Fully Printed All-Solid-State Organic Flexible Artificial Synapse for Neuromorphic Computing

Modulation of polyaniline memristive device switching voltage by nucleotide-free analogue of vitamin B12

Memristive devices offer essential properties to become a part of the next-generation computing systems based on neuromorphic principles. Organic memristive devices exhibit a unique set of properties which makes them an indispensable choice for specific applications, such as interfacing with biological systems. While the switching rate of organic devices can be easily adjusted over a wide range through various methods, controlling the switching potential is often more challenging, as this parameter is intricately tied to the materials used. Given the limited options in the selection conductive polymers and the complexity of polymer chemical engineering, the most straightforward and accessible approach to modulate switching potentials is by introducing specific molecules into the electrolyte solution. In our study, we show polyaniline (PANI)-based device switching potential control by adding nucleotide-free analogue of vitamin B12, aquacyanocobinamide, to the electrolyte solution. The employed concentrations of this molecule, ranging from 0.2 to 2 mM, enabled organic memristive devices to achieve switching potential decrease for up to 100 mV, thus providing a way to control device properties. This effect is attributed to strong aromatic interactions between PANI phenyl groups and corrin macrocycle of the aquacyanocobinamide molecule, which was supported by ultraviolet-visible spectra analysis.

Neuromorphic Computing of Optoelectronic Artificial BFCO/AZO Heterostructure Memristors Synapses

Compared with purely electrical neuromorphic devices, those stimulated by optical signals have gained increasing attention due to their realistic sensory simulation. In this work, an optoelectronic neuromorphic device based on a photoelectric memristor with a Bi2FeCrO6/Al-doped ZnO (BFCO/AZO) heterostructure is fabricated that can respond to both electrical and optical signals and successfully simulate a variety of synaptic behaviors, such as STP, LTP, and PPF. In addition, the photomemory mechanism was identified by analyzing the energy band structures of AZO and BFCO. A convolutional neural network (CNN) architecture for pattern classification at the Mixed National Institute of Standards and Technology (MNIST) was used and improved the recognition accuracy of the MNIST and Fashion-MNIST datasets to 95.21% and 74.19%, respectively, by implementing an improved stochastic adaptive algorithm. These results provide a feasible approach for future implementation of optoelectronic synapses.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Printable devices for neurotechnology

Printable electronics for neurotechnology is a rapidly emerging field that leverages various printing techniques to fabricate electronic devices, offering advantages in rapid prototyping, scalability, and cost-effectiveness. These devices have promising applications in neurobiology, enabling the recording of neuronal signals and controlled drug delivery. This review provides an overview of printing techniques, materials used in neural device fabrication, and their applications. The printing techniques discussed include inkjet, screen printing, flexographic printing, 3D printing, and more. Each method has its unique advantages and challenges, ranging from precise printing and high resolution to material compatibility and scalability. Selecting the right materials for printable devices is crucial, considering factors like biocompatibility, flexibility, electrical properties, and durability. Conductive materials such as metallic nanoparticles and conducting polymers are commonly used in neurotechnology. Dielectric materials, like polyimide and polycaprolactone, play a vital role in device fabrication. Applications of printable devices in neurotechnology encompass various neuroprobes, electrocorticography arrays, and microelectrode arrays. These devices offer flexibility, biocompatibility, and scalability, making them cost-effective and suitable for preclinical research. However, several challenges need to be addressed, including biocompatibility, precision, electrical performance, long-term stability, and regulatory hurdles. This review highlights the potential of printable electronics in advancing our understanding of the brain and treating neurological disorders while emphasizing the importance of overcoming these challenges.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Breaking the Trade-Off Between Polymer Dielectric Constant and Loss via Aluminum Oxo Macrocycle Dopants for High-Performance Neuromorphic Electronics

