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

Strategic Material Design for Highly Reliable QLC 3D V-NAND Using Bypass Resistive Random Access Memory

To overcome the limitation of conventional flash memory, electrochemical random-access memory (ECRAM)-based bypass memory (bypass RRAM) has been proposed as a potential candidate for V-NAND memory application. While bypass RRAM demonstrates excellent memory characteristics through ion hopping conduction, the key parameters governing multilevel cell (MLC) operation remain unexplored. In this study, we propose design guidelines for bypass RRAM, targeting highly uniform quadruple-level cell (QLC) operation by using quantized oxygen vacancy (Vo) injections. To achieve the uniform QLC operation, we precisely controlled ion migration using material engineering in the bypass RRAM. By leveraging the unique electrical characteristic of the WOx resistive switching (RS) layer, we minimized Vo migration (from WO2.65 to WO2.73), which enabled low-voltage operation (<5 V) and a significant on/off ratio (>106) with a minimal stoichiometry (Δx < 0.08) change. Additionally, key parameters, such as ionic barrier (Ea,ion) in the electrolyte layer and ion diffusivity (Dion) in the RS layer, were identified to achieve both a high on/off ratio and a uniform sensing margin based on MATLAB simulations and experimental results. As a result, optimized parameters led to superior QLC performance, featuring a highly uniform distribution (σ/μ ∼ 0.01) and a uniform sensing margin (ΔG ∼ 4 μS) between each state without read disturbance issues. Finally, we also confirmed that the substantial reduction of the Vo migration at the nanometer scale suggests the potential for extending beyond QLC levels with quantized Vo injection, ensuring highly uniform switching for V-NAND memory.

Artificial Synaptic Properties in Oxygen-Based Electrochemical Random-Access Memory with CeO2 Nanoparticle Assembly as Gate Insulator for Neuromorphic Computing

Beyond the von Neumann architecture, neuromorphic computing attracts considerable attention as an energy-efficient computing system for data-centric applications. Among various synapse device candidates, a memtransistor with a three-terminal structure has been considered to be a promising one for artificial synapse with controllable weight update characteristics and strong immunity to disturbance due to decoupled write and read electrode. In this study, oxygen ion exchange-based electrochemical random-access memory consisting of the ZnO channel and CeO2 nanoparticle (NP) assembly as a gate insulator, also as an ion exchange layer, is proposed and investigated as an artificial synapse device for neuromorphic computing. The memtransistor shows a tunable and reversible conductance change via oxygen ion exchange between ZnO and CeO2 NPs upon gate voltage application. The use of CeO2 enables efficient oxygen ion exchange with the ZnO channel due to its inherent property of easily absorbing and releasing oxygen ions by altering the valence state of the Ce cation. Additionally, the porous structure of the CeO2 NP assembly supports the oxygen reservoir function while retaining its insulating properties as a gate insulator, ensuring reliable device operation. Also, its porous nature enhancing oxygen ion exchange permits high-speed operation within tens of microsecond range. Based on the facilitated oxygen ion exchange, a highly linear and symmetric conductance modulation is achieved with good endurance over 104 pulses and excellent nonvolatile retention. Furthermore, the memtransistor mimics representative functions of the biological synapse such as paired-pulse facilitation, short-term (STP) and long-term plasticity (LTP), and the transition from STP to LTP as repeating learning cycles.

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.

Improvement of the weight update and retention characteristics of Pr0.7Ca0.3MnO3-x ECRAM via elevated temperature training

To achieve both excellent analog switching for training and retention for inference simultaneously, we investigated elevated-temperature (ET) training of Pr0.7Ca0.3MnO3-x (PCMO) electrochemical random access memory (ECRAM). Improved weight update characteristics can be obtained by thermally reduced ionic resistivity of the HfOx electrolyte at ET (413 K). Furthermore, excellent retention characteristics (108 s) were observed at room temperature, which can be explained by enhanced ion storage within the reservoir (or channel) layer via ET training. By adopting ET training on PCMO ECRAM, we can achieve both training and inference accuracy of neural networks (NNs).

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

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

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

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

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.

Analysis of electro-chemical RAM synaptic array for energy-efficient weight update

While electro-chemical RAM (ECRAM)-based cross-point synaptic arrays are considered to be promising candidates for energy-efficient neural network computational hardware, array-level analyses to achieve energy-efficient update operations have not yet been performed. In this work, we fabricated CuOx/HfOx/WOx ECRAM arrays and demonstrated linear and symmetrical weight update capabilities in both fully parallel and sequential update operations. Based on the experimental measurements, we showed that the source-drain leakage current (ISD) through the unselected ECRAM cells and resultant energy consumption—which had been neglected thus far—contributed a large portion to the total update energy. We showed that both device engineering to reduce ISD and the selection of an update scheme—for example, column-by-column—that avoided ISD intervention via unselected cells were key to enable energy-efficient neuromorphic computing.