., glutamate and acetylcholine, for fast switching between short- and long-term plasticity (STP and LTP). This is realized by the special p-i-n junction with hole transport in the p-type P3HT NW to form STP, and electron transport in the n-type TiO2 layer and trapped under the PMMA inversion layer to form LTP. Altering the external input induces changes of the polarity of the charge carriers in the conductive channel, promoting fast switching between STP and LTP modes. When stimulated using two parallel inputs, the response of PMMA/TiO2 emulates the synergistic effect of taste and aroma on the control of food-intake in the brain. Because of the bipolarity, the p-i-n JST has excellent reconfigurability, which importantly is attributed to simulate the plasticity of synapses and to mimic how distinct types of gustatory receptor neurons respond to different concentrations of salt. The electronic device lays the technical foundation for the realization of the future complex artificial neural networks.

Recent Process of Flexible Transistor-Structured Memory

Flexible transistor-structured memory (FTSM) has attracted great attention for its important role in flexible electronics. For nonvolatile information storage, FTSMs with floating-gate, charge-trap, and ferroelectric mechanisms have been developed. By introducing an optical sensory module, FTSM can be operated by optical inputs to function as an optical memory transistor. As a special type of FTSM, transistor-structured artificial synapse emulates important functions of a biological synapse to mimic brain-inspired memory behaviors and nervous signal transmissions. This work reviews the recent development of the above mentioned FTSMs, with a focus on working mechanism and materials, and flexibility.

Bioinspired mechano-photonic artificial synapse based on graphene/MoS2 heterostructure

Developing multifunctional and diversified artificial neural systems to integrate multimodal plasticity, memory, and supervised learning functions is an important task toward the emulation of neuromorphic computation. Here, we present a bioinspired mechano-photonic artificial synapse with synergistic mechanical and optical plasticity. The artificial synapse is composed of an optoelectronic transistor based on graphene/MoS₂ heterostructure and an integrated triboelectric nanogenerator. By controlling the charge transfer/exchange in the heterostructure with triboelectric potential, the optoelectronic synaptic behaviors can be readily modulated, including postsynaptic photocurrents, persistent photoconductivity, and photosensitivity. The photonic synaptic plasticity is elaborately investigated under the synergistic effect of mechanical displacement and the light pulses embodying different spatiotemporal information. Furthermore, artificial neural networks are simulated to demonstrate the improved image recognition accuracy up to 92% assisted with mechanical plasticization. The mechano-photonic artificial synapse is highly promising for implementing mixed-modal interaction, emulating complex biological nervous system, and promoting the development of interactive artificial intelligence.

The capabilities of nanoelectronic 2-D materials for bio-inspired computing and drug delivery indicate their significance in modern drug design

Remarkable advancements in the computational techniques and nanoelectronics have attracted considerable interests for development of highly-sophisticated materials (Ms) including the theranostics with optimal characteristics and innovative delivery systems. Analyzing the huge amounts of multivariate data and solving the newly-emerged complicated problems including the healthcare-related ones have created increasing demands for improving the computational speed and minimizing the consumption of energy. Shifting towards the non-von Neumann approaches enables performing specific computational tasks and optimizing the processing of signals. Besides usefulness for neuromorphic computing and increasing the efficiency of computation energy, 2-D electronic Ms are capable of optical sensing with ultra-fast and ultra-sensitive responses, mimicking the neurons, detection of pathogens or biomolecules, and prediction of the progression of diseases, assessment of the pharmacokinetics/pharmacodynamics of therapeutic candidates, mimicking the dynamics of the release of neurotransmitters or fluxes of ions that might provide a deeper knowledge about the computations and information flow in the brain, and development of more effective treatment protocols with improved outcomes. 2-D Ms appear as the major components of the next-generation electronically-enabled devices for highly-advanced computations, bio-imaging, diagnostics, tissue engineering, and designing smart systems for site-specific delivery of therapeutics that might result in the reduced adverse effects of drugs and improved patient compliance. This manuscript highlights the significance of 2-D Ms in the neuromorphic computing, optimizing the energy efficiency of the multi-step computations, providing novel architectures or multi-functional systems, improved performance of a variety of devices and bio-inspired functionalities, and delivery of theranostics.