The dielectric layer is crucial in regulating the overall performance of field-effect transistors (FETs), the key component in central processing units, sensors, and displays. Despite considerable efforts being devoted to developing high-permittivity (k) dielectrics, limited progress is made due to the inherent trade-off between dielectric constant and loss. Here, a solution is presented by designing a monodispersed disk-shaped Ce-Al-O-macrocycle as a dopant in polymer dielectrics. The molecule features a central Ce(III) core connected with eight Al atoms through sixteen bridging hydroxyls and eight 3-aminophenyl peripheries. The incorporation of this macrocycle in polymer dielectrics results in an up to sevenfold increase in dielectric constants and up to 89% reduction in dielectric loss at low frequencies. Moreover, the leakage-current densities decrease, and the breakdown strengths are improved by 63%. Relying on the above merits, FETs bearing cluster-doped polymer dielectrics give near three-orders source-drain current increments while maintaining low-level leakage/off currents, resulting in much higher charge-carrier mobilities (up to 2.45 cm2  V-1  s-1 ) and on/off ratios. This cluster-doping strategy is generalizable and shows great promise for ultralow-power photoelectric synapses and neuromorphic retinas. This work successfully breaks the trade-off between dielectric constant and loss and offers a unique design for polymer composite dielectrics.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Bionic Tactile-Gustatory Receptor for Object Identification Based on All-Polymer Electrochemical Transistor

Human sensory receptors enable the real world to be perceived effortlessly. Hence, massive efforts have been devoted to the development of bionic receptors capable of identifying objects. Unfortunately, most of the existing devices are limited to single sensory emulation and are established on solid-state electronic technologies, which are incompatible with the biological reactions occurring in electrolyte media. Here, an iontronic tactile-gustatory receptor using an all-polymer electrochemical transistor (AECT) is presented. The sensor is biocompatible with the operation voltage of 0.1 V, which is 1 to 2 orders lower than those of reported values. By this study, one receptor is able to accurately recognize various objects perceived by the human tactile and gustatory system without complex circuitry. Additionally, to promote its further application, flexible AECT arrays with channel length of 2 µm and density of 104 167 transistors cm^-2 (yield of 97%) are fabricated, 1 to 5 orders higher than those of related works. Finally, a flexible integrated network for electrocardiogram recording is successfully constructed. This study moves a step forward toward state-of-the-art bionic sensors.

Organic Memristor-Based Flexible Neural Networks with Bio-Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal-ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio-realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal-ion injections, for the first time. In the proposed artificial synapse, short-term plasticity (STP), long-term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion-injection density and electric-signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike-dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

Artificial Tactile Recognition Enabled by Flexible Low-Voltage Organic Transistors and Low-Power Synaptic Electronics

The advancement of self-powered intelligent strain systems for human-computer interaction is crucial toward wearable and energy-saving applications. Simultaneously, lowering operating voltage and thus reducing power consumption are of particular interests. A brain-like smart synaptic hardware system is considered as a promising candidate for low-power, parallel computing and learning processes. However, the combination of low-voltage organic transistors and energy efficient smart synapse hardware systems driven by a tactile signal has been hindered by the limited materials and technology. Here, by employing an elastomeric copolymer poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) with a high HFP content of 25 mol %, flexible, low-voltage transistors (|V_G| ≤ 3 V) and a low energy consumption synapse ≤ 9.2 × 10^-17 J are devised simultaneously, along with the lowest quality factor (R = P_w × V_G, 2.76 × 10^-16 J V). Furthermore, based on the low voltage and low power consumption characteristics, flexible artificial tactile recognition system and Morse code recognition are established without any computing supporting. Mechanical flexibility, cycling stability, image contrast enhancement functions, and simulated pattern recognition accuracy of the multilayer perceptron neural network are also simulated. This work recommends a route of exploiting low voltage, low power consumption synaptic systems and smart human-machine interfaces with low energy loss based on flexible organic synaptic transistors.

Neurorobotic approaches to emulate human motor control with the integration of artificial synapse

The advancement of electronic devices has enabled researchers to successfully emulate human synapses, thereby promoting the development of the research field of artificial synapse integrated soft robots. This paper proposes an artificial reciprocal inhibition system that can successfully emulate the human motor control mechanism through the integration of artificial synapses. The proposed system is composed of artificial synapses, load transistors, voltage/current amplifiers, and a soft actuator to demonstrate the muscle movement. The speed, range, and direction of the soft actuator movement can be precisely controlled via the preset input voltages with different amplitudes, numbers, and signs (positive or negative). The artificial reciprocal inhibition system can impart lifelike motion to soft robots and is a promising tool to enable the successful integration of soft robots or prostheses in a living body.

