Electrochemical Ionic Synapses: Progress and Perspectives

Bio-Plausible Multimodal Learning with Emerging Neuromorphic Devices

Multimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans. However, this approach requires simultaneous processing of diverse types of data, leading to increased model complexity, longer training times, and higher energy consumption. Multimodal neuromorphic devices have the capability to preprocess spatio-temporal information from various physical signals into unified electrical signals with high information density, thereby enabling more biologically plausible multimodal learning with low complexity and high energy-efficiency. Here, this work conducts a comparison between the expression of multimodal machine learning and multimodal neuromorphic computing, followed by an overview of the key characteristics associated with multimodal neuromorphic devices. The bio-plausible operational principles and the multimodal learning abilities of emerging devices are examined, which are classified into heterogeneous and homogeneous multimodal neuromorphic devices. Subsequently, this work provides a detailed description of the multimodal learning capabilities demonstrated by neuromorphic circuits and their respective applications. Finally, this work highlights the limitations and challenges of multimodal neuromorphic computing in order to hopefully provide insight into potential future research directions.

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.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

Voltage Control of Multiple Electrochemical Processes during Lithium Ion Migration in NiFe2O4 Ferrite

Li-ion-based electric field control has been attracting significant attention, since it is able to penetrate deep into materials to exhibit diverse and controllable electrochemical processes, which offer more degrees of freedom to design multifunctional devices with low power consumption. As opposed to previous studies that mainly focused on single lithiation/delithiation mechanisms, we reveal three Li-ion modulation mechanisms in the same NiFe2O4 spinel ferrite by in situ magnetometry, i.e., intercalation, conversion, and space charge, which are respectively demonstrated in high, medium, and low voltage range. During the intercalation stage, the spinel structure is preserved, and a reversible modulation of magnetization arises from the charge transfer-induced variation of Fe valence states (Fe2+/Fe3+). Conversion-driven change in magnetization is the largest up to 89 emu g-1, due to the structural and magnetic phase transitions. Although both intercalation and conversion exhibit sluggish kinetics and long response times, the space charge manifests a faster switching speed and superior durability due to its interface electrostatic effect. These results not only provide a clear and comprehensive understanding on Li-based modulation mechanisms but also facilitate multifunctional and multiscenario applications, such as multistate memory, micromagnetic actuation, artificial synapse, and energy storage.

How to Correctly Analyze 2p X-ray Photoelectron Spectra of 3d Transition-Metal Oxides: Pitfalls and Principles

Nanomaterials based on transition-metal oxides (TMOs) that contain late 3d transition metals (e.g., Mn, Fe, Co, Ni) have diverse properties and functionality that are related to the oxidation state of constituent transition-metal (TM) cations. X-ray photoelectron spectroscopy (XPS) of TM 2p orbitals has been widely used to quantify the TM oxidation state of TMOs. However, 2p XPS spectra of late 3d TM cations usually have complicated shapes due to the charge transfer between the TM cation and oxygen ligands (anions), which makes the analysis highly nontrivial. In this article, we will examine the validity of commonly used analysis methods based on either peak fitting or the shift of binding energy (BE). The different origins of the BE shift in XPS spectra will be discussed. We will then introduce a model to reproduce the complex shapes of TM 2p spectra, based on fundamental parameters that describe the TMO electronic structure.

Ultrathin All-Solid-State MoS2-Based Electrolyte Gated Synaptic Transistor with Tunable Organic-Inorganic Hybrid Film

Electrolyte-gated synaptic transistors (EGSTs) have attracted considerable attention as synaptic devices owing to their adjustable conductance, low power consumption, and multi-state storage capabilities. To demonstrate high-density EGST arrays, 2D materials are recommended owing to their excellent electrical properties and ultrathin profile. However, widespread implementation of 2D-based EGSTs has challenges in achieving large-area channel growth and finding compatible nanoscale solid electrolytes. This study demonstrates large-scale process-compatible, all-solid-state EGSTs utilizing molybdenum disulfide (MoS2) channels grown through chemical vapor deposition (CVD) and sub-30