Ti3C2-Based MXene Oxide Nanosheets for Resistive Memory and Synaptic Learning Applications

MXene, a new state-of-the-art two-dimensional (2D) nanomaterial, has attracted considerable interest from both industry and academia because of its excellent electrical, mechanical, and chemical properties. However, MXene-based device engineering has rarely been reported. In this study, we explored Ti₃C₂ MXene for digital and analog computing applications by engineering the top electrode. For this purpose, Ti₃C₂ MXene was synthesized by a simple chemical process, and its structural, compositional, and morphological properties were studied using various analytical tools. Finally, we explored its potential application in bipolar resistive switching (RS) and synaptic learning devices. In particular, the effect of the top electrode (Ag, Pt, and Al) on the RS properties of the Ti₃C₂ MXene-based memory devices was thoroughly investigated. Compared with the Ag and Pt top electrode-based devices, the Al/Ti₃C₂/Pt device exhibited better RS and operated more reliably, as determined by the evaluation of the charge-magnetic property and memory endurance and retention. Thus, we selected the Al/Ti₃C₂/Pt memristive device to mimic the potentiation and depression synaptic properties and spike-timing-dependent plasticity-based Hebbian learning rules. Furthermore, the electron transport in this device was found to occur by a filamentary RS mechanism (based on oxidized Ti₃C₂ MXene), as determined by analyzing the electrical fitting curves. The results suggest that the 2D Ti₃C₂ MXene is an excellent nanomaterial for non-volatile memory and synaptic learning applications.

Solution-processed electronics for artificial synapses

Artificial synaptic devices and systems have become hot topics due to parallel computing, high plasticity, integration of storage, and processing to meet the challenges of the traditional Von Neumann computers. Currently, two-terminal memristors and three-terminal transistors have been mainly developed for high-density storage with high switching speed and high reliability because of the adjustable resistivity, controllable ion migration, and abundant choices of functional materials and fabrication processes. To achieve the low-cost, large-scale, and easy-process fabrication, solution-processed techniques have been extensively employed to develop synaptic electronics towards flexible and highly integrated three-dimensional (3D) neural networks. Herein, we have summarized and discussed solution-processed techniques in the fabrication of two-terminal memristors and three-terminal transistors for the application of artificial synaptic electronics mainly reported in the recent five years from the view of fabrication processes, functional materials, electronic operating mechanisms, and system applications. Furthermore, the challenges and prospects were discussed in depth to promote solution-processed techniques in the future development of artificial synapse with high performance and high integration.

A Monochloro Copper Phthalocyanine Memristor with High-Temperature Resilience for Electronic Synapse Applications

Memristors are considered to be one of the most promising device concepts for neuromorphic computing, in particular thanks to their highly tunable resistive states. To realize neuromorphic computing architectures, the assembly of large memristive crossbar arrays is necessary, but is often accompanied by severe heat dispassion. Organic materials can be tailored with on-demand electronic properties in the context of neuromorphic applications. However, such materials are more susceptible to heat, and detrimental effects such as thermally induced degradation directly lead to failure of device operation. Here, an organic memristive synapse formed of monochloro copper phthalocyanine, which remains operational and capable of memristive switching at temperatures as high as 300 °C in ambient air without any encapsulation, is demonstrated. The change in the electrical conductance is found to be a result of ion movement, closely resembling what takes place in biological neurons. Furthermore, the high viability of this approach is showcased by demonstrating flexible memristors with stable switching behaviors after repeated mechanical bending as well as organic synapses capable of emulating a trainable and reconfigurable memristor array for image information processing. The results set a precedent for thermally resilient organic synapses to impact organic neuromorphic devices in progressing their practicality.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Spectral blueshift of biophotonic activity and transmission in the ageing mouse brain

The brain is considered to be a complex system with extremely low energy consumption and high-efficiency information transmission and processing, and this system has not been replicated by any artificial systems so far. Several studies indicate that the activity and transmission of biophotons in neural circuits may play an important role in neural information communication, while the biophotonic spectral redshift from lower to higher in animals may be related to the evolution of intelligence. The ageing processes of higher organisms are often accompanied by a decline in brain functions; however, the underlying mechanisms are unclear. Combining an ultraweak biophoton imaging system with the improved biophoton spectral analysis device, we compared and analyzed the spectra of glutamate-induced biophotonic emissions in mouse brain slices at different ages (newborn, 1, 3, 6, 12, 15, and 18 months). We found that the glutamate-induced biophotonic emissions presented a spectral blueshift from young to old mice, suggesting that the brain may transform to use relatively high-energy biophotons for neural information transmission and processing during the ageing process. Such a change may lead to a gradual decrease in the efficiency of the nervous system and provide a new biophysical mechanism for explaining the ageing-related changes in cognitive functions.