Emulating the Signal Transmission in a Neural System Using Polymer Membranes

Neurons are vital components of the brain. When stimulated by neurotransmitters at the dendrites, neurons deliver signals as changes in the membrane potential by ion movement. The signal transmission of a nervous system exhibits a high energy efficiency. These characteristics of neurons are being exploited to develop efficient neuromorphic computing systems. In this study, we develop chemical synapses for neuromorphic devices and emulate the signaling processes in a nervous system using a polymer membrane, in which the ionic permeability can be controlled. The polymer membrane comprises poly(diallyl-dimethylammonium chloride) and poly(3-sulfopropyl acrylate potassium salt), which have positive and negative charges, respectively. The ionic permeability of the polymer membrane is controlled by the injection of a neurotransmitter solution. This device emulates the signal transmission behavior of biological neurons depending on the concentration of the injected neurotransmitter solution. The proposed artificial neuronal signaling device can facilitate the development of bio-realistic neuromorphic devices.

Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing

Printed electronic devices have demonstrated their applicability in complex electronic circuits. There is recent progress in the realization of neuromorphic computing systems (NCSs) to implement basic synaptic functions using solution-processed materials. However, a fully printed neuron is yet to be realised. We demonstrate a fully printed artificial neuromorphic circuit on flexible polyimide (PI) substrate. Characteristic features of individual components of the printed system were guided by the software training of the NCS. The printing process employs graphene ink for passive structures and In2O3 as active material to print a two-input artificial neuron on PI. To ensure a small area footprint, the thickness of graphene film is tuned to target a resistance and to obtain conductors or resistors. The sheet resistance of the graphene film annealed at 300 °C can be adjusted between 200 Ω and 500 kΩ depending on the number of printed layers. The fully printed devices withstand a minimum of 2% tensile strain for at least 200 cycles of applied stress without any crack formation. The area usage of the printed two-input neuron is 16.25 mm2, with a power consumption of 37.7 mW, a propagation delay of 1 s, and a voltage supply of 2 V, which renders the device a promising candidate for future applications in smart wearable sensors.

Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic-Electronic Conductor

Organic mixed ionic-electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high-performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p- and n-type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.

Asymmetric carrier transport in flexible interface-type memristor enables artificial synapses with sub-femtojoule energy consumption

Flexible and transparent artificial synapses with extremely low energy consumption have potential for use in brain-like neuromorphic electronics. However, most of the transparent materials for flexible memristive artificial synapses were reported to show picojoule-scale high energy consumption with kiloohm-scale low resistance, which limits the scalability for parallel operation. Here, we report on a flexible memristive artificial synapse based on Cs₃Cu₂I₅ with energy consumption as low as 10.48 aJ (= 10.48 × 10^-18 J) μm^-2 and resistance as high as 243 MΩ for writing pulses. Interface-type resistive switching at the Schottky junction between p-type Cu₃Cs₂I₅ and Au is verified, where migration of iodide vacancies and asymmetric carrier transport owing to the effective hole mass is three times heavier than effective electron mass are found to play critical roles in controlling the conductance, leading to high resistance. There was little difference in synaptic weight updates with high linearity and 250 states before and after bending the flexible device. Moreover, the MNIST-based recognition rate of over 90% is maintained upon bending, indicative of a promising candidate for highly efficient flexible artificial synapses.

Dissecting Biological and Synthetic Soft-Hard Interfaces for Tissue-Like Systems

Soft and hard materials at interfaces exhibit mismatched behaviors, such as mismatched chemical or biochemical reactivity, mechanical response, and environmental adaptability. Leveraging or mitigating these differences can yield interfacial processes difficult to achieve, or inapplicable, in pure soft or pure hard phases. Exploration of interfacial mismatches and their associated (bio)chemical, mechanical, or other physical processes may yield numerous opportunities in both fundamental studies and applications, in a manner similar to that of semiconductor heterojunctions and their contribution to solid-state physics and the semiconductor industry over the past few decades. In this review, we explore the fundamental chemical roles and principles involved in designing these interfaces, such as the (bio)chemical evolution of adaptive or buffer zones. We discuss the spectroscopic, microscopic, (bio)chemical, and computational tools required to uncover the chemical processes in these confined or hidden soft-hard interfaces. We propose a soft-hard interaction framework and use it to discuss soft-hard interfacial processes in multiple systems and across several spatiotemporal scales, focusing on tissue-like materials and devices. We end this review by proposing several new scientific and engineering approaches to leveraging the soft-hard interfacial processes involved in biointerfacing composites and exploring new applications for these composites.

