All-Solid-State Synaptic Transistors with High-Temperature Stability Using Proton Pump Gating of Strongly Correlated Materials

Controlled Migration of Lithium Cations by Diamine Bridges in Water-Processable Polymer-Based Solid-State Electrolyte Memory Layers for Organic Synaptic Transistors

Synaptic transistors require sufficient retention (memory) performances of current signals to exactly mimic biological synapses. Ion migration has been proposed to achieve high retention characteristics but less attention has been paid to polymer-based solid-state electrolytes (SSEs) for organic synaptic transistors (OSTRs). Here, OSTRs with water-processable polymer-based SSEs, featuring ion migration-controllable molecular bridges, which are prepared by reactions of poly(4-styrenesulfonic acid) (PSSA), diethylenetriamine (DETA), and lithium hydroxide (LiOH) are demonstrated. The ion conductivity of PSSA:LiOH:DETA (1:0.4:X, PLiD) films is remarkably changed by the molar ratio (X) of DETA, which is attributed to the extended distances between the PSSA chains by the DETA bridges. The devices with the PLiD layers deliver noticeably changed hysteresis reaching an optimum at X = 0.2, leading to the longest retention of current signals upon single/double pulses. The long-term potentiation test confirms that the present OSTRs can gradually build up the postsynaptic current by gate pulses of -2 V, while the long-term depression can be adjusted by varying the depression gate pulses (≈0.2-1.2 V). The artificial neural network simulations disclose that the present OSTRs with the ion migration-controlled PLiD layers can perform synaptic processes with an accuracy of ≈96%.

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

A high-performance organic lithium salt-doped OFET with the optical radical effect for photoelectric pulse synaptic simulation and neuromorphic memory learning

Simulation of synaptic characteristics is essential for the application of organic field effect transistors (OFETs) in neural morphology. Although excellent performance, including bias stability and mobility, as well as photoelectric pulse synaptic simulation, has been achieved in SiO2-gated OFETs with PDVT-10 as an organic channel, there are relatively few studies on photoelectric pulse synaptic simulation of electrolyte-gated OFETs based on environmentally friendly and low-voltage operation. Herein, synaptic transistors based on organic semiconductors are reported to simulate the photoelectric pulse response by developing solution-based organic semiconductor PDVT-10, and polyvinyl alcohol (PVA) with an electric double layer (EDL) effect to act as a channel and gate dielectric layer, respectively, and organic lithium salt-doped PVA is used to enhance the EDL effect. The presence of electrical pulses in doped devices not only achieves basic electrical synaptic characteristics, but also significantly realizes the long-term characteristics, pain perception, memory and sensitization applications. Furthermore, the introduction of photoinitiator molecules into the channel layer leads to improved photosynaptic performances by using light-induced free radicals, and the photoelectric synergistic effect has been actualized by introducing heterojunction architecture. This work provides promising prospects for achieving photoelectric pulse modulation based on organic synaptic devices, which shows great potential for the development of artificial intelligence.

Phase-Field Model of Electronic Antidoping

Charge carrier doping usually reduces the resistance of a semiconductor or insulator, but was recently found to dramatically enhance the resistance in certain series of materials. This remarkable antidoping effect has been leveraged to realize synaptic memory trees in nanoscale hydrogenated perovskite nickelates, opening a new direction for neuromorphic computing. To understand these phenomena, we formulate a physical phase-field model of the antidoping effect based on its microscopic mechanism and simulate the voltage-driven resistance change in the prototypical system of hydrogenated perovskite nickelates. Remarkably, the simulations using this model, containing only one adjustable parameter whose magnitude is justified by first-principles calculations, quantitatively reproduce the experimentally observed treelike resistance states, which are shown unambiguously to arise from proton redistribution-induced local band gap enhancement and carrier blockage. Our work lays the foundation for modeling the antidoping phenomenon in strongly correlated materials at the mesoscale, which can provide guidance to the design of novel antidoping-physics-based devices.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing

Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications

As technologies like the Internet, artificial intelligence, and big data evolve at a rapid pace, computer architecture is transitioning from compute-intensive to memory-intensive. However, traditional von Neumann architectures encounter bottlenecks in addressing modern computational challenges. The emulation of the behaviors of a synapse at the device level by ionic/electronic devices has shown promising potential in future neural-inspired and compact artificial intelligence systems. To address these issues, this review thoroughly investigates the recent progress in metal-oxide heterostructures for neuromorphic applications. These heterostructures not only offer low power consumption and high stability but also possess optimized electrical characteristics via interface engineering. The paper first outlines various synthesis methods for metal oxides and then summarizes the neuromorphic devices using these materials and their heterostructures. More importantly, we review the emerging multifunctional applications, including neuromorphic vision, touch, and pain systems. Finally, we summarize the future prospects of neuromorphic devices with metal-oxide heterostructures and list the current challenges while offering potential solutions. This review provides insights into the design and construction of metal-oxide devices and their applications for neuromorphic systems.

Advancements in Complementary Metal-Oxide Semiconductor-Compatible Tunnel Barrier Engineered Charge-Trapping Synaptic Transistors for Bio-Inspired Neural Networks in Harsh Environments

This study aimed to propose a silicon-on-insulator (SOI)-based charge-trapping synaptic transistor with engineered tunnel barriers using high-k dielectrics for artificial synapse electronics capable of operating at high temperatures. The transistor employed sequential electron trapping and de-trapping in the charge storage medium, facilitating gradual modulation of the silicon channel conductance. The engineered tunnel barrier structure (SiO2/Si3N4/SiO2), coupled with the high-k charge-trapping layer of HfO2 and high-k blocking layer of Al2O3, enabled reliable long-term potentiation/depression behaviors within a short gate stimulus time (100 μs), even under elevated temperatures (75 and 125 °C). Conductance variability was determined by the number of gate stimuli reflected in the maximum excitatory postsynaptic current (EPSC) and the residual EPSC ratio. Moreover, we analyzed the Arrhenius relationship between the EPSC as a function of the gate pulse number (N = 1-100) and the measured temperatures (25, 75, and 125 °C), allowing us to deduce the charge trap activation energy. A learning simulation was performed to assess the pattern recognition capabilities of the neuromorphic computing system using the modified National Institute of Standards and Technology datasheets. This study demonstrates high-reliability silicon channel conductance modulation and proposes in-memory computing capabilities for artificial neural networks using SOI-based charge-trapping synaptic transistors.

Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface

A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.

Intelligent matter endows reconfigurable temperature and humidity sensations for in-sensor computing

Data-centric tactics with in-sensor computing go beyond the conventional computing-centric tactic that is suffering from processing latency and excessive energy consumption. The multifunctional intelligent matter with dynamic smart responses to environmental variations paves the way to implement data-centric tactics with high computing efficiency. However, intelligent matter with humidity and temperature sensitivity has not been reported. In this work, a design is demonstrated based on a single memristive device to achieve reconfigurable temperature and humidity sensations. Opposite temperature sensations at the low resistance state (LRS) and high resistance state (HRS) were observed for low-level sensory data processing. Integrated devices mimicking intelligent electronic skin (e-skin) can work in three modes to adapt to different scenarios. Additionally, the device acts as a humidity-sensory artificial synapse that can implement high-level cognitive in-sensor computing. The intelligent matter with reconfigurable temperature and humidity sensations is promising for energy-efficient artificial intelligence (AI) systems.

Monolayer MoS2 Synaptic Transistors for High-Temperature Neuromorphic Applications

As essential units in an artificial neural network (ANN), artificial synapses have to adapt to various environments. In particular, the development of synaptic transistors that can work above 125 °C is desirable. However, it is challenging due to the failure of materials or mechanisms at high temperatures. Here, we report a synaptic transistor working at hundreds of degrees Celsius. It employs monolayer MoS₂ as the channel and Na⁺-diffused SiO₂ as the ionic gate medium. A large on/off ratio of 10⁶ can be achieved at 350 °C, 5 orders of magnitude higher than that of a normal MoS₂ transistor in the same range of gate voltage. The short-term plasticity has a synaptic transistor function as an excellent low-pass dynamic filter. Long-term potentiation/depression and spike-timing-dependent plasticity are demonstrated at 150 °C. An ANN can be simulated, with the recognition accuracy reaching 90%. Our work provides promising strategies for high-temperature neuromorphic applications.

