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

CMOS-Compatible Protonic Three-Terminal Memristor for Analog Synapse in Neuromorphic Computing

All-solid-state inorganic hydrogen-based three-terminal memristors (H-3TMs) suffer from poor retention, susceptibility to humidity and temperature, and the reliance on wet chemistry during fabrication, hindering their manufacturability within existing foundry processes. To address these, this study presents a CMOS-compatible H-3TM based on reversible intercalation and extraction of protons between the SiNx electrolyte and WOx channel. The protons are introduced via a straightforward hydrogen plasma treatment, promoting a compatible fabrication process with back-end-of-line integration. Experimental and simulation results indicate that the low proton transport tendency across the electrolyte/channel interface without an external electric field contributes to high retention performance. Furthermore, the device demonstrates linear potentiation and depression, 512 conductance states with a dynamic range of ≈40, low energy operation (≈73 fJ per write), and excellent overall device-to-device variation. Its analog properties are evaluated under the training and inference framework of MNIST and Fashion-MNIST datasets. The device achieved training and inference accuracies only 0.4% and 0.3% below the ideal benchmark on the F-MNIST dataset. This work offers a rational approach for future artificial synaptic device design and fabrication.

Electrochemical Random-Access Memory: Progress, Perspectives, and Opportunities

Non-von Neumann computing using neuromorphic systems based on analogue synaptic and neuronal elements has emerged as a potential solution to tackle the growing need for more efficient data processing, but progress toward practical systems has been stymied due to a lack of materials and devices with the appropriate attributes. Recently, solid state electrochemical ion-insertion, also known as electrochemical random access memory (ECRAM) has emerged as a promising approach to realize the needed device characteristics. ECRAM is a three terminal device that operates by tuning electronic conductance in functional materials through solid-state electrochemical redox reactions. This mechanism can be considered as a gate-controlled bulk modulation of dopants and/or phases in the channel. Early work demonstrating that ECRAM can achieve nearly ideal analogue synaptic characteristics has sparked tremendous interest in this approach. More recently, the realization that electrochemical ion insertion can be used to tune the electronic properties of many types of materials including transition metal oxides, layered two-dimensional materials, organic and coordination polymers, and that the changes in conductance can span orders of magnitude has further attracted interest in ECRAM as the basis for analogue synaptic elements for inference accelerators as well as for dynamical devices that can emulate a wide range of neuronal characteristics for implementation in analogue spiking neural networks. At its core, ECRAM shares many fundamental aspects with rechargeable batteries, where ion insertion materials are used extensively for their ability to reversibly store charge and energy. Computing applications, however, present drastically different requirements: systems will require many millions of devices, scaled down to tens of nanometers, all while achieving reliable electronic-state tuning at scaled-up rates and endurances, and with minimal energy dissipation and noise. In this review, we discuss the history, basic concepts, recent progress, as well as the challenges and opportunities for different types of ECRAM, broadly grouped by their primary mobile ionic charge carrier, including Li, protons, and oxygen vacancies.

Solid-State Oxide-Ion Synaptic Transistor for Neuromorphic Computing

Neuromorphic hardware facilitates rapid and energy-efficient training and operation of neural network models for artificial intelligence. However, existing analog in-memory computing devices, like memristors, continue to face significant challenges that impede their commercialization. These challenges include high variability due to their stochastic nature. Microfabricated electrochemical synapses offer a promising approach by functioning as an analog programmable resistor based on deterministic ion-insertion mechanisms. Here, an all-solid-state oxide-ion synaptic transistor is developed, employing Bi2V0.9Cu0.1O5.35 as a superior oxide-ion conductor electrolyte and La0.5Sr0.5FeO3-δ as a variable-resistance channel able to efficiently operate at temperatures compatible with conventional electronics. This transistor exhibits essential synaptic behaviors such as long- and short-term potentiation, paired-pulse facilitation, and post-tetanic potentiation, mimicking fundamental properties of biological neural networks. Key criteria for efficient neuromorphic computing are satisfied, including excellent linear and symmetric synaptic plasticity, low energy consumption per programming pulse, and high endurance with minimal cycle-to-cycle variation. Integrated into an artificial neural network (ANN) simulation for handwritten digit recognition, the presented synaptic transistor achieved a 96% accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, illustrating the effective implementation of the device in ANNs. These findings demonstrate the potential of oxide-ion based synaptic transistors for effective implementation in analog neuromorphic computing based on iontronics.

Materials Horizons Emerging Investigator Series: Professor Yiyang Li, University of Michigan, USA

Our Emerging Investigator Series features exceptional work by early-career researchers working in the field of materials science.

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.

Artificial Neuron Devices

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

Redox-based ion-gating reservoir consisting of (104) oriented LiCoO2 film, assisted by physical masking

Reservoir computing (RC) is a machine learning framework suitable for processing time series data, and is a computationally inexpensive and fast learning model. A physical reservoir is a hardware implementation of RC using a physical system, which is expected to become the social infrastructure of a data society that needs to process vast amounts of information. Ion-gating reservoirs (IGR) are compact and suitable for integration with various physical reservoirs, but the prediction accuracy and operating speed of redox-IGRs using WO3 as the channel are not sufficient due to irreversible Li+ trapping in the WO3 matrix during operation. Here, in order to enhance the computation performance of redox-IGRs, we developed a redox-based IGR using a (104) oriented LiCoO2 thin film with high electronic and ionic conductivity as a trap-free channel material. The subject IGR utilizes resistance change that is due to a redox reaction (LiCoO2 ⟺ Li1-xCoO2 + xLi+ + xe-) with the insertion and desertion of Li+. The prediction error in the subject IGR was reduced by 72% and the operation speed was increased by 4 times compared to the previously reported WO3, which changes are due to the nonlinear and reversible electrical response of LiCoO2 and the high dimensionality enhanced by a newly developed physical masking technique. This study has demonstrated the possibility of developing high-performance IGRs by utilizing materials with stronger nonlinearity and by increasing output dimensionality.

Fe Site Order and Magnetic Properties of Fe1/4NbS2

Transition-metal dichalcogenides (TMDs) have long been attractive to researchers for their diverse properties and high degree of tunability. Most recently, interest in magnetically intercalated TMDs has resurged due to their potential applications in spintronic devices. While certain compositions featuring the absence of inversion symmetry such as Fe1/3NbS2 and Cr1/3NbS2 have garnered the most attention, the diverse compositional space afforded through the host matrix composition as well as intercalant identity and concentration is large and remains relatively underexplored. Here, we report the magnetic ground state of Fe1/4NbS2 that was determined from low-temperature neutron powder diffraction as an A-type antiferromagnet. Despite the presence of overall inversion symmetry, the pristine compound manifests spin polarization induced by the antiferromagnetic order at generic k points, based on density functional theory band-structure calculations. Furthermore, by combining synchrotron diffraction, pair distribution function, and magnetic susceptibility measurements, we find that the magnetic properties of Fe1/4NbS2 are sensitive to the Fe site order, which can be tuned via electrochemical lithiation and thermal history.

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.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

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

ECRAM Materials, Devices, Circuits and Architectures: A Perspective

Non-von-Neumann computing using neuromorphic systems based on two-terminal resistive nonvolatile memory elements has emerged as a promising approach, but its full potential has not been realized due to the lack of materials and devices with the appropriate attributes. Unlike memristors, which require large write currents to drive phase transformations or filament growth, electrochemical random access memory (ECRAM) decouples the "write" and "read" operations using a "gate" electrode to tune the conductance state through charge-transfer reactions, and every electron transferred through the external circuit in ECRAM corresponds to the migration of ≈1 ion used to store analogue information. Like static dopants in traditional semiconductors, electrochemically inserted ions modulate the conductivity by locally perturbing a host's electronic structure; however, ECRAM does so in a dynamic and reversible manner. The resulting change in conductance can span orders of magnitude, from gradual increments needed for analog elements, to large, abrupt changes for dynamically reconfigurable adaptive architectures. In this in-depth perspective, the history of ECRAM, the recent progress in devices spanning organic, inorganic, and 2D materials, circuits, architectures, the rich portfolio of challenging, fundamental questions, and how ECRAM can be harnessed to realize a new paradigm for low-power neuromorphic computing are discussed.

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.

Tunable Intervalence Charge Transfer in Ruthenium Prussian Blue Analog Enables Stable and Efficient Biocompatible Artificial Synapses

Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. This report expands the materials playground for neuromorphic devices to include a mixed valence inorganic 3D coordination framework, a ruthenium Prussian blue analog (RuPBA), for flexible and biocompatible artificial synapses that reversibly switch conductance by more than four orders of magnitude based on electrochemically tunable oxidation state. The electrochemically tunable degree of mixed valency and electronic coupling between N-coordinated Ru sites controls the carrier concentration and mobility, as supported by density functional theory computations and application of electron transfer theory to in situ spectroscopy of intervalence charge transfer. Retention of programmed states is improved by nearly two orders of magnitude compared to extensively studied organic polymers, thus reducing the frequency, complexity, and energy costs associated with error correction schemes. This report demonstrates dopamine-mediated plasticity of RuPBA synapses and biocompatibility of RuPBA with neuronal cells, evoking prospective application for brain-computer interfacing.

Li iontronics in single-crystalline T-Nb2O5 thin films with vertical ionic transport channels

The niobium oxide polymorph T-Nb2O5 has been extensively investigated in its bulk form especially for applications in fast-charging batteries and electrochemical (pseudo)capacitors. Its crystal structure, which has two-dimensional (2D) layers with very low steric hindrance, allows for fast Li-ion migration. However, since its discovery in 1941, the growth of single-crystalline thin films and its electronic applications have not yet been realized, probably due to its large orthorhombic unit cell along with the existence of many polymorphs. Here we demonstrate the epitaxial growth of single-crystalline T-Nb2O5 thin films, critically with the ionic transport channels oriented perpendicular to the film's surface. These vertical 2D channels enable fast Li-ion migration, which we show gives rise to a colossal insulator-metal transition, where the resistivity drops by 11 orders of magnitude due to the population of the initially empty Nb 4d0 states by electrons. Moreover, we reveal multiple unexplored phase transitions with distinct crystal and electronic structures over a wide range of Li-ion concentrations by comprehensive in situ experiments and theoretical calculations, which allow for the reversible and repeatable manipulation of these phases and their distinct electronic properties. This work paves the way for the exploration of novel thin films with ionic channels and their potential applications.

Hafnium Oxide (HfO2 ) - A Multifunctional Oxide: A Review on the Prospect and Challenges of Hafnium Oxide in Resistive Switching and Ferroelectric Memories

Hafnium oxide (HfO₂ ) is one of the mature high-k dielectrics that has been standing strong in the memory arena over the last two decades. Its dielectric properties have been researched rigorously for the development of flash memory devices. In this review, the application of HfO₂ in two main emerging nonvolatile memory technologies is surveyed, namely resistive random access memory and ferroelectric memory. How the properties of HfO₂ equip the former to achieve superlative performance with high-speed reliable switching, excellent endurance, and retention is discussed. The parameters to control HfO₂ domains are further discussed, which can unleash the ferroelectric properties in memory applications. Finally, the prospect of HfO₂ materials in emerging applications, such as high-density memory and neuromorphic devices are examined, and the various challenges of HfO₂ -based resistive random access memory and ferroelectric memory devices are addressed with a future outlook.

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.

Strategies to Improve the Synaptic Characteristics of Oxygen-Based Electrochemical Random-Access Memory Based on Material Parameters Optimization

Oxygen-based electrochemical random-access memories (O-ECRAMs) are promising synaptic devices for neuromorphic applications because of their near-ideal synaptic characteristics and compatibility with complementary metal-oxide-semiconductor processes. However, the correlation between material parameters and synaptic properties of O-ECRAM devices has not yet been elucidated. Here, we propose the critical design parameters to fabricate an ideal ECRAM device. Based on the experimental data and simulation results, it is revealed that consistent ion supply from the electrolyte and rapid ion diffusion in the channel are critical factors for ideal synaptic characteristics. To optimize these parameters, crystalline WO_2.7 exhibiting fast ion diffusivity and ZrO_1.7 exhibiting an appropriate ion conduction energy barrier (1.1 eV) are used as a channel and an electrolyte, respectively. As a result, synaptic characteristics with near-ideal weight-update linearity in the nanosiemens conductance range are achieved. Finally, a selector-less O-ECRAM device is integrated into a 2 × 2 array, and high recognition accuracy (94.83%) of the Modified National Institute of Standards and Technology pattern is evaluated.

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.

Highly Efficient Uniaxial In-Plane Stretching of a 2D Material via Ion Insertion

On-chip dynamic strain engineering requires efficient micro-actuators that can generate large in-plane strains. Inorganic electrochemical actuators are unique in that they are driven by low voltages (≈1 V) and produce considerable strains (≈1%). However, actuation speed and efficiency are limited by mass transport of ions. Minimizing the number of ions required to actuate is thus key to enabling useful "straintronic" devices. Here, it is shown that the electrochemical intercalation of exceptionally few lithium ions into WTe₂ causes large anisotropic in-plane strain: 5% in one in-plane direction and 0.1% in the other. This efficient stretching of the 2D WTe₂ layers contrasts to intercalation-induced strains in related materials which are predominantly in the out-of-plane direction. The unusual actuation of Li_x WTe₂ is linked to the formation of a newly discovered crystallographic phase, referred to as Td', with an exotic atomic arrangement. On-chip low-voltage (<0.2 V) control is demonstrated over the transition to the novel phase and its composition. Within the Td'-Li_{0.5- δ} WTe₂ phase, a uniaxial in-plane strain of 1.4% is achieved with a change of δ of only 0.075. This makes the in-plane chemical expansion coefficient of Td'-Li_0.5-δ WTe₂ far greater than of any other single-phase material, enabling fast and efficient planar electrochemical actuation.

Improved synaptic functionalities of Li-based nano-ionic synaptic transistor with ultralow conductance enabled by Al2O3barrier layer

In this study, we investigated the effect of an Al₂O₃barrier layer in an all-solid-state inorganic Li-based nano-ionic synaptic transistor (LST) with Li₃PO₄electrolyte/WO_xchannel structure. Near-ideal synaptic behavior in the ultralow conductance range (∼50 nS) was obtained by controlling the abrupt ion migration through the introduction of a sputter-deposited thin (∼3 nm) Al₂O₃interfacial layer. A trade-off relationship between the weight update linearity and on/off ratio with varying Al₂O₃layer thickness was also observed. To determine the origin of the Al₂O₃barrier layer effects, cyclic voltammetry analysis was conducted, and the optimal ionic diffusivity and mobility were found to be key parameters in achieving ideal synaptic behavior. Owing to the controlled ion migration, the retention characteristics were considerably improved by the Al₂O₃barrier. Finally, a highly improved pattern recognition accuracy (83.13%) was achieved using the LST with an Al₂O₃barrier of optimal thickness.

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.

Sodium-based nano-ionic synaptic transistor with improved retention characteristics

We propose an all-solid-state Na ion-based synaptic transistor (NST) to overcome the low retention problem of the Li ion-based synaptic transistor (LST). Through our analysis, it was found that the retention instability in an ionic synaptic transistor originated from its high ionic diffusivity. As confirmed by cyclic voltammetry analysis, Na ions have a lower ionic diffusivity than Li ions in the WO_x layer. The state retention of NST was found to be improved to 20 times that of LST. Furthermore, near-ideal synaptic behaviors, such as linear weight update and linear I-V characteristics, were also obtained by material engineering.

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.

Surface-Dependent Performance of Ultrathin TiN Films as an Electrically Conducting Li Diffusion Barrier for Li-Ion-Based Devices

An in-depth understanding of lithium (Li) diffusion barriers is a crucial factor for enabling Li-ion-based devices such as three-dimensional (3D) thin-film batteries and synaptic redox transistors integrated on silicon substrates. Diffusion of Li ions into silicon can damage the surrounding components, detach the device itself, lead to battery capacity loss, and cause an uncontrolled change of the transistor channel conductance. In this study, we analyze for the first time ultrathin 10 nm titanium nitride (TiN) films as a bifunctional Li-ion diffusion barrier and current collector. Thermal atomic layer deposition (ALD) and pulsed chemical vapor deposition (pCVD) are employed for manufacturing ultrathin films. The 10 nm ALD films demonstrate excellent blocking capability with an insertion of only 0.03 Li per TiN formula unit exceeding 200 galvanostatic cycles at 3 μA/cm² between 0.05 and 3 V versus Li/Li⁺. An ultralow electrical resistivity of 115 μΩ cm is obtained. In contrast, a partial barrier breakdown is observed for 10 nm pCVD films. High surface quality with low contamination is identified as a key factor for the excellent performance of ALD TiN. Conformal deposition of 10 nm ALD TiN in 3D structures with high aspect ratios of up to 20:1 is demonstrated. The measured capacities of the surface area-enhanced samples are in good agreement with the expected values. High-temperature blocking capability is proven for a typical electrode crystallization step. Ultrathin ALD TiN is an ideal candidate for an electrically conducting Li-ion diffusion barrier for Si-integrated devices.

Synaptic Characteristics of Amorphous Boron Nitride-Based Memristors on a Highly Doped Silicon Substrate for Neuromorphic Engineering

In this study, the resistive switching and synaptic properties of a complementary metal-oxide semiconductor-compatible Ti/a-BN/Si device are investigated for neuromorphic systems. A gradual change in resistance is observed in a positive SET operation in which Ti diffusion is involved in the conducting path. This operation is extremely suitable for synaptic devices in hardware-based neuromorphic systems. The isosurface charge density plots and experimental results confirm that boron vacancies can help generate a conducting path, whereas the conducting path generated by a Ti cation from interdiffusion forms is limited. A negative SET operation causes a considerable decrease in the formation energy of only boron vacancies, thereby increasing the conductivity in the low-resistance state, which may be related to RESET failure and poor endurance. The pulse transient characteristics, potentiation and depression characteristics, and good retention property of eight multilevel cells also indicate that the positive SET operation is more suitable for a synaptic device owing to the gradual modulation of conductance. Moreover, pattern recognition accuracy is examined by considering the conductance values of the measured data in the Ti/a-BN/Si device as the synaptic part of a neural network. The linear and symmetric synaptic weight update in a positive SET operation with an incremental voltage pulse scheme ensures higher pattern recognition accuracy.

Temperature-resilient solid-state organic artificial synapses for neuromorphic computing

Devices with tunable resistance are highly sought after for neuromorphic computing. Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance tuning and excessive write noise, degrading artificial neural network (ANN) accelerator performance. Emerging electrochemical random-access memories (ECRAMs) display write linearity, which enables substantially faster ANN training by array programing in parallel. However, state-of-the-art ECRAMs have not yet demonstrated stable and efficient operation at temperatures required for packaged electronic devices (~90°C). Here, we show that (semi)conducting polymers combined with ion gel electrolyte films enable solid-state ECRAMs with stable and nearly temperature-independent operation up to 90°C. These ECRAMs show linear resistance tuning over a >2× dynamic range, 20-nanosecond switching, submicrosecond write-read cycling, low noise, and low-voltage (±1 volt) and low-energy (~80 femtojoules per write) operation combined with excellent endurance (>10⁹ write-read operations at 90°C). Demonstration of these high-performance ECRAMs is a fundamental step toward their implementation in hardware ANNs.

Excellent synaptic behavior of lithium-based nano-ionic transistor based on optimal WO2.7 stoichiometry with high ion diffusivity

In this study, we introduce a lithium (Li) ion-based three-terminal (3-T) synapse device using WO _x as a channel. Our study reveals a key stoichiometry of WO_2.7 for excellent synaptic characteristics that is related to Li-ion diffusivity. The open-lattice structure formed by oxygen deficiency promoted Li-ion injection and diffusion. The optimized stoichiometry and improved Li-ion diffusivity were confirmed by x-ray photoelectron spectroscopy analysis and cyclic voltammetry, respectively. Furthermore, the transient conductance change that inevitably occurs in ion-based synaptic transistors was resolved by applying a two-step voltage pulse scheme. As a result, we achieved a symmetric and linear weight-update characteristic with reduced program/erase operation time.

Near ideal synaptic functionalities in Li ion synaptic transistor using Li3POxSex electrolyte with high ionic conductivity

All solid-state lithium-ion transistors are considered as promising synaptic devices for building artificial neural networks for neuromorphic computing. However, the slow ionic conduction in existing electrolytes hinders the performance of lithium-ion-based synaptic transistors. In this study, we systematically explore the influence of ionic conductivity of electrolytes on the synaptic performance of ionic transistors. Isovalent chalcogenide substitution such as Se in Li₃PO₄ significantly reduces the activation energy for Li ion migration from 0.35 to 0.253 eV, leading to a fast ionic conduction. This high ionic conductivity allows linear conductance switching in the LiCoO₂ channel with several discrete nonvolatile states and good retention for both potentiation and depression steps. Consequently, optimized devices demonstrate the smallest nonlinearity ratio of 0.12 and high on/off ratio of 19. However, Li₃PO₄ electrolyte (with lower ionic conductivity) shows asymmetric and nonlinear weight-update characteristics. Our findings show that the facilitation of Li ionic conduction in solid-state electrolyte suggests potential application in artificial synapse device development.