Advances in Flexible Memristors with Hybrid Perovskites

Hybrid organic-inorganic metal halide perovskite (HOIP)-based memristors have captured strong attention not only as an emerging candidate for next-generation high-density information storage technology but also for use in healthcare technology and the Internet of Things (IoT) because of their unique properties: low weight, flexibility, compatibility, stretchability, and low power consumption. In this Perspective, we review the recent advances of various aspects of flexible memristors focusing on the selection of the flexible substrates, materials, interfaces, several resistive switching mechanisms, and different methodologies of perovskite growth. The current state of the art of the memristor as an artificial synapse, light-induced resistive switching, and logic gates is comprehensively and systematically reviewed. Finally, we briefly discuss the stability factors of perovskites and present the conclusion with a broad outlook on the progress and challenges in the field of perovskite-based flexible memristors.

Realization and training of an inverter-based printed neuromorphic computing system

Emerging applications in soft robotics, wearables, smart consumer products or IoT-devices benefit from soft materials, flexible substrates in conjunction with electronic functionality. Due to high production costs and conformity restrictions, rigid silicon technologies do not meet application requirements in these new domains. However, whenever signal processing becomes too comprehensive, silicon technology must be used for the high-performance computing unit. At the same time, designing everything in flexible or printed electronics using conventional digital logic is not feasible yet due to the limitations of printed technologies in terms of performance, power and integration density. We propose to rather use the strengths of neuromorphic computing architectures consisting in their homogeneous topologies, few building blocks and analog signal processing to be mapped to an inkjet-printed hardware architecture. It has remained a challenge to demonstrate non-linear elements besides weighted aggregation. We demonstrate in this work printed hardware building blocks such as inverter-based comprehensive weight representation and resistive crossbars as well as printed transistor-based activation functions. In addition, we present a learning algorithm developed to train the proposed printed NCS architecture based on specific requirements and constraints of the technology.

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.

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 Multichannel Flexible Optoelectronic Fiber Device for Distributed Implantable Neurological Stimulation and Monitoring

Optical fibers made of polymeric materials possess high flexibility that can potentially integrate with flexible electronic devices to realize complex functions in biology and neurology. Here, a multichannel flexible device based on four individually addressable optical fibers transfer-printed with flexible electronic components and controlled by a wireless circuit is developed. The resulting device offers excellent mechanics that is compatible with soft and curvilinear tissues, and excellent diversity through switching different light sources. The combined configuration of optical fibers and flexible electronics allows optical stimulation in selective wavelengths guided by the optical fibers, while conducting distributed, high-throughput biopotential sensing using the flexible microelectrode arrays. The device has been demonstrated in vivo with rats through optical stimulation and simultaneously monitoring of spontaneous/evoked spike signals and local field potentials using 32 microelectrodes in four brain regions. Biocompatibility of the device has been characterized by behavior and immunohistochemistry studies, demonstrating potential applications of the device in long-term animal studies. The techniques to integrate flexible electronics with optical fibers may inspire the development of more flexible optoelectronic devices for sophisticated applications in biomedicine and biology.

Tunable and nonvolatile multibit data storage memory based on MoTe2/boron nitride/graphene heterostructures through contact engineering

Heterostructures formed by stacking atomically thin two-dimensional materials are promising candidates for flash memory devices to achieve premium performances, due to the capability of effective carrier modulation and unique charge trapping behavior at the interfaces with atomic flatness. Here, we report a nonvolatile floating-gate flash memory based on MoTe₂/h-BN/graphene van der Waals heterostructure, which possesses increased data storage capacity per cell and versatile tunability. The decent memory behavior of the device is enabled by the carriers stored in the floating gate of graphene layer, which tunnel through the dielectric layer of h-BN from the channel layer of MoTe₂ under static-electrical field. Consequently, the developed memory device is capable to store 2 bits per cell by applying varied gate bias to implement multi-distinctive current levels. The device also exhibits remarkable erase/program current ratio of ∼10⁵ with 1 µs switch speed and stable retention with estimated ∼30% charge loss after 10 yr. Furthermore, the memory device can operate in both p- and n-type modes through contact engineering, offering wide adaptability for emerging applications in electronic technologies, such as neuromorphic computing, data-adaptive energy efficient memory, and complex digital circuits.