Photoassisted Electron-Ion Synergic Doping Induced Phase Transition of n-VO2/p-GaN Thin-Film Heterojunction

As a typical correlated metal oxide, vanadium dioxide (VO₂) shows specific metal-insulator transition (MIT) properties and demonstrates great potential applications in ultrafast optoelectronic switch, resistive memory, and neuromorphic devices. Effective control of the MIT process is essential for improving the device performance. In the current study, we have first proposed a photoassisted ion-doping method to modulate the phase transition of the VO₂ layer based on the photovoltaic effect and electron-ion synergic doping in acid solution. Experimental results show that, for the prepared n-VO₂/p-GaN nanojunction, this photoassisted strategy can effectively dope the n-VO₂ layer by H⁺, Al³⁺, or Mg²⁺ ions under light radiation and trigger consecutive insulator-metal-insulator transitions. If combined with standard lithography or electron beam etching processes, selective doping with nanoscale size area can also be achieved. This photoassisted doping method not only shows a facile route for MIT modulation via a doping route under ambient conditions but also supplies some clues for photosensitive detection in the future.

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.

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

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

Catalytic Hydrogen Doping of NdNiO3 Thin Films under Electric Fields

The electric-field-assisted hydrogenation and corresponding resistance modulation of NdNiO₃ (NNO) thin-film resistors were systematically studied as a function of temperature and dc electric bias. Catalytic Pt electrodes serve as triple-phase boundaries for hydrogen incorporation into a perovskite lattice. A kinetic model describing the relationship between resistance modulation and proton diffusion was proposed by considering the effect of the electric field during hydrogenation. An electric field, in addition to thermal activation, is demonstrated to effectively control the proton distribution along its gradient with an efficiency of ∼22% at 2 × 10⁵ V/m. The combination of an electric field and gas-phase annealing is shown to enable the elegant control of the diffusional doping of complex oxides.

In Vivo Glutamate Sensing inside the Mouse Brain with Perovskite Nickelate-Nafion Heterostructures

Glutamate, one of the main neurotransmitters in the brain, plays a critical role in communication between neurons, neuronal development, and various neurological disorders. Extracellular measurement of neurotransmitters such as glutamate in the brain is important for understanding these processes and developing a new generation of brain-machine interfaces. Here, we demonstrate the use of a perovskite nickelate-Nafion heterostructure as a promising glutamate sensor with a low detection limit of 16 nM and a response time of 1.2 s via amperometric sensing. We have designed and successfully tested novel perovskite nickelate-Nafion electrodes for recording of glutamate release ex vivo in electrically stimulated brain slices and in vivo from the primary visual cortex (V1) of awake mice exposed to visual stimuli. These results demonstrate the potential of perovskite nickelates as sensing media for brain-machine interfaces.

Perovskite neural trees

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

Accelerated Hydrogen Diffusion and Surface Exchange by Domain Boundaries in Epitaxial VO2 Thin Films

Electronic phase modulation based on hydrogen insertion/extraction is kinetically limited by the bulk hydrogen diffusion or surface exchange reaction, so slow hydrogen kinetics has been a fundamental challenge to be solved for realizing faster solid-state electrochemical switching devices. Here we accelerate electronic phase modulation that occurs by hydrogen insertion in VO₂ through vertically aligned 2D defects induced by symmetry mismatch between epitaxial films and substrates. By using domain-matching epitaxial growth of monoclinic VO₂ films with lattice rotation and twinning on hexagonal Al₂O₃ substrates, the domain boundaries naturally align vertically; they provide a "highway" for hydrogen diffusion and surface exchange in VO₂ films and overcome the limited rates of bulk diffusion and surface reaction. From the quantitative analysis of the deuterium (²H) isotope tracer exchange, it is confirmed that the tracer diffusion coefficient (D*) and surface exchange coefficient (k*) were increased by several orders of magnitude in VO₂ films that had domain boundaries. These results yield fundamental insights into the mechanism by which mobile ions are inserted along extended defects and provide a strategy to overcome a limitation to switching speed in electrochemical devices that exploit ion insertion.

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.