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Functionally diverse devices with artificial neuron and synapse properties are critical for neuromorphic systems. We present a two-terminal artificial leaky-integrate-fire (LIF) neuron based on 6 nm Hf0.1Zr0.9O2 (HZO) antiferroelectric (AFE) thin films and develop a synaptic device through work function (WF) engineering. LIF neuron characteristics, including integration, firing, and leakage, are achieved in W/HZO/W devices due to the accumulated polarization and spontaneous depolarization of AFE HZO films. By engineering the top electrode with asymmetric WFs, we found that Au/Ti/HZO/W devices exhibit synaptic weight plasticity, such as paired-pulse facilitation and long-term potentiation/depression, achieving >90% accuracy in digit recognition within constructed artificial neural network systems. These findings suggest that AFE HZO capacitor-based neurons and WF-engineered artificial synapses hold promise for constructing efficient spiking neuron networks and artificial neural networks, thereby advancing neuromorphic computing applications based on emerging AFE HZO devices.
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We demonstrate a lithium (Li) imbued TiOx iontronic device that exhibits synapse-like short-term plasticity behavior without requiring a forming process beforehand or a compliance current during switching. A solid-state electrolyte lithium phosphorus oxynitride (LiPON) behaves as the ion source, and the embedding and releasing of Li ions inside the cathodic like TiOx renders volatile conductance responses from the device and offers a natural platform for hardware simulating neuron functionalities. Besides, these devices possess high uniformity and great endurance as no conductive filaments are present. Different short-term pulse-based phenomena, including paired pulse facilitation, post-tetanic potentiation, and spike rate-dependent plasticity, were observed with self-relaxation characteristics. Based on the voltage excitation period, the time scale of the volatile memory can be tuned. Temperature measurement reveals the ion displacement-induced conductance channels become frozen below 220 K. In addition, the volatile analog devices can be configured into nonvolatile memory units with multibit storage capabilities after an electroforming process. Therefore, on the same platform, we can configure volatile units as nonlinear dynamic reservoirs for performing neuromorphic training and the nonvolatile units as the weight storage layer. We proceed to use voice recognition as an example with the tunable time constant relationship and obtain 94.4% accuracy with a minimal training data set. Thus, this iontronic platform can effectively process and update temporal information for reservoir and neuromorphic computing paradigms.
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Brainoid computing using 2D atomic crystals and their heterostructures, by emulating the human brain's remarkable efficiency and minimal energy consumption in information processing, poses a formidable solution to the energy-efficiency and processing speed constraints inherent in the von Neumann architecture. However, conventional 2D material based heterostructures employed in brainoid devices are beset with limitations, performance uniformity, fabrication intricacies, and weak interfacial adhesion, which restrain their broader application. The introduction of novel 2D atomic-molecular heterojunctions (2DAMH), achieved through covalent functionalization of 2D materials with functional molecules, ushers in a new era for brain-like devices by providing both stability and tunability of functionalities. This review chiefly delves into the electronic attributes of 2DAMH derived from the synergy of polymer materials with 2D materials, emphasizing the most recent advancements in their utilization within memristive devices, particularly their potential in replicating the functionality of biological synapses. Despite ongoing challenges pertaining to precision in modification, scalability in production, and the refinement of underlying theories, the proliferation of innovative research is actively pursuing solutions. These endeavors illuminate the vast potential for incorporating 2DAMH within brain-inspired intelligent systems, highlighting the prospect of achieving a more efficient and energy-conserving computing paradigm.