Engineering Complex Synaptic Behaviors in a Single Device: Emulating Consolidation of Short-term Memory to Long-term Memory in Artificial Synapses via Dielectric Band Engineering

As one of the key neuronal activities associated with memory in the human brain, memory consolidation is the process of the transition of short-term memory (STM) to long-term memory (LTM), which transforms an external stimulus to permanently stored information. Here, we report the emulation of this complex synaptic function, consolidation of STM to LTM, in a single-crystal indium phosphide (InP) field effect transistor (FET)-based artificial synapse. This behavior is achieved via the dielectric band and charge trap lifetime engineering in a dielectric gate heterostructure of aluminum oxide and titanium oxide. We analyze the behavior of these complex synaptic functions by engineering a variety of action potential parameters, and the devices exhibit good endurance, long retention time (>10⁵ s), and high uniformity. Uniquely, this approach utilizes growth and device fabrication techniques which are scalable and back-end CMOS compatible, making this InP synaptic device a potential building block for neuromorphic computing.

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.

Flexible Carbon Nanotube Synaptic Transistor for Neurological Electronic Skin Applications

There is an increasing interest in the development of memristive or artificial synaptic devices that emulate the neuronal activities for neuromorphic computing applications. While there have already been many reports on artificial synaptic transistors implemented on rigid substrates, the use of flexible devices could potentially enable an even broader range of applications. In this paper, we report artificial synaptic thin-film transistors built on an ultrathin flexible substrate using high carrier mobility semiconducting single-wall carbon nanotubes. The synaptic characteristics of the flexible synaptic transistor including long-term/short-term plasticity, spike-amplitude-dependent plasticity, spike-width-dependent plasticity, paired-pulse facilitation, and spike-time-dependent plasticity have all been systematically characterized. Furthermore, we have demonstrated a flexible neurological electronic skin and its peripheral nerve with a flexible ferroelectret nanogenerator (FENG) serving as the sensory mechanoreceptor that generates action potentials to be processed and transmitted by the artificial synapse. In such neurological electronic skin, the flexible FENG sensor converts the tactile input (magnitude and frequency of force) into presynaptic action potential pulses, which are then passed to the gate of the synaptic transistor to induce change in its postsynaptic current, mimicking the modulation of synaptic weight in a biological synapse. Our neurological electronic skin closely imitates the behavior of actual human skin, and it allows for instantaneous detection of force stimuli and offers biological synapse-like behavior to relay the stimulus signals to the next stage. The flexible sensory skin could potentially be used to interface with skeletal muscle fibers for applications in neuroprosthetic devices.

Protonic solid-state electrochemical synapse for physical neural networks

Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Herein, we demonstrate the behavior of an alternative synapse design that relies on a deterministic charge-controlled mechanism, modulated electrochemically in solid-state. The device operates by shuffling the smallest cation, the proton, in a three-terminal configuration. It has a channel of active material, WO3. A solid proton reservoir layer, PdHx, also serves as the gate terminal. A proton conducting solid electrolyte separates the channel and the reservoir. By protonation/deprotonation, we modulate the electronic conductivity of the channel over seven orders of magnitude, obtaining a continuum of resistance states. Proton intercalation increases the electronic conductivity of WO3 by increasing both the carrier density and mobility. This switching mechanism offers low energy dissipation, good reversibility, and high symmetry in programming.

Room-temperature developed flexible biomemristor with ultralow switching voltage for array learning

As one of the emerging neuromorphic computing devices, memristors may break through the limitation of traditional computers with a von Neumann architecture. However, the development of flexible memristors is limited by the high-temperature fabrication process, large operating voltage and non-uniform distribution of resistance. The room-temperature process has attracted great attention due to its advantages of low thermal dissipation, low cost and excellent compatibility with flexible electronics. Here, we proposed a fully physical vapour deposition (PVD) process for fabricating a memristor without additional heat treatment. The device showed excellent resistive switching characteristics with ultralow set/reset voltages (0.48 V/-0.39 V), uniform distribution (10%/15%), stable retention characteristic, multilevel storage behavior and reliable flexibility (radius of 10 mm). With continuously modulated conductance, typical synaptic plasticities were simulated by our flexible biomemristor, including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) and learning-forgetting curve. Furthermore, the array learning behavior like that of the human brain was simulated with these trainable biomemristors. This study paves a new way for developing low-cost, wearable, neuromorphic computing electronics at room temperature and expands the applications of artificial synapse arrays.

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.

