Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors

Polymer Coating Enabled Carrier Modulation for Single-Walled Carbon Nanotube Network Inverters and Antiambipolar Transistors

The control of the performance of single-walled carbon nanotube (SWCNT) random network-based transistors is of critical importance for their applications in electronic devices, such as complementary metal oxide semiconducting (CMOS)-based logics. In ambient conditions, SWCNTs are heavily p-doped by the H2O/O2 redox couple, and most doping processes have to counteract this effect, which usually leads to broadened hysteresis and poor stability. In this work, we coated an SWCNT network with various common polymers and compared their thin-film transistors' (TFTs') performance in a nitrogen-filled glove box. It was found that all polymer coatings will decrease the hysteresis of these transistors due to the partial removal of charge trapping sites and also provide the stable control of the doping level of the SWCNT network. Counter-intuitively, polymers with electron-withdrawing functional groups lead to a dramatically enhanced n-branch in their transfer curve. Specifically, SWCNT TFTs with poly (vinylidene fluoride) coating show an n-type mobility up to 61 cm2/Vs, with a decent on/off ratio and small hysteresis. The inverters constructed by connecting two ambipolar TFTs demonstrate high gain but with certain voltage loss. P-type or n-type doping from polymer coating layers could suppress unnecessary n- or p-branches, shift the threshold voltage and optimize the performance of these inverters to realize rail-to-rail switching. Similar devices also demonstrate interesting antiambipolar performance with tunable on and off voltage when tested in a different configuration.

Unveiling the Role of Side Chain for Improving Nonvolatile Characteristics of Conjugated Polymers-Based Artificial Synapse

Interest has grown in services that consume a significant amount of energy, such as large language models (LLMs), and research is being conducted worldwide on synaptic devices for neuromorphic hardware. However, various complex processes are problematic for the implementation of synaptic properties. Here, synaptic characteristics are implemented through a novel method, namely side chain control of conjugated polymers. The developed devices exhibit the characteristics of the biological brain, especially spike-timing-dependent plasticity (STDP), high-pass filtering, and long-term potentiation/depression (LTP/D). Moreover, the fabricated synaptic devices show enhanced nonvolatile characteristics, such as long retention time (≈102 s), high ratio of Gmax/Gmin, high linearity, and reliable cyclic endurance (≈103 pulses). This study presents a new pathway for next-generation neuromorphic computing by modulating conjugated polymers with side chain control, thereby achieving high-performance synaptic properties.

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.

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

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

A sensory memory processing system with multi-wavelength synaptic-polychromatic light emission for multi-modal information recognition

Realizing multi-modal information recognition tasks which can process external information efficiently and comprehensively is an urgent requirement in the field of artificial intelligence. However, it remains a challenge to achieve simple structure and high-performance multi-modal recognition demonstrations owing to the complex execution module and separation of memory processing based on the traditional complementary metal oxide semiconductor (CMOS) architecture. Here, we propose an efficient sensory memory processing system (SMPS), which can process sensory information and generate synapse-like and multi-wavelength light-emitting output, realizing diversified utilization of light in information processing and multi-modal information recognition. The SMPS exhibits strong robustness in information encoding/transmission and the capability of visible information display through the multi-level color responses, which can implement the multi-level pain warning process of organisms intuitively. Furthermore, different from the conventional multi-modal information processing system that requires independent and complex circuit modules, the proposed SMPS with unique optical multi-information parallel output can realize efficient multi-modal information recognition of dynamic step frequency and spatial positioning simultaneously with the accuracy of 99.5% and 98.2%, respectively. Therefore, the SMPS proposed in this work with simple component, flexible operation, strong robustness, and highly efficiency is promising for future sensory-neuromorphic photonic systems and interactive artificial intelligence.

Environmentally Stable and Reconfigurable Ultralow-Power Two-Dimensional Tellurene Synaptic Transistor for Neuromorphic Edge Computing

While neuromorphic computing can define a new era for next-generation computing architecture, the introduction of an efficient synaptic transistor for neuromorphic edge computing still remains a challenge. Here, we envision an atomically thin 2D Te synaptic device capable of achieving a desirable neuromorphic edge computing design. The hydrothermally grown 2D Te nanosheet synaptic transistor apparently mimicked the biological synaptic nature, exhibiting 100 effective multilevel states, a low power consumption of ∼110 fJ, excellent linearity, and short-/long-term plasticity. Furthermore, the 2D Te synaptic device achieved reconfigurable MNIST recognition accuracy characteristics of 88.2%, even after harmful detergent environment infection. We believe that this work serves as a guide for developing futuristic neuromorphic edge computing.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.

Light-Controlled Reconfigurable Optical Synapse Based on Carbon Nanotubes/2D Perovskite Heterostructure for Image Recognition

Two-dimensional (2D) halide perovskite material is characterized by a mixed conducting behavior that possesses both electronic and ionic conductivity. The study on the influence of the light on ion migration in the 2D perovskite is helpful to improve the performance of perovskite-based optoelectronic devices. Here, we constructed an exfoliated 2D perovskite/carbon nanotubes (CNTs) heterostructure optical synapse, in which CNTs can be used as nanoprobes to qualitatively observe the ion aggregation or dissipation process in 2D perovskite, and found that light significantly changes the memory curve of the reconfigurable optical synapses. Through the molecular dynamic simulation, the dynamic process of ion migration in the heterostructure was simulated and the electrostatic interaction effect of nonequilibrium charge distribution of CNTs on iodide ion was demonstrated. Finally, an effective light-controlled process was realized through the synapses, which in situ regulated the performance of the weight-value discretized BP (WD-BP) neural network. This work lays a foundation for the future development of intelligent nano-optoelectronic devices.

Synaptic Emulation via Ferroelectric P(VDF-TrFE) Reinforced Charge Trapping/Detrapping in Zinc-Tin Oxide Transistor

Brain inspired artificial synapses are highly desirable for neuromorphic computing and are an alternative to a conventional computing system. Here, we report a simple and cost-effective ferroelectric capacitively coupled zinc-tin oxide (ZTO) thin-film transistor (TFT) topped with ferroelectric copolymer poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) for artificial synaptic devices. Ferroelectric dipoles enhance the charge trapping/detrapping effect in ZTO TFT, as confirmed by the transfer curve (I_D-V_G) analysis. This substantiates superior artificial synapse responses in ferroelectric-coupled ZTO TFT because the current potentiation and depression are individually improved. The ferroelectric-coupled ZTO TFT successfully emulates the essential features of the artificial synapse, including pair-pulsed facilitation (PPF) and potentiation/depression (P/D) characteristics. In addition, the device also mimics the memory consolidation behavior through intensified stimulation. This work demonstrates that the ferroelectric-coupled ZTO synaptic transistor possesses great potential as a hardware candidate for neuromorphic computing.

2D Heterostructures for Ubiquitous Electronics and Optoelectronics: Principles, Opportunities, and Challenges

A grand family of two-dimensional (2D) materials and their heterostructures have been discovered through the extensive experimental and theoretical efforts of chemists, material scientists, physicists, and technologists. These pioneering works contribute to realizing the fundamental platforms to explore and analyze new physical/chemical properties and technological phenomena at the micro-nano-pico scales. Engineering 2D van der Waals (vdW) materials and their heterostructures via chemical and physical methods with a suitable choice of stacking order, thickness, and interlayer interactions enable exotic carrier dynamics, showing potential in high-frequency electronics, broadband optoelectronics, low-power neuromorphic computing, and ubiquitous electronics. This comprehensive review addresses recent advances in terms of representative 2D materials, the general fabrication methods, and characterization techniques and the vital role of the physical parameters affecting the quality of 2D heterostructures. The main emphasis is on 2D heterostructures and 3D-bulk (3D) hybrid systems exhibiting intrinsic quantum mechanical responses in the optical, valley, and topological states. Finally, we discuss the universality of 2D heterostructures with representative applications and trends for future electronics and optoelectronics (FEO) under the challenges and opportunities from physical, nanotechnological, and material synthesis perspectives.