Recent advances in optical and optoelectronic data storage based on luminescent nanomaterials

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems

The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can rival the human brain. Spiking neural networks based on neuromorphic computing platforms simulate the architecture and information processing of the intelligent brain, providing new insights for building AIs. The rapid development of materials engineering, device physics, chip integration, and neuroscience has led to exciting progress in neuromorphic computing with the goal of overcoming the von Neumann bottleneck. Herein, fundamental knowledge related to the structures and working principles of neurons and synapses of the biological nervous system is reviewed. An overview is then provided on the development of neuromorphic hardware systems, from artificial synapses and neurons to spike-based neuromorphic computing platforms. It is hoped that this review will shed new light on the evolution of brain-like computing.

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.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time- and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb₂ O₅ and Li_x SiO₂ is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (10⁶ ) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm^-2 ). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuromorphic computing to support edge application.

A Habituation Sensory Nervous System with Memristors

The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a Li_x SiO_y -based memristor (TiN/Li_x SiO_y /Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate-and-fire neuron based on a Ag/SiO₂ :Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.

Complementary amide-based donor-acceptor with unique nano-scale aggregation, fluorescence, and band gap-lowering properties: a WORM memory device

Organic fluorescent semiconducting nanomaterials have gained widespread research interest owing to their potential applications in the arena of high-tech devices. We designed two pyrazaacene-based compounds, their stacked system, and the role of gluing interactions to fabricate nanomaterials, and determined the prospective band gaps utilizing the density functional theory calculation. The two pyrazaacene derivatives containing complementary amide linkages (-CONH and -NHCO) were efficiently synthesized. The synthesized compounds are highly soluble in common organic solvents as well as highly fluorescent and photostable. The heterocycles and their mixture displayed efficient solvent dependent fluorescence in the visible region of the solar spectrum. Notably, the compounds were associated through complementary NH•••O = C type hydrogen bonding, π-π stacking, and hydrophobic interactions, and thereby afforded nanomaterials with a low band gap. Fascinatingly, the fabricated stacked nanomaterial system exhibited resistive switching behavior, leading to the fabrication of an efficient write-once-read-many-times memory device of crossbar structure.

Vertical organic synapse expandable to 3D crossbar array

Recently, three-terminal synaptic devices have attracted considerable attention owing to their nondestructive weight-update behavior, which is attributed to the completely separated terminals for reading and writing. However, the structural limitations of these devices, such as a low array density and complex line design, are predicted to result in low processing speeds and high energy consumption of the entire system. Here, we propose a vertical three-terminal synapse featuring a remote weight update via ion gel, which is also extendable to a crossbar array structure. This synaptic device exhibits excellent synaptic characteristics, which are achieved via precise control of ion penetration onto the vertical channel through the weight-control terminal. Especially, the applicability of the developed vertical organic synapse array to neuromorphic computing is demonstrated using a simple crossbar synapse array. The proposed synaptic device technology is expected to be an important steppingstone to the development of high-performance and high-density neural networks.

Short Communication: An Updated Design to Implement Artificial Neuron Synaptic Behaviors in One Device with a Control Gate

Background

As a key component in artificial intelligence computing, a transistor design is updated here as a potential alternative candidate for artificial synaptic behavior implementation. However, further updates are needed to better control artificial synaptic behavior. Here, an updated channel-electrode transistor design is proposed as an artificial synapse device; this structure is different from previously published designs by other groups.

Methods

A semiconductor characterization system was used in order to simulate the artificial synaptic behavior and a scanning electron microscope was used to characterize the device structure.

Results

It was found that the electrode added to the transistor channel had a strong impact on the representative transmission behavior of such artificial synaptic devices, such as excitatory postsynaptic current (EPSC) and the paired-pulse facilitation (PPF) index.

Conclusion

These behaviors were tuned effectively and the impact of the channel electrode is explained by the combined effects of the joint channel electrode and conventional gate. The voltage dependence of such oxide devices suggests more capability to emulate various synaptic behaviors for numerous medical and non-medical applications. This is extremely helpful for future neuromorphic computational system implementation.

Mixed receptors of AMPA and NMDA emulated using a 'Polka Dot'-structured two-dimensional conjugated polymer-based artificial synapse

In a biological synapse, α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors mediate fast excitatory neurotransmission, whereas N-methyl-d-aspartate (NMDA) receptors trigger an enhanced memory effect; the complementary roles of AMPA and NMDA are essential in short-term plasticity (STP) to enhance memory effect (EME) transition. Herein, we report the design and fabrication of the first two-dimensional (2D) conjugated polymer (CP)-based synaptic transistor. The special design of the 2D CP with nanoscale-segregated 'polka dot'-structured crystalline phases and adjacent amorphous phases emulate the different receptors of NMDA and AMPA on the postsynaptic membrane for the first time. The synergistic effect of mixed receptors distinguishes STP and enhanced memory effect with a critical point, which regulates the threshold level of the enhanced memory effect induction. This effect has not been reported yet. The special structure avoids easy saturation of a single receptor with consecutively increased excitatory postsynaptic current (EPSC) in response to 1200 stimuli. Furthermore, the 2D P3HT synapse successfully emulates activity-dependent synaptic plasticity, such as metaplasticity and homeostatic plasticity, which are advanced forms of plasticity, allowing the self-adaptive ability of a synapse, but have rarely been reported.

Fabrication of Waterproof Artificial Compound Eyes with Variable Field of View Based on the Bioinspiration from Natural Hierarchical Micro-Nanostructures

Planar and curved microlens arrays (MLAs) are the key components of miniaturized microoptical systems. In order to meet the requirements for advanced and multipurpose applications in microoptical field, a simple manufacturing method is urgently required for fabricating MLAs with unique properties, such as waterproofness and variable field-of-view (FOV) imaging. Such properties are beneficial for the production of advanced artificial compound eyes for the significant applications in complex microcavity environments with high humidity, for instance, miniature medical endoscopy. However, the simple and effective fabrication of advanced artificial compound eyes still presents significant challenges. In this paper, bioinspired by the natural superhydrophobic surface of lotus leaf, we propose a novel method for the fabrication of waterproof artificial compound eyes. Electrohydrodynamic jet printing was used to fabricate hierarchical MLAs and nanolens arrays (NLAs) on polydimethylsiloxane film. The flexible film of MLAs hybridized with NLAs exhibited excellent superhydrophobic property with a water contact angle of 158°. The MLAs film was deformed using a microfluidics chip to create artificial compound eyes with variable FOV, which ranged from 0° to 160°.

Multi-terminal ionic-gated low-power silicon nanowire synaptic transistors with dendritic functions for neuromorphic systems

Neuromorphic computing systems have shown powerful capability in tasks, such as recognition, learning, classification and decision-making, which are both challenging and inefficient in using the traditional computation architecture. The key elements including synapses and neurons, and their feasible hardware implementation are essential for practical neuromorphic computing. However, most existing synaptic devices used to emulate functions of a single synapse and the synapse-based networks are more energy intensive and less sustainable than their biological counterparts. The dendritic functions such as integration of spatiotemporal signals and spike-frequency coding characteristics have not been well implemented in a single synaptic device and thus play an imperative role in future practical hardware-based spiking neural networks. Moreover, most emerging synaptic transistors are fabricated by nanofabrication processes without CMOS compatibility for further wafer-scale integration. Herein, we demonstrate a novel ionic-gated silicon nanowire synaptic field-effect transistor (IGNWFET) with low power consumption (<400 fJ per switching event) based on the standard CMOS process platform. For the first time, the dendritic integration and dual-synaptic dendritic computations (such as "Add" and "Subtraction") could be realized by processing frequency coded spikes using a single device. Meanwhile, multi-functional characteristics of artificial synapses including the short-term and long-term synaptic plasticity, paired pulse facilitation and high-pass filtering were also successfully demonstrated based on 40 nm wide IGNWFETs. The migration of ions in polymer electrolyte and trapping in high-k dielectric were also experimentally studied in-depth to understand the short-term plasticity and long-term plasticity. Combined with statistical uniformity across a 4-inch wafer, the comprehensive performance of IGNWFET demonstrates its potential application in future biologically emulated neuromorphic systems.

Emulating synaptic response in n- and p-channel MoS2 transistors by utilizing charge trapping dynamics

Brain-inspired, neuromorphic computing aims to address the growing computational complexity and power consumption in modern von-Neumann architectures. Progress in this area has been hindered due to the lack of hardware elements that can mimic neuronal/synaptic behavior which form the fundamental building blocks for spiking neural networks (SNNs). In this work, we leverage the short/long term memory effects due to the electron trapping events in an atomically thin channel transistor that mimic the exchange of neurotransmitters and emulate a synaptic response. Re-doped (n-type) and Nb-doped (p-type) molybdenum di-sulfide (MoS₂) field-effect transistors are examined using pulsed-gate measurements, which identify the time scales of electron trapping/de-trapping. The devices demonstrate promising trends for short/long term plasticity in the order of ms/minutes, respectively. Interestingly, pulse paired facilitation (PPF), which quantifies the short-term plasticity, reveal time constants (τ₁ = 27.4 ms, τ₂ = 725 ms) that closely match those from a biological synapse. Potentiation and depression measurements describe the ability of the synaptic device to traverse several analog states, where at least 50 conductance values are accessed using consecutive pulses of equal height and width. Finally, we demonstrate devices, which can emulate a well-known learning rule, spike time-dependent plasticity (STDP) which codifies the temporal sequence of pre- and post-synaptic neuronal firing into corresponding synaptic weights. These synaptic devices present significant advantages over iontronic counterparts and are envisioned to create new directions in the development of hardware for neuromorphic computing.

Neuromorphic nanoelectronic materials

Memristive and nanoionic devices have recently emerged as leading candidates for neuromorphic computing architectures. While top-down fabrication based on conventional bulk materials has enabled many early neuromorphic devices and circuits, bottom-up approaches based on low-dimensional nanomaterials have shown novel device functionality that often better mimics a biological neuron. In addition, the chemical, structural and compositional tunability of low-dimensional nanomaterials coupled with the permutational flexibility enabled by van der Waals heterostructures offers significant opportunities for artificial neural networks. In this Review, we present a critical survey of emerging neuromorphic devices and architectures enabled by quantum dots, metal nanoparticles, polymers, nanotubes, nanowires, two-dimensional layered materials and van der Waals heterojunctions with a particular emphasis on bio-inspired device responses that are uniquely enabled by low-dimensional topology, quantum confinement and interfaces. We also provide a forward-looking perspective on the opportunities and challenges of neuromorphic nanoelectronic materials in comparison with more mature technologies based on traditional bulk electronic materials.

Bioinspired Multifunctional Organic Transistors Based on Natural Chlorophyll/Organic Semiconductors

Inspired by the photosynthesis process of natural plants, multifunctional transistors based on natural biomaterial chlorophyll and organic semiconductors (OSCs) are reported. Functions as photodetectors (PDs) and light-stimulated synaptic transistors (LSSTs) can be switched by gate voltage. As PDs, the devices exhibit ultrahigh photoresponsivity up to 2 × 10⁶ A W^-1 , detectivity of 6 × 10¹⁵ Jones, and I_photo /I_dark ratio of 2.7 × 10⁶ , which make them among the best reported organic PDs. As LSSTs, important synaptic functions similar to biological synapses are demonstrated, together with a dynamic learning and forgetting process and image-processing function. Significantly, benefiting from the ultrahigh photosensitivity of chlorophyll, the lowest operating voltage and energy consumption of the LSSTs can be 10^-5 V and 0.25 fJ, respectively. The devices also exhibit high flexibility and long-term air stability. This work provides a new guide for developing organic electronics based on natural biomaterials.

Three-Dimensional Nanoscale Flexible Memristor Networks with Ultralow Power for Information Transmission and Processing Application

To construct an artificial intelligence system with high efficient information integration and computing capability like the human brain, it is necessary to realize the biological neurotransmission and information processing in artificial neural network (ANN), rather than a single electronic synapse as most reports. Because the power consumption of single synaptic event is ∼10 fJ in biology, designing an intelligent memristors-based 3D ANN with energy consumption lower than femtojoule-level (e.g., attojoule-level) and faster operating speed than millisecond-level makes it possible for constructing a higher energy efficient and higher speed computing system than the human brain. In this paper, a flexible 3D crossbar memristor array is presented, exhibiting the multilevel information transmission functionality with the power consumption of 4.28 aJ and the response speed of 50 ns per synaptic event. This work is a significant step toward the development of an ultrahigh efficient and ultrahigh-speed wearable 3D neuromorphic computing system.

A flexible artificial intrinsic-synaptic tactile sensory organ

Imbuing bio-inspired sensory devices with intelligent functions of human sensory organs has been limited by challenges in emulating the preprocessing abilities of sensory organs such as reception, filtering, adaptation, and sensory memory at the device level itself. Merkel cells, which is a part of tactile sensory organs, form synapse-like connections with afferent neuron terminals referred to as Merkel cell-neurite complexes. Here, inspired by structure and intelligent functions of Merkel cell-neurite complexes, we report a flexible, artificial, intrinsic-synaptic tactile sensory organ that mimics synapse-like connections using an organic synaptic transistor with ferroelectric nanocomposite gate dielectric of barium titanate nanoparticles and poly(vinylidene fluoride-trifluoroethylene). Modulation of the post-synaptic current of the device induced by ferroelectric dipole switching due to triboelectric-capacitive coupling under finger touch allowed reception and slow adaptation. Modulation of synaptic weight by varying the nanocomposite composition of gate dielectric layer enabled tuning of filtering and sensory memory functions.

Bioinspired bio-voltage memristors

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated.