Protonic solid-state electrochemical synapse for physical neural networks

Switching Response in Organic Electrochemical Transistors by Ionic Diffusion and Electronic Transport

The switching response in organic electrochemical transistors (OECT) is a basic effect in which a transient current occurs in response to a voltage perturbation. This phenomenon has an important impact on different aspects of the application of OECT, such as the equilibration times, the hysteresis dependence on scan rates, and the synaptic properties for neuromorphic applications. Here we establish a model that unites vertical ion diffusion and horizontal electronic transport for the analysis of the time-dependent current response of OECTs. We use a combination of tools consisting of a physical analytical model; advanced 2D drift-diffusion simulation; and the experimental measurement of a poly(3-hexylthiophene) (P3HT) OECT. We show the reduction of the general model to simple time-dependent equations for the average ionic/hole concentration inside the organic film, which produces a Bernards-Malliaras conservation equation coupled with a diffusion equation. We provide a basic classification of the transient response to a voltage pulse, and the correspondent hysteresis effects of the transfer curves. The shape of transients is basically related to the main control phenomenon, either the vertical diffusion of ions during doping and dedoping, or the equilibration of electronic current along the channel length.

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.

Multifunctional In-Memory Logics Based on a Dual-Gate Antiambipolar Transistor toward Non-von Neumann Computing Architecture

In-memory computing may make it possible to realize non-von Neumann computing because the logic circuits are unified in the memory units. We investigated two types of in-memory logic operations, namely, two-input logic circuits and multifunctional artificial synapses. These were realized in a dual-gate antiambipolar transistor (AAT) with a ReS2/WSe2 heterojunction, in which polystyrene with a zinc phthalocyanine core (ZnPc-PS4) was incorporated as a memory layer. Here, reconfigurability is a key concept for both types of device operations and was achieved by merging the Λ-shaped transfer curve of the AAT and the nonvolatile memory effect of ZnPc-PS4. First, we achieved electrically reconfigurable two-input logic circuits. Versatile logic circuits such as AND, OR, NAND, NOR, and XOR circuits were demonstrated by taking advantage of the Λ-shaped transfer curve of the dual-gate AAT. Importantly, the nonvolatile memory function provided the electrical switching of the individual circuits between AND/OR, NAND/NOR, and XOR/NAND circuits with constant input signals. Second, the memory effect was applied to multifunctional artificial synapses. The inhibitory/excitatory and long-term potentiation/depression synaptic operations were electrically reconfigured simply by controlling one parameter (readout voltage), making three distinct responses possible even with the same presynaptic signals. These findings provide hints that may lead to the realization of new in-memory computing architectures beyond the current von Neumann computers.

Bio-Inspired Organic Synaptor with In Situ Ion-Doped Ultrathin Polyelectrolyte Containing Acetylcholine-Like Cation

An ion-based synaptic transistor (synaptor) is designed to emulate a biological synapse using controlled ion movements. However, developing a solid-state electrolyte that can facilitate ion movement while achieving large-scale integration remains challenging. Here, a bio-inspired organic synaptor (BioSyn) with an in situ ion-doped polyelectrolyte (i-IDOPE) is demonstrated. At the molecular scale, a polyelectrolyte containing the tert-amine cation, inspired by the neurotransmitter acetylcholine is synthesized using initiated chemical vapor deposition (iCVD) with in situ doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method results in an ultrathin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that is not previously attainable. At a synapse scale, synapse functionality is replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system is mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate, and stretchable poly(styrene-ethylene-butylene-styrene) for a decentralized processing unit. Finally, a classification accuracy of 90.6% is achieved through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns.

Sodium-Controlled Interfacial Resistive Switching in Thin Film Niobium Oxide for Neuromorphic Applications

A double layer 2-terminal device is employed to show Na-controlled interfacial resistive switching and neuromorphic behavior. The bilayer is based on interfacing biocompatible NaNbO3 and Nb2O5, which allows the reversible uptake of Na+ in the Nb2O5 layer. We demonstrate voltage-controlled interfacial barrier tuning via Na+ transfer, enabling conductivity modulation and spike-amplitude- and spike-timing-dependent plasticity. The neuromorphic behavior controlled by Na+ ion dynamics in biocompatible materials shows potential for future low-power sensing electronics and smart wearables with local processing.

Spatial evolution of the proton-coupled Mott transition in correlated oxides for neuromorphic computing

The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO3-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.

All-Oxide Metasurfaces Formed by Synchronized Local Ionic Gating

Ionic gating of oxide thin films has emerged as a novel way of manipulating the properties of thin films. Most studies are carried out on single devices with a three-terminal configuration, but, by exploring the electrokinetics during the ionic gating, such a configuration with initially insulating films leads to a highly non-uniform gating response of individual devices within large arrays of the devices. It is shown that such an issue can be circumvented by the formation of a uniform charge potential by the use of a thin conducting underlayer. This synchronized local ionic gating allows for the simultaneous manipulation of the electrical, magnetic, and/or optical properties of large arrays of devices. Designer metasurfaces formed in this way from SrCoO2.5 thin films display an anomalous optical reflection of light that relies on the uniform and coherent response of all the devices. Beyond oxides, almost any material whose properties can be controlled by the addition or removal of ions via gating can form novel metasurfaces using this technique. These findings provide insights into the electrokinetics of ionic gating and a wide range of applications using synchronized local ionic gating.

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.

3D-integrated multilayered physical reservoir array for learning and forecasting time-series information

A wide reservoir computing system is an advanced architecture composed of multiple reservoir layers in parallel, which enables more complex and diverse internal dynamics for multiple time-series information processing. However, its hardware implementation has not yet been realized due to the lack of a high-performance physical reservoir and the complexity of fabricating multiple stacks. Here, we achieve a proof-of-principle demonstration of such hardware made of a multilayered three-dimensional stacked 3 × 10 × 10 tungsten oxide memristive crossbar array, with which we further realize a wide physical reservoir computing for efficient learning and forecasting of multiple time-series data. Because a three-layer structure allows the seamless and effective extraction of intricate three-dimensional local features produced by various temporal inputs, it can readily outperform two-dimensional based approaches extensively studied previously. Our demonstration paves the way for wide physical reservoir computing systems capable of efficiently processing multiple dynamic time-series information.

Strategy to improve synaptic behavior of ion-actuated synaptic transistors-the use of ion blocking layer to improve state retention

Synaptic transistors (STs) with a gate/electrolyte/channel stack, where mobile ions are electrically driven across the solid electrolyte, have been considered as analog weight elements for neuromorphic computing. The current (ID) between the source and drain in the ST is analogously updated by gate voltage (VG) pulses, enabling high pattern recognition accuracy in neuromorphic systems; however, the governing physical mechanisms of the ST are not fully understood yet. Our previous physics-based simulation study showed that ion movement in the electrolyte, rather than the electrochemical reactions that occur in the channel, plays an important role in switching. In this study, we experimentally explore the properties of the HfOx electrolyte and show that by tuning the density of oxygen vacancies, it can assume the dual role of electrolyte and channel. We demonstrate analog synaptic behavior using a novel ST with a two-layer stack of CuOx/HfOx, where the CuOx is the gate and Cu ion reservoir, and the HfOx is the electrolyte and channel. To improve state retention and linearity, we introduce a Cu ion transport barrier in the form of a dense and stoichiometric Al2O3 layer. The CuOx/Al2O3/HfOx exhibits excellent state retention and improved potentiation and depression response. Energy dispersive spectroscopy mapping following potentiation confirms the role of the Al2O3 layer in confining the Cu ions in the HfOx layer. We also show that a two-step programming scheme can further enhance synaptic response and demonstrate high recognition accuracy on the Fashion-MNIST dataset in simulation.

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.

Demonstration of the threshold-switching memory devices using EMIm(AlCl3)Cl and ZnO for neuromorphic applications

The threshold-switching behaviors of the synapses lead to energy-efficient operation in the neural computing system. Here, we demonstrated the threshold-switching memory devices by inserting the ZnO layer into the ionic synaptic devices. The EMIm(AlCl3)Cl is utilized as the electrolyte because its conductance can be tuned by the charge states of the Al-based ions. The redox reactions of the Al ions in the electrolyte can lead to the analog resistive switching characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, potentiation, and depression. By inserting the ZnO layer into the EMIm(AlCl3)-based ionic synaptic devices, the threshold switching behaviors are demonstrated. Using the resistivity difference between ZnO and EMIm(AlCl3)Cl, the analog resistive switching behaviors are tunned as the threshold-switching behaviors. The threshold-switching behaviors are achieved by applying the spike stimuli to the device. Demonstration of the threshold-switching behaviors of the ionic synaptic devices has a possibility to achieve high energy-efficiency for the ion-based artificial synapses.

A low-power vertical dual-gate neurotransistor with short-term memory for high energy-efficient neuromorphic computing

Neuromorphic computing aims to emulate the computing processes of the brain by replicating the functions of biological neural networks using electronic counterparts. One promising approach is dendritic computing, which takes inspiration from the multi-dendritic branch structure of neurons to enhance the processing capability of artificial neural networks. While there has been a recent surge of interest in implementing dendritic computing using emerging devices, achieving artificial dendrites with throughputs and energy efficiency comparable to those of the human brain has proven challenging. In this study, we report on the development of a compact and low-power neurotransistor based on a vertical dual-gate electrolyte-gated transistor (EGT) with short-term memory characteristics, a 30 nm channel length, a record-low read power of ~3.16 fW and a biology-comparable read energy of ~30 fJ. Leveraging this neurotransistor, we demonstrate dendrite integration as well as digital and analog dendritic computing for coincidence detection. We also showcase the potential of neurotransistors in realizing advanced brain-like functions by developing a hardware neural network and demonstrating bio-inspired sound localization. Our results suggest that the neurotransistor-based approach may pave the way for next-generation neuromorphic computing with energy efficiency on par with those of the brain.

Effects of Mg Doping to a LiCoO2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors

Artificial synapses with ideal functionalities are essential in hardware neural networks to allow for energy-efficient analog computing. However, the realization of linear and symmetric weight updates in real synaptic devices has proven challenging and ultimately limits the online training capabilities of neural network systems. Herein, we investigate the effect of Mg doping on a LiCoO2 (LCO) channel in a Li ion-gated synaptic transistor, so as to improve long-term and short-term plasticity. Two transistor structures, based on a lithium phosphorus oxynitride electrolyte, were examined by using undoped LCO and Mg-doped LCO as the channel material between the source and drain electrodes. It was found that Mg doping increased the initial channel conductance by 3 orders of magnitude, which is probably due to the substitution of Co3+ by Mg2+ and the compensation of hole creation. It was further found that the doped channel transistor showed good retention characteristics and better linearity of long-term potentiation and depression when voltage pulses were applied to the gate electrode. The improved retention and linearity are attributed to an extended range of the insulator-to-conductor transition by Mg doping and Li-ion extraction/insertion cooperated in the LCO channel. Using the obtained synaptic weight update, artificial neural network simulations demonstrated that the doped channel transistor shows an image recognition accuracy of ∼80% for handwritten digits, which is higher than ∼65% exhibited by the undoped channel transistor. Mg doping also improved short-term plasticity such as paired-pulse facilitation/depression and Hebbian spike timing-dependent plasticity. These results indicate that elemental doping to the channel of Li ion-gated synaptic transistors could be a useful procedure for realizing robust neuromorphic systems based on analog computing.

Exploring the Cutting-Edge Frontiers of Electrochemical Random Access Memories (ECRAMs) for Neuromorphic Computing: Revolutionary Advances in Material-to-Device Engineering

Advanced materials and device engineering has played a crucial role in improving the performance of electrochemical random access memory (ECRAM) devices. ECRAM technology has been identified as a promising candidate for implementing artificial synapses in neuromorphic computing systems due to its ability to store analog values and its ease of programmability. ECRAM devices consist of an electrolyte and a channel material sandwiched between two electrodes, and the performance of these devices depends on the properties of the materials used. This review provides a comprehensive overview of material engineering strategies to optimize the electrolyte and channel materials' ionic conductivity, stability, and ionic diffusivity to improve the performance and reliability of ECRAM devices. Device engineering and scaling strategies are further discussed to enhance ECRAM performance. Last, perspectives on the current challenges and future directions in developing ECRAM-based artificial synapses in neuromorphic computing systems are provided.

Electrochemical Ionic Synapses: Progress and Perspectives

Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three-terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid-solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+ , O2- , and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal-oxide-semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.

Flexible aluminum-doped hafnium oxide ferroelectric synapse devices for neuromorphic computing

The HfO2-based ferroelectric tunnel junction has received outstanding attention owing to its high-speed and low-power characteristics. In this work, aluminum-doped HfO2 (HfAlO) ferroelectric thin films are deposited on a muscovite substrate (Mica). We investigate the bending effect on the ferroelectric characteristics of the Au/Ti/HfAlO/Pt/Ti/Mica device. After 1000 bending times, the ferroelectric properties and the fatigue characteristics are largely degraded. The finite element analysis indicates that crack formation is the main reason for the fatigue damage under threshold bending diameters. Moreover, the HfAlO-based ferroelectric synaptic device exhibits excellent performance of neuromorphic computing. The artificial synapse can mimic the paired-pulse facilitation and long-term potentiation/depression of biological synapses. Meanwhile, the accuracy of digit recognition is 88.8%. This research provides a new research idea for the further development of hafnium-based ferroelectric devices.

Ultrahigh Effective Diffusion in Oxide by Engineering the Interfacial Transporter Channels

Mass storage and removal in solids always play a vital role in technological applications such as modern batteries and neuronal computations. However, they were kinetically limited by the slow diffusional process in the lattice, which made it challenging to fabricate applicable conductors with high electronic and ionic conductivities at room temperature. Here, we proposed an acid solution/WO3/ITO sandwich structure and achieved ultrafast H transport in the WO3 layer by interfacial job-sharing diffusion, which means the spatially separated transport of the H+ and e- in different layers. From the color change of WO3, the effective diffusion coefficient (Deff) was estimated, dramatically increasing ≤106 times and overwhelming values from previous reports. The experiments and simulations also revealed the universality of extending this approach to other atoms and oxides, which could stimulate systematic studies of ultrafast mixed conductors in the future.

Engineering Interface Defects and Interdiffusion at the Degenerate Conductive In2O3/Al2O3 Interface for Stable Electrodes in a Saline Solution

A low-temperature Al₂O₃ deposition process provides a simplified method to form a conductive two-dimensional electron gas (2DEG) at the metal oxide/Al₂O₃ heterointerface. However, the impact of key factors of the interface defects and cation interdiffusion on the interface is still not well understood. Furthermore, there is still a blank space in terms of applications that go beyond the understanding of the interface's electrical conductivity. In this work, we carried out a systematic experimental study by oxygen plasma pretreatment and thermal annealing post-treatment to study the impact of interface defects and cation interdiffusion at the In₂O₃/Al₂O₃ interface on the electrical conductance, respectively. Combining the trends in electrical conductance with the structural characteristics, we found that building a sharp interface with a high concentration of interface defects provides a reliable approach to producing such a conductive interface. After applying this conductive interface as electrodes for fabricating a field-effect transistor (FET) device, we found that this interface electrode exhibited ultrastability in phosphate-buffered saline (PBS), a commonly used biological saline solution. This study provides new insights into the formation of conductive 2DEGs at metal oxide/Al₂O₃ interfaces and lays the foundation for further applications as electrodes in bioelectronic devices.

Nanoscale multistate resistive switching in WO3 through scanning probe induced proton evolution

Multistate resistive switching device emerges as a promising electronic unit for energy-efficient neuromorphic computing. Electric-field induced topotactic phase transition with ionic evolution represents an important pathway for this purpose, which, however, faces significant challenges in device scaling. This work demonstrates a convenient scanning-probe-induced proton evolution within WO₃, driving a reversible insulator-to-metal transition (IMT) at nanoscale. Specifically, the Pt-coated scanning probe serves as an efficient hydrogen catalysis probe, leading to a hydrogen spillover across the nano junction between the probe and sample surface. A positively biased voltage drives protons into the sample, while a negative voltage extracts protons out, giving rise to a reversible manipulation on hydrogenation-induced electron doping, accompanied by a dramatic resistive switching. The precise control of the scanning probe offers the opportunity to manipulate the local conductivity at nanoscale, which is further visualized through a printed portrait encoded by local conductivity. Notably, multistate resistive switching is successfully demonstrated via successive set and reset processes. Our work highlights the probe-induced hydrogen evolution as a new direction to engineer memristor at nanoscale.

Proton-Gated Synaptic Transistors, Based on an Electron-Beam Patterned Nafion Electrolyte

Neuromorphic processors using artificial neural networks are the center of attention for energy-efficient analog computing. Artificial synapses act as building blocks in such neural networks for parallel information processing and data storage. Herein we describe the fabrication of a proton-gated synaptic transistor using a Nafion electrolyte thin film, which is patterned by electron-beam lithography (EBL). The device has an active channel of indium-zinc-oxide (IZO) between the source and drain electrodes, which shows Ohmic behavior with a conductance level on the order of 100 μS. Under voltage applications to the gate electrode, the channel conductance is changed due to the injection and extraction of protons between the IZO channel and the Nafion electrolyte, emulating various synaptic functions with short-term and long-term plasticity. When positive (negative) gate voltage pulses are consecutively applied, the device exhibits long-term potentiation (depression) at the same number of steps as the number of input pulses. Based on these characteristics, an artificial neural network using this transistor shows ∼84% image recognition accuracy for handwritten digits. The subject transistor also successfully mimics paired-pulse facilitation and depression, Hebbian spike-timing-dependent plasticity, and Pavlovian associative learning followed by extinction activities. Finally, dynamical pattern image memorization is demonstrated in a 5 × 5 array of these synaptic transistors. The results indicate that EBL patternable Nafion electrolytes have great potential for use in the fabrication and circuit-level integration of synaptic devices for neuromorphic computing applications.

Reversal of Anomalous Hall Effect and Octahedral Tilting in SrRuO3 Thin Films via Hydrogen Spillover

The perovskite SrRuO₃ (SRO) is a strongly correlated oxide whose physical and structural properties are strongly intertwined. Notably, SRO is an itinerant ferromagnet that exhibits a large anomalous Hall effect (AHE) whose sign can be readily modified. Here, a hydrogen spillover method is used to tailor the properties of SRO thin films via hydrogen incorporation. It is found that the magnetization and Curie temperature of the films are strongly reduced and, at the same time, the structure evolves from an orthorhombic to a tetragonal phase as the hydrogen content is increased up to ≈0.9 H per SRO formula unit. The structural phase transition is shown, via in situ crystal truncation rod measurements, to be related to tilting of the RuO₆ octahedral units. The significant changes observed in magnetization are shown, via density functional theory (DFT), to be a consequence of shifts in the Fermi level. The reported findings provide new insights into the physical properties of SRO via tailoring its lattice symmetry and emergent physical phenomena via the hydrogen spillover technique.

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.

Nanosecond protonic programmable resistors for analog deep learning

Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of ionic transport and charge-transfer reaction rates point to operation in the nonlinear regime, where extreme electric fields are present within the solid electrolyte and its interfaces. In this work, we generated silicon-compatible nanoscale protonic programmable resistors with highly desirable characteristics under extreme electric fields. This operation regime enabled controlled shuttling and intercalation of protons in nanoseconds at room temperature in an energy-efficient manner. The devices showed symmetric, linear, and reversible modulation characteristics with many conductance states covering a 20× dynamic range. Thus, the space-time-energy performance of the all-solid-state artificial synapses can greatly exceed that of their biological counterparts.

Interfacial Resistive Switching by Multiphase Polarization in Ion-Intercalation Nanofilms

Nonvolatile resistive-switching (RS) memories promise to revolutionize hardware architectures with in-memory computing. Recently, ion-interclation materials have attracted increasing attention as potential RS materials for their ion-modulated electronic conductivity. In this Letter, we propose RS by multiphase polarization (MP) of ion-intercalated thin films between ion-blocking electrodes, in which interfacial phase separation triggered by an applied voltage switches the electron-transfer resistance. We develop an electrochemical phase-field model for simulations of coupled ion-electron transport and ion-modulated electron-transfer rates and use it to analyze the MP switching current and time, resistance ratio, and current-voltage response. The model is able to reproduce the complex cyclic voltammograms of lithium titanate (LTO) memristors, which cannot be explained by existing models based on bulk dielectric breakdown. The theory predicts the achievable switching speeds for multiphase ion-intercalation materials and could be used to guide the design of high-performance MP-based RS memories.

Ion-Movement-Based Synaptic Device for Brain-Inspired Computing

As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.

Neural Network Training With Asymmetric Crosspoint Elements

Analog crossbar arrays comprising programmable non-volatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices critically degrades the classification performance of networks trained with conventional algorithms. Here we first describe the fundamental reasons behind this incompatibility. Then, we explain the theoretical underpinnings of a novel fully-parallel training algorithm that is compatible with asymmetric crosspoint elements. By establishing a powerful analogy with classical mechanics, we explain how device asymmetry can be exploited as a useful feature for analog deep learning processors. Instead of conventionally tuning weights in the direction of the error function gradient, network parameters can be programmed to successfully minimize the total energy (Hamiltonian) of the system that incorporates the effects of device asymmetry. Our technique enables immediate realization of analog deep learning accelerators based on readily available device technologies.

Ion-Driven Electrochemical Random-Access Memory-Based Synaptic Devices for Neuromorphic Computing Systems: A Mini-Review

To enhance the computing efficiency in a neuromorphic architecture, it is important to develop suitable memory devices that can emulate the role of biological synapses. More specifically, not only are multiple conductance states needed to be achieved in the memory but each state is also analogously adjusted by consecutive identical pulses. Recently, electrochemical random-access memory (ECRAM) has been dedicatedly designed to realize the desired synaptic characteristics. Electric-field-driven ion motion through various electrolytes enables the conductance of the ECRAM to be analogously modulated, resulting in a linear and symmetric response. Therefore, the aim of this study is to review recent advances in ECRAM technology from the material and device engineering perspectives. Since controllable mobile ions play an important role in achieving synaptic behavior, the prospect and challenges of ECRAM devices classified according to mobile ion species are discussed.

Ionic Sieving Through One-Atom-Thick 2D Material Enables Analog Nonvolatile Memory for Neuromorphic Computing

The first report on ion transport through atomic sieves of atomically thin 2D material is provided to solve critical limitations of electrochemical random-access memory (ECRAM) devices. Conventional ECRAMs have random and localized ion migration paths; as a result, the analog switching efficiency is inadequate to perform in-memory logic operations. Herein ion transport path scaled down to the one-atom-thick (≈0.33 nm) hexagonal boron nitride (hBN), and the ionic transport area is confined to a small pore (≈0.3 nm² ) at the single-hexagonal ring. One-atom-thick hBN has ion-permeable pores at the center of each hexagonal ring due to weakened electron cloud and highly polarized B-N bond. The experimental evidence indicates that the activation energy barrier for H⁺ ion transport through single-layer hBN is ≈0.51 eV. Benefiting from the controlled ionic sieving through single-layer hBN, the ECRAMs exhibit superior nonvolatile analog switching with good memory retention and high endurance. The proposed approach enables atomically thin 2D material as an ion transport layer to regulate the switching of various ECRAM devices for artificial synaptic electronics.

CMOS-Compatible Protonic Programmable Resistor Based on Phosphosilicate Glass Electrolyte for Analog Deep Learning

Ion intercalation based programmable resistors have emerged as a potential next-generation technology for analog deep-learning applications. Proton, being the smallest ion, is a very promising candidate to enable devices with high modulation speed, low energy consumption, and enhanced endurance. In this work, we report on the first back-end CMOS-compatible nonvolatile protonic programmable resistor enabled by the integration of phosphosilicate glass (PSG) as the proton solid electrolyte layer. PSG is an outstanding solid electrolyte material that displays both excellent protonic conduction and electronic insulation characteristics. Moreover, it is a well-known material within conventional Si fabrication, which enables precise deposition control and scalability. Our scaled all-solid-state three-terminal devices show desirable modulation characteristics in terms of symmetry, retention, endurance, and energy efficiency. Protonic programmable resistors based on phosphosilicate glass, therefore, represent promising candidates to realize nanoscale analog crossbar processors for monolithic CMOS integration.

Enhancement of Synaptic Characteristics Achieved by the Optimization of Proton-Electron Coupling Effect in a Solid-State Electrolyte-Gated Transistor

Presently, the 3-terminal artificial synapse device has been in focus for neuromorphic computing systems owing to its excellent weight controllability. Here, an artificial synapse device based on the 3-terminal solid-state electrolyte-gated transistor is proposed to achieve outstanding synaptic characteristics with a human-like mechanism at low power. Novel synaptic characteristics are accomplished by precisely tuning the threshold voltage using the proton-electron coupling effect, which is caused by proton migration inside the electrolyte. However, these synaptic characteristics are degraded because traps at the interface of channel/electrolyte disturb the proton-electron coupling effect. To minimize degradation, the oxygen plasma treatment is performed to reduce interface traps. As a result, symmetric weight updates and outstanding synaptic characteristics are achieved. Furthermore, high repeatability and long-term plasticity are observed at low operating power, which is essential for artificial synapses. Therefore, this study shows the progress of artificial synapses and proposes a promising method, a low-power neuromorphic system, to achieve high accuracy.

Vacancy diffusion in palladium hydrides

The self-diffusion coefficients of palladium in PdHx (x = 0, 0.25, 0.5, 0.75, 1) were studied using density functional theory to obtain the required thermodynamic and kinetic parameters. The enthalpy of migration decreased from 0.95 eV for Pd to 0.78 eV for PdH. The enthalpy of vacancy formation exhibited a substantial decrease from about 1.1 eV in Pd to 0.4 eV in PdH, which was ascribed to successive filling of antibonding states weakening the Pd-Pd bonds. Concurrently, the Arrhenius pre-exponential was significantly reduced from 4.75 × 10-3 cm2 s-1 for Pd to 5.67 × 10-9 cm2 s-1 for PdH due to softening of the vibrational modes that determine the entropy of vacancy formation and initial/transition state frequencies. A linear correlation between the logarithm of the pre-exponential and the activation energy was interpreted as enthalpy-entropy compensation (Meyer-Neldel rule). The Pd self-diffusion coefficients in the hydrides were within 1 order of magnitude of that in pure palladium above 200 °C for hydrogen pressures up to at least 107 Pa.

In situ Parallel Training of Analog Neural Network Using Electrochemical Random-Access Memory

In-memory computing based on non-volatile resistive memory can significantly improve the energy efficiency of artificial neural networks. However, accurate in situ training has been challenging due to the nonlinear and stochastic switching of the resistive memory elements. One promising analog memory is the electrochemical random-access memory (ECRAM), also known as the redox transistor. Its low write currents and linear switching properties across hundreds of analog states enable accurate and massively parallel updates of a full crossbar array, which yield rapid and energy-efficient training. While simulations predict that ECRAM based neural networks achieve high training accuracy at significantly higher energy efficiency than digital implementations, these predictions have not been experimentally achieved. In this work, we train a 3 × 3 array of ECRAM devices that learns to discriminate several elementary logic gates (AND, OR, NAND). We record the evolution of the network's synaptic weights during parallel in situ (on-line) training, with outer product updates. Due to linear and reproducible device switching characteristics, our crossbar simulations not only accurately simulate the epochs to convergence, but also quantitatively capture the evolution of weights in individual devices. The implementation of the first in situ parallel training together with strong agreement with simulation results provides a significant advance toward developing ECRAM into larger crossbar arrays for artificial neural network accelerators, which could enable orders of magnitude improvements in energy efficiency of deep neural networks.

An Artificial Electrical-Chemical Mixed Synapse Based on Ion-Gated MoS2 Nanosheets for Real-Time Facilitation Index Tuning

Tunability of facilitation in short-term memory (STM) provides great potential in bioinspired computing. Recently, several doping strategies were proposed to modify the intrinsic features of materials, resulting in the optimization of the facilitation index (FI). However, real-time scale tuning, which is implemented on the same synaptic device, has not yet been demonstrated. Inspired by the chemical-electrical mixed synapse structure in the brain, we propose a three-terminal artificial synapse based on an ion-gated MoS₂ memristor. The gate terminal serves as a nonvolatile ionic pump via chemical intercalation, which effectively affects both the conductance baseline and the hysteresis degree of the STM effect of the memristor. We further modeled the postsynaptic current (PSC) behavior and used it for reservoir computing. Simulation results show that, due to the real-time tuning ability, the built reservoir can be programmed for specific handwritten recognition tasks with the pruning of neurons from 784 to 50. The developed artificial mixed synapse is promising for a downsampling module in neural network design.

Reversible Rapid Hydrogen Doping of WO3 in Non-Acid Solution

Hydrogenation, an effective way to tune the properties of transition metal oxide (TMO) thin films, has been long awaited to be performed safely and without an external energy input. Recently, metal-acid-TMO has been reported to be an effective approach for hydrogenation, but the requirement of acid limits its application. In this work, the reversible and rapid hydrogen doping of WO₃ in NaOH(aq) | Al(s) | WO₃(s) is revealed by structural and electrical measurements. Accompanied by the structural phase transition identified by in situ X-ray diffraction, the electric resistance of the WO₃ film is found to be able to change by 5 orders of magnitude. A significant electrical response of touching, 8-fold in amplitude and 3 s in a cycle, can be achieved in the low-resistance state. These reactions are reversible at room temperature. This study unambiguously proves that the hydrogenation-driven dynamic phase transition of WO₃ in metal-solution-WO₃ systems could occur not only in acid solutions but also in some non-acid environments. Unlike the monotonic increase of resistance revealed during H_δWO₃ to WO₃ transition, an intriguing non-monotonic evolution was found for crystal lattice parameter c, indicating that the mechanism of WO₃ hydrogenation involves a series of metastable states, more comprehensive and reasonable. This work sheds light on the potential applications of metal-solution-TMO hydrogenation in touching sensors, circuits survey, and information storage.

Autonomous Computing Materials

Conventional materials are reaching their limits in computation, sensing, and data storage capabilities, ushered in by the end of Moore's law, myriad sensing applications, and the continuing exponential rise in worldwide data storage demand. Conventional materials are also limited by the controlled environments in which they must operate, their high energy consumption, and their limited capacity to perform simultaneous, integrated sensing, computation, and data storage and retrieval. In contrast, the human brain is capable of multimodal sensing, complex computation, and both short- and long-term data storage simultaneously, with near instantaneous rate of recall, seamless integration, and minimal energy consumption. Motivated by the brain and the need for revolutionary new computing materials, we recently proposed the data-driven materials discovery framework, autonomous computing materials. This framework aims to mimic the brain's capabilities for integrated sensing, computation, and data storage by programming excitonic, phononic, photonic, and dynamic structural nanoscale materials, without attempting to mimic the unknown implementational details of the brain. If realized, such materials would offer transformative opportunities for distributed, multimodal sensing, computation, and data storage in an integrated manner in biological and other nonconventional environments, including interfacing with biological sensors and computers such as the brain itself.

Hydrogen Control of Double Exchange Interaction in La0.67 Sr0.33 MnO3 for Ionic-Electric-Magnetic Coupled Applications

The dynamic tuning of ion concentrations has attracted significant attention for creating versatile functionalities of materials, which are impossible to reach using classical control knobs. Despite these merits, the following fundamental questions remain: how do ions affect the electronic bandstructure, and how do ions simultaneously change the electrical and magnetic properties? Here, by annealing platinum-dotted La_0.67 Sr_0.33 MnO₃ films in hydrogen and argon at a lower temperature of 200 °C for several minutes, a reversible change in resistivity is achieved by three orders of magnitude with tailored ferromagnetic magnetization. The transition occurs through the tuning of the double exchange interaction, ascribed to an electron-doping-induced and/or a lattice-expansion-induced modulation, along with an increase in the hydrogen concentration. High reproducibility, long-term stability, and multilevel linearity are appealing for ionic-electric-magnetic coupled applications.

Oxide-Based Electrolyte-Gated Transistors for Spatiotemporal Information Processing

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

Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching

Digital computing is nearing its physical limits as computing needs and energy consumption rapidly increase. Analogue-memory-based neuromorphic computing can be orders of magnitude more energy efficient at data-intensive tasks like deep neural networks, but has been limited by the inaccurate and unpredictable switching of analogue resistive memory. Filamentary resistive random access memory (RRAM) suffers from stochastic switching due to the random kinetic motion of discrete defects in the nanometer-sized filament. In this work, this stochasticity is overcome by incorporating a solid electrolyte interlayer, in this case, yttria-stabilized zirconia (YSZ), toward eliminating filaments. Filament-free, bulk-RRAM cells instead store analogue states using the bulk point defect concentration, yielding predictable switching because the statistical ensemble behavior of oxygen vacancy defects is deterministic even when individual defects are stochastic. Both experiments and modeling show bulk-RRAM devices using TiO_{2- X} switching layers and YSZ electrolytes yield deterministic and linear analogue switching for efficient inference and training. Bulk-RRAM solves many outstanding issues with memristor unpredictability that have inhibited commercialization, and can, therefore, enable unprecedented new applications for energy-efficient neuromorphic computing. Beyond RRAM, this work shows how harnessing bulk point defects in ionic materials can be used to engineer deterministic nanoelectronic materials and devices.

Reversible multicolor chromism in layered formamidinium metal halide perovskites

Metal halide perovskites feature crystalline-like electronic band structures and liquid-like physical properties. The crystal–liquid duality enables optoelectronic devices with unprecedented performance and a unique opportunity to chemically manipulate the structure with low energy input. In this work, we leverage the low formation energy of metal halide perovskites to demonstrate multicolor reversible chromism. We synthesized layered Ruddlesden-Popper FA_n+1Pb_nX_3n+1 (FA = formamidinium, X = I, Br; n = number of layers = 1, 2, 3 … ∞) and reversibly tune the dimensionality (n) by modulating the strength and number of H-bonds in the system. H-bonding was controlled by exposure to solvent vapor (solvatochromism) or temperature change (thermochromism), which shuttles FAX salt pairs between the FA_n+1Pb_nX_3n+1 domains and adjacent FAX “reservoir” domains. Unlike traditional chromic materials that only offer a single-color transition, FA_n+1Pb_nX_3n+1 films reversibly switch between multiple colors including yellow, orange, red, brown, and white/colorless. Each colored phase exhibits distinct optoelectronic properties characteristic of 2D superlattice materials with tunable quantum well thickness.

Metal halide perovskites feature crystalline-like electronic band structures and liquid-like physical properties that allow chemical manipulation of the structure with low energy input. Here, the authors leverage the low formation energy of 2D metal halide perovskites to demonstrate films that reversibly switch between multiple colors using tunable quantum well thickness.