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

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

Accelerated Learning in Wide-Band-Gap AlN Artificial Photonic Synaptic Devices: Impact on Suppressed Shallow Trap Level

Artificial synaptic platforms are promising for next-generation semiconductor computing devices; however, state-of-the-art optoelectronic approaches remain challenging, owing to their unstable charge trap states and limited integration. We demonstrate wide-band-gap (WBG) III-V materials for photoelectronic neural networks. Our experimental analysis shows that the enhanced crystallinity of WBG synapses promotes better synaptic characteristics, such as effective multilevel states, a wider dynamic range, and linearity, allowing the better power consumption, training, and recognition accuracy of artificial neural networks. Furthermore, light-frequency-dependent memory characteristics suggest that artificial optoelectronic synapses with improved crystallinity support the transition from short-term potentiation to long-term potentiation, implying a clear emulation of the psychological multistorage model. This is attributed to the charge trapping in deep-level states and suppresses fast decay and nonradiative recombination in shallow traps. We believe that the fingerprints of these WBG synaptic characteristics provide an effective strategy for establishing an artificial optoelectronic synaptic architecture for innovative neuromorphic computing.

Rectifying optoelectronic memory based on WSe2/graphene heterostructures

van der Waals heterostructures composed of two-dimensional materials vertically stacked have been extensively studied to develop various multifunctional devices. Here, we report WSe₂/graphene heterostructure devices with a top floating gate that can serve as multifunctional devices. They exhibit gate-controlled rectification inversion, rectified nonvolatile memory effects, and multilevel optoelectronic memory effects. Depending on the polarity of the gate voltage pulses (V _Gp), electrons or holes can be trapped in the floating gate, resulting in rectified nonvolatile memory properties. Furthermore, upon repeated illumination with laser pulses, positive or negative staircase photoconductivity is observed depending on the history of V _Gp, which is ascribed to the tunneling of electrons or holes between the WSe₂ channel and the floating gate. These multifunctional devices can be used to emulate excitatory and inhibitory synapses that have different neurotransmitters. Various synaptic functions, such as potentiation/depression curves and spike-timing-dependent plasticity, have been also implemented using these devices. In particular, 128 optoelectronic memory states with nonlinearity less than 1 can be achieved by controlling applied laser pulses and V _Gp, suggesting that the WSe₂/graphene heterostructure devices with a top floating gate can be applied to optoelectronic synapse devices.

Synaptic transistors and neuromorphic systems based on carbon nano-materials

Carbon-based materials possessing a nanometer size and unique electrical properties perfectly address the two critical issues of transistors, the low power consumption and scalability, and are considered as a promising material in next-generation synaptic devices. In this review, carbon-based synaptic transistors were systematically summarized. In the carbon nanotube section, the synthesis of carbon nanotubes, purification of carbon nanotubes, the effect of architecture on the device performance and related carbon nanotube-based devices for neuromorphic computing were discussed. In the graphene section, the synthesis of graphene and its derivative, as well as graphene-based devices for neuromorphic computing, was systematically studied. Finally, the current challenges for carbon-based synaptic transistors were discussed.

Biologically Plausible Artificial Synaptic Array: Replicating Ebbinghaus' Memory Curve with Selective Attention

The nature of repetitive learning and oblivion of memory enables humans to effectively manage vast amounts of memory by prioritizing information for long-term storage. Inspired by the memorization process of the human brain, an artificial synaptic array is presented, which mimics the biological memorization process by replicating Ebbinghaus' forgetting curve. To construct the artificial synaptic array, signal-transmitting access transistors and artificial synaptic memory transistors are designed using indium-gallium-zinc-oxide and poly(3-hexylthiophene), respectively. To secure the desired performance of the access transistor in regulating the input signal to the synaptic transistor, the content of gallium in the access transistor is optimized. In addition, the operation voltage of the synaptic transistor is carefully selected to achieve memory-state efficiency. Repetitive learning characterizing Ebbinghaus' oblivion curves is realized using an artificial synaptic array with optimized conditions for both transistor components. This successfully demonstrates a biologically plausible memorization process. Furthermore, selective attention for information prioritization in the human brain is mimicked by selectively applying repetitive learning to a synaptic transistor with a high memory state. The demonstrated biologically plausible artificial synaptic array provides great scope for advancement in bioinspired electronics.

Artificial stimulus-response system capable of conscious response

A stimulus-response system and conscious response enable humans to respond effectively to environmental changes and external stimuli. This paper presents an artificial stimulus-response system that is inspired by human conscious response and is capable of emulating it. The system is composed of an artificial visual receptor, artificial synapse, artificial neuron circuits, and actuator. By incorporating these artificial nervous components, a series of conscious response processes that markedly reduces response time as a result of learning from repeated stimuli are demonstrated. The proposed artificial stimulus-response system offers the promise of a new research field that would aid the development of artificial intelligence-based organs for patients with neurological disorders.

Suppressed Stochastic Switching Behavior and Improved Synaptic Functions in an Atomic Switch Embedded with a 2D NbSe2 Material

We investigated chemical vapor-deposited (CVD) two-dimensional (2D) niobium diselenide (NbSe₂) material for the resistive switching and synaptic characteristics. Three different atomic switch devices with Ag/HfO₂/Pt, Ag/Ti/HfO₂/Pt, and Ag/NbSe₂/HfO₂/Pt were studied as both memory and neuromorphic devices. Both the inserted Ti and NbSe₂ buffer layers effectively control the stochastic Ag-ion diffusion, leading to suppressed variation of switching characteristics, which is a critical issue in an atomic switch device. Especially, the device with the 2D NbSe₂ buffer layer strikingly enhanced the device reliability in both endurance and retention. In conjunction with scanning transmission electron microscopy (STEM) and energy-dispersive spectrometry (EDS) analysis of the control of the Ag-ion migration, it was understood that filament connection is interrelated with the SET and RESET processes. Besides resistive behaviors in the memory device, various synapse functions such as spike-rate-dependent plasticity (SRDP), forgetting curve, potentiation, and depression were demonstrated with an atomic switch with the 2D NbSe₂ buffer layer. Furthermore, the emulated long-term synaptic property was simulated using the MNIST 28 × 28 pixel database. Using adopting a CVD 2D NbSe₂ blocking layer, the stochastic Ag-ion diffusion behavior is well-controlled and therefore stable switching and synapse functions are attained.

Carbon Nanotube Field-Effect Transistor-Based Chemical and Biological Sensors

Chemical and biological sensors have attracted great interest due to their importance in applications of healthcare, food quality monitoring, environmental monitoring, etc. Carbon nanotube (CNT)-based field-effect transistors (FETs) are novel sensing device configurations and are very promising for their potential to drive many technological advancements in this field due to the extraordinary electrical properties of CNTs. This review focuses on the implementation of CNT-based FETs (CNTFETs) in chemical and biological sensors. It begins with the introduction of properties, and surface functionalization of CNTs for sensing. Then, configurations and sensing mechanisms for CNT FETs are introduced. Next, recent progresses of CNTFET-based chemical sensors, and biological sensors are summarized. Finally, we end the review with an overview about the current application status and the remaining challenges for the CNTFET-based chemical and biological sensors.

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.

Nanoscale Patterning of Carbon Nanotubes: Techniques, Applications, and Future

Carbon nanotube (CNT) devices and electronics are achieving maturity and directly competing or surpassing devices that use conventional materials. CNTs have demonstrated ballistic conduction, minimal scaling effects, high current capacity, low power requirements, and excellent optical/photonic properties; making them the ideal candidate for a new material to replace conventional materials in next-generation electronic and photonic systems. CNTs also demonstrate high stability and flexibility, allowing them to be used in flexible, printable, and/or biocompatible electronics. However, a major challenge to fully commercialize these devices is the scalable placement of CNTs into desired micro/nanopatterns and architectures to translate the superior properties of CNTs into macroscale devices. Precise and high throughput patterning becomes increasingly difficult at nanoscale resolution, but it is essential to fully realize the benefits of CNTs. The relatively long, high aspect ratio structures of CNTs must be preserved to maintain their functionalities, consequently making them more difficult to pattern than conventional materials like metals and polymers. This review comprehensively explores the recent development of innovative CNT patterning techniques with nanoscale lateral resolution. Each technique is critically analyzed and applications for the nanoscale-resolution approaches are demonstrated. Promising techniques and the challenges ahead for future devices and applications are discussed.