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Hydrogels find widespread applications in biomedicine because of their outstanding biocompatibility, biodegradability, and tunable material properties. Hydrogels can be chemically functionalized or reinforced to respond to physical or chemical stimulation, which opens up new possibilities in the emerging field of intelligent bioelectronics. Here, the state-of-the-art in functional hydrogel-based transistors and memristors is reviewed as potential artificial synapses. Within these systems, hydrogels can serve as semisolid dielectric electrolytes in transistors and as switching layers in memristors. These synaptic devices with volatile and non-volatile resistive switching show good adaptability to external stimuli for short-term and long-term synaptic memory effects, some of which are integrated into synaptic arrays as artificial neurons; although, there are discrepancies in switching performance and efficacy. By comparing different hydrogels and their respective properties, an outlook is provided on a new range of biocompatible, environment-friendly, and sustainable neuromorphic hardware. How potential energy-efficient information storage and processing can be achieved using artificial neural networks with brain-inspired architecture for neuromorphic computing is described. The development of hydrogel-based artificial synapses can significantly impact the fields of neuromorphic bionics, biometrics, and biosensing.
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Two-dimensional (2D) transition metal dichalcogenides (TMDs) allow for atomic-scale manipulation, challenging the conventional limitations of semiconductor materials. This capability may overcome the short-channel effect, sparking significant advancements in electronic devices that utilize 2D TMDs. Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance. This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor. It delves into the impacts of miniaturization, including the reduction of channel length, gate length, source/drain contact length, and dielectric thickness on transistor operation and performance. In addition, this review provides a detailed analysis of performance parameters such as source/drain contact resistance, subthreshold swing, hysteresis loop, carrier mobility, on/off ratio, and the development of p-type and single logic transistors. This review details the two logical expressions of the single 2D-TMD logic transistor, including current and voltage. It also emphasizes the role of 2D TMD-based transistors as memory devices, focusing on enhancing memory operation speed, endurance, data retention, and extinction ratio, as well as reducing energy consumption in memory devices functioning as artificial synapses. This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices. This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications. It underscores the anticipated challenges, opportunities, and potential solutions in navigating the dimension and performance boundaries of 2D transistors.
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A promising approach for implementing biomimetic systems relies on organic electronic devices designed to emulate neural synapses. However, organic artificial synapses face challenges in achieving high yield and robustness, rendering them difficult to use in practical applications. In this work, a high-yield and highly stable bulk heterojunction (BHJ) synaptic device composed of Poly(3-hexylthiophene-2,5-diyl) (P3HT) and [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) was fabricated via a simple solution process followed by thermal treatments. The crystallinity of P3HT and the precipitation of PCBM in BHJ films can be controlled by the thermal annealing temperatures. At 80 °C, P3HT reaches its highest crystallinity, while PCBM remains uniformly distributed. This thermal treatment significantly contributes to the fabrication of devices characterized by a high yield rate, reaching 98.43%. Additionally, this device remained operational even after being immersed in deionized water, ethanol, and seawater for 100 h. More importantly, it exhibited high elasticity over a wide temperature range from -90 to 310 °C. Finally, this device was utilized to construct a biomimetic vehicle with autonomous memory learning capabilities. After repeated training, the avoidance time was optimized by 31.4%. The robust P3HT:PCBM artificial synapses hold great promise for advancing the development of biomimetic electronic products in extreme environments.
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Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.
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Solid electrolyte-gated transistors exhibit improved chemical stability and can fulfill the requirements of microelectronic packaging. Typically, metal oxide semiconductors are employed as channel materials. However, the extrinsic electron transport properties of these oxides, which are often prone to defects, pose limitations on the overall electrical performance. Achieving excellent repeatability and stability of transistors through the solution process remains a challenging task. In this study, we propose the utilization of a solution-based method to fabricate an In2O3/ZnO heterojunction structure, enabling the development of efficient multifunctional optoelectronic devices. The heterojunction's upper and lower interfaces induce energy band bending, resulting in the accumulation of a large number of electrons and a significant enhancement in transistor mobility. To mimic synaptic plasticity responses to electrical and optical stimuli, we utilize Li+-doped high-k ZrOxthin films as a solid electrolyte in the device. Notably, the heterojunction transistor-based convolutional neural network achieves a high accuracy rate of 93% in recognizing handwritten digits. Moreover, our research involves the simulation of a typical sensory neuron, specifically a nociceptor, within our synaptic transistor. This research offers a novel avenue for the advancement of cost-effective three-terminal thin-film transistors tailored for neuromorphic applications.
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Retina-inspired visual sensors play a crucial role in the realization of neuromorphic visual systems. Nevertheless, significant obstacles persist in the pursuit of achieving bidirectional synaptic behavior and attaining high performance in the context of photostimulation. In this study, we propose a reconfigurable all-optical controlled synaptic device based on the IGZO/SnO/SnS heterostructure, which integrates sensing, storage and processing functions. Relying on the simple heterojunction stack structure and the role of energy band engineering, synaptic excitatory and inhibitory behaviors can be observed under the light stimulation of ultraviolet (266 nm) and visible light (405, 520 and 658 nm) without additional voltage modulation. In particular, junction field-effect transistors based on the IGZO/SnO/SnS heterostructure were fabricated to elucidate the underlying bidirectional photoresponse mechanism. In addition to optical signal processing, an artificial neural network simulator based on the optoelectrical synapse was trained and recognized handwritten numerals with a recognition rate of 91%. Furthermore, we prepared an 8 × 8 optoelectrical synaptic array and successfully demonstrated the process of perception and memory for image recognition in the human brain, as well as simulated the situation of damage to the retina by ultraviolet light. This work provides an effective strategy for the development of high-performance all-optical controlled optoelectronic synapses and a practical approach to the design of multifunctional artificial neural vision systems.
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The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.
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Bioinspired synaptic devices have shown great potential in artificial intelligence and neuromorphic electronics. Low energy consumption, multi-modal sensing and recording, and multifunctional integration are critical aspects limiting their applications. Recently, a new synaptic device architecture, the ion-gating vertical transistor (IGVT), has been successfully realized and timely applied to perform brain-like perception, such as artificial vision, touch, taste, and hearing. In this short time, IGVTs have already achieved faster data processing speeds and more promising memory capabilities than many conventional neuromorphic devices, even while operating at lower voltages and consuming less power. This work focuses on the cutting-edge progress of IGVT technology, from outstanding fabrication strategies to the design and realization of low-voltage multi-sensing IGVTs for artificial-synapse applications. The fundamental concepts of artificial synaptic IGVTs, such as signal processing, transduction, plasticity, and multi-stimulus perception are discussed comprehensively. The contribution draws special attention to the development and optimization of multi-modal flexible sensor technologies and presents a roadmap for future high-end theoretical and experimental advancements in neuromorphic research that are mostly achievable by the synaptic IGVTs.
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The ability of ferroelectric memristors to modulate conductance and offer multilevel storage has garnered significant attention in the realm of artificial synapses. On one hand, the resistance change of ferroelectric memristors mainly depends on the polarization reversal. On the other hand, the defects such as oxygen vacancies, which are inevitable presence during high-temperature processes, can undergo diffusion drift with the polarization reversal, thereby change the interface potential barrier. Thus, it is both desirable and necessary to investigate the synergistic effect of ferroelectricity and defects. Here, we prepare BaTiO3 ferroelectric memristor by pulse laser deposition and achieve resistance switching through the synergistic effect of ferroelectricity and oxygen vacancies. The memristor shows excellent switching characteristics with a large switching ratio (104) and good stability (103 s). It effectively emulates the features of artificial synapses and accomplishes decimal logical neural computing. In the neuromorphic system crafted with the memristor, the recognition accuracy of the 28 × 28 pixel image reaches 94.9%. These findings strongly support the research of ferroelectric memristors in neuromorphic devices.
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Exploring multiple states based on the domain wall (DW) position has garnered increased attention for in-memory computing applications, particularly focusing on the utilization of spin-orbit torque (SOT) to drive DW motion. However, devices relying on the DW position require efficient DW pinning. Here, we achieve granular magnetization switching by incorporating an HfOx insertion layer between the Co/Ti interface. This corresponds to a transition in the switching model from the DW motion to DW nucleation. Compared to the conventional Pt/Co/Ti structure, incorporation of the HfOx layer results in an enhanced SOT efficiency and a lower switching current density. We also realized stable multistate storage and synaptic plasticity by applying pulse current in the Pt/Co/HfOx/Ti device. The simulation of artificial neural networks (ANN) based on the device can perform digital recognition tasks with an accuracy rate of 91%. These results identify that DW nucleation with a Pt/Co/HfOx/Ti based device has potential applications in multistate storage and ANN.
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Metal halide perovskites (MHPs) are considered as promising candidates in the application of nonvolatile high-density, low-cost resistive switching (RS) memories and artificial synapses, resulting from their excellent electronic and optoelectronic properties including large light absorption coefficient, fast ion migration, long carrier diffusion length, low trap density, high defect tolerance. Among MHPs, 2D halide perovskites have exotic layered structure and great environment stability as compared with 3D counterparts. Herein, recent advances of 2D MHPs for the RS memories and artificial synapses realms are comprehensively summarized and discussed, as well as the layered structure properties and the related physical mechanisms are presented. Furthermore, the current issues and developing roadmap for the next-generation 2D MHPs RS memories and artificial synapse are elucidated.
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Electrolyte-gated transistors (EGTs) are promising candidates as artificial synapses owing to their precise conductance controllability, quick response times, and especially their low operating voltages resulting from ion-assisted signal transmission. However, it is still vague how ion-related physiochemical elements and working mechanisms impact synaptic performance. Here, to address the unclear correlations, we suggest a methodical approach based on electrochemical analysis using poly(ethylene oxide) EGTs with three alkali ions: Li+, Na+, and K+. Cyclic voltammetry is employed to identify the kind of electrochemical reactions taking place at the channel/electrolyte interface, which determines the nonvolatile memory functionality of the EGTs. Additionally, using electrochemical impedance spectroscopy and qualitative analysis of electrolytes, we confirm that the intrinsic properties of electrolytes (such as crystallinity, solubility, and ion conductivity) and ion dynamics ultimately define the linearity/symmetricity of conductance modulation. Through simple but systematic electrochemical analysis, these results offer useful insights for the selection of components for high-performing artificial synapses.
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Artificial vision systems (AVS) have potential applications in visual prosthetics and artificially intelligent robotics, and they require a preprocessor and a processor to mimic human vision. Halide perovskite (HP) is a promising preprocessor and processor due to its excellent photoresponse, ubiquitous charge migration pathways, and innate hysteresis. However, the material instability associated with HP thin films hinders their utilization in physical AVSs. Herein, we have developed ultrahigh-density arrays of robust HP nanowires (NWs) rooted in a porous alumina membrane (PAM) as the active layer for an AVS. The NW devices exhibit gradual photocurrent change, responding to changes in light pulse duration, intensity, and number, and allow contrast enhancement of visual inputs with a device lifetime of over 5 months. The NW-based processor possesses temporally stable conductance states with retention >105 s and jitter <10%. The physical AVS demonstrated 100% accuracy in recognizing different shapes, establishing HP as a reliable material for neuromorphic vision systems.
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α-In2Se3 semiconductor crystals realize artificial synapses by tuning in-plane and out-of-plane ferroelectricity with diverse avenues of electrical and optical pulses. While the electrically induced ferroelectricity of α-In2Se3 shows synaptic memory operation, the optically assisted synaptic plasticity in α-In2Se3 has also been preferred for polarization flipping enhancement. Here, the synaptic memory behavior of α-In2Se3 is demonstrated by applying electrical gate voltages under white light. As a result, the induced internal electric field is identified at a polarization flipped conductance channel in α-In2Se3/hexagonal boron nitride (hBN) heterostructure ferroelectric field effect transistors (FeFETs) under white light and discuss the contribution of this built-in electric field on synapse characterization. The biased dipoles in α-In2Se3 toward potentiation polarization direction by an enhanced internal built-in electric field under illumination of white light lead to improvement of linearity for long-term depression curves with proper electric spikes. Consequently, upon applying appropriate electric spikes to α-In2Se3/hBN FeFETs with illuminating white light, the recognition accuracy values significantly through the artificial learning simulation is elevated for discriminating hand-written digit number images.
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Recent advancements in reservoir computing (RC) research have created a demand for analogue devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic memristors, with nonlinear and short-term memory dynamics, are excellent candidates as information-processing devices or reservoirs for temporal classification and prediction tasks. Previous implementations relied on nominally identical memristors that applied the same nonlinear transformation to the input data, which is not enough to achieve a rich state space. To address this limitation, researchers either diversified the data encoding across multiple memristors or harnessed the stochastic device-to-device variability among the memristors. However, this approach requires additional preprocessing steps and leads to synchronization issues. Instead, it is preferable to encode the data once and pass them through a reservoir layer consisting of memristors with distinct dynamics. Here, we demonstrate that ion-channel-based memristors with voltage-dependent dynamics can be controllably and predictively tuned through the voltage or adjustment of the ion channel concentration to exhibit diverse dynamic properties. We show, through experiments and simulations, that reservoir layers constructed with a small number of distinct memristors exhibit significantly higher predictive and classification accuracies with a single data encoding. We found that for a second-order nonlinear dynamical system prediction task, the varied memristor reservoir experimentally achieved an impressive normalized mean square error of 1.5 × 10-3, using only five distinct memristors. Moreover, in a neural activity classification task, a reservoir of just three distinct memristors experimentally attained an accuracy of 96.5%. This work lays the foundation for next-generation physical RC systems that can exploit the complex dynamics of their diverse building blocks to achieve increased signal processing capabilities.
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Charge trap materials that can store carriers efficiently and controllably are desired for memory applications. 2D materials are promising for highly compacted and reliable memory mainly due to their ease of constructing atomically uniform interfaces, however, remain unexplored as being charge trap media. Here it is discovered that 2D semiconducting PbI2 is an excellent charge trap material for nonvolatile memory and artificial synapses. It is simple to construct PbI2 -based charge trap devices since no complicated synthesis or additional defect manufacturing are required. As a demonstration, MoS2 /PbI2 device exhibits a large memory window of 120 V, fast write speed of 5 µs, high on-off ratio around 106 , multilevel memory of over 8 distinct states, high reliability with endurance up to 104 cycles and retention over 1.2 × 104 s. It is envisioned that PbI2 with ionic activity caused by the natively formed iodine vacancies is unique to combine with unlimited 2D materials for versatile van der Waals devices with high-integration and multifunctionality.
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Memristor-based artificial synapses are regarded as the most promising candidate to develop brain-like neuromorphic network computers and overcome the bottleneck of Von-Neumann architecture. Violet phosphorus (VP) as a new allotrope of available phosphorus with outstanding electro-optical properties and stability has attracted more and more attention in the past several years. In this study, large-scale, high-yield VP microfiber vertical arrays have been successfully developed on a Sn-coated graphite paper and are used as the memristor functional layers to build reliable, low-power artificial synaptic devices. The VP devices can well mimic the major synaptic functions such as short-term memory (STM), long-term memory (LTM), paired-pulse facilitation (PPF), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP) under both electrical and light stimulation conditions, even the dendritic synapse functions and simple logical operations. By virtue of the excellent performance, the VP artificial synapse devices can be conductive to building high-performance optic-neural synaptic devices simulating the human-like optic nerve system. On this basis, Pavlov's associative memory can be successfully implemented optically. This study provides a promising approach for the design and manufacture of VP-based artificial synaptic devices and outlines a direction with multifunctional neural devices.