Electret-Based Organic Synaptic Transistor for Neuromorphic Computing

Neuromorphic computing inspired by the neural systems in human brain will overcome the issue of independent information processing and storage. An artificial synaptic device as a basic unit of a neuromorphic computing system can perform signal processing with low power consumption, and exploring different types of synaptic transistors is essential to provide suitable artificial synaptic devices for artificial intelligence. Hence, for the first time, an electret-based synaptic transistor (EST) is presented, which successfully shows synaptic behaviors including excitatory/inhibitory postsynaptic current, paired-pulse facilitation/depression, long-term memory, and high-pass filtering. Moreover, a neuromorphic computing simulation based on our EST is performed using the handwritten artificial neural network, which exhibits an excellent recognition accuracy (85.88%) after 120 learning epochs, higher than most reported organic synaptic transistors and close to the ideal accuracy (92.11%). Such a novel synaptic device enriches the diversity of synaptic transistors, laying the foundation for the diversified development of the next generation of neuromorphic computing systems.

Bottom-Gate Approach for All Basic Logic Gates Implementation by a Single-Type IGZO-Based MOS Transistor with Reduced Footprint

Logic functions are the key backbone in electronic circuits for computing applications. Complementary metal-oxide-semiconductor (CMOS) logic gates, with both n-type and p-type channel transistors, have been to date the dominant building blocks of logic circuitry as they carry obvious advantages over other technologies. Important physical limits are however starting to arise, as the transistor-processing technology has begun to meet scaling-down difficulties. To address this issue, there is the crucial need for a next-generation electronics era based on new concepts and designs. In this respect, a single-type channel multigate MOS transistor (SMG-MOS) is introduced holding the two important aspects of processing adaptability and low static dissipation of CMOS. Furthermore, the SMG-MOS approach strongly reduces the footprint down to 40% or even less area needed for current CMOS logic function in the same processing technology node. Logic NAND, NOT, AND, NOR, and OR gates, which typically require a large number of CMOS transistors, can be realized by a single SMG-MOS transistor. Two functional examples of SMG-MOS are reported here with their analysis based both on simulations and experiments. The results strongly suggest that SMG-MOS can represent a facile approach to scale down complex integrated circuits, enabling design flexibility and production rates ramp-up.

Low-Voltage, CMOS-Free Synaptic Memory Based on LiXTiO2 Redox Transistors

Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into Li_XTiO₂ that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the Li_XTiO₂ redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.

Molecular Junctions Inspired by Nature: Electrical Conduction through Noncovalent Nanobelts

Charge transport occurs in a range of biomolecular systems, whose structures have covalent and noncovalent bonds. Understanding from these systems have yet to translate into molecular junction devices. We design junctions which have hydrogen-bonds between the edges of a series of prototype noncovalent nanobelts (NCNs) and vary the number of donor-acceptors to study their electrical properties. From frontier molecular orbitals (FMOs) and projected density of state (DOS) calculations, we found these NCN dimer junctions to have low HOMO-LUMO gaps and states at the Fermi level, suggesting these are metallic-like systems. Their conductance properties were studied with nonequilibrium Green's functions density functional theory (NEGF-DFT) and was found to decrease with cooperative H-bonding, that is, the conductance decreased as the alternating donor-acceptors around the nanobelts attenuates to a uniform distribution in the H-bonding arrays. The latter gave the highest conductance of 51.3 × 10^-6 S and the Seebeck coefficient showed n-type (-36 to -39 μV K^-1) behavior, while the lower conductors with alternating H-bonds are p-type (49.7 to 204 μV K^-1). In addition, the NCNs have appreciable binding energies (19.8 to 46.1 kcal mol^-1), implying they could form self-assembled monolayer (SAM) heterojunctions leading to a polymeric network for long-range charge transport.

Reversible uptake and release of sodium ions in layered SnS2-reduced graphene oxide composites for neuromorphic devices

With the advent of brain-inspired computing for complex data processing, emerging nonvolatile memories have been widely studied to develop neuromorphic devices for pattern recognition and deep learning. However, the devices still suffer from limitations such as nonlinearity and large write noise because they adopt a stochastic switching approach. Here, we suggest a biomimetic three-terminal electrochemical artificial synapse that is operated by a conductance change in response to intercalation of sodium (Na+) ions into a layered SnS2-reduced graphene oxide (RGO) composite channel. SnS2-RGO can reversibly uptake and release Na+ ions, so the conductance of the channel in artificial synapse can be controlled effectively and thereby it can emulate essential synaptic functions including short-term plasticity, spatiotemporal signal processing, and transition from short-term to long-term plasticity. The artificial synapse also shows linear and symmetric potentiation/depression with low cycle-to-cycle variation; these responses could improve the write linearity and reduce the write noise of devices. This study demonstrates the feasibility of next-generation neuromorphic memory using ion-based electrochemical devices that can mimic biological synapses with the migration of Na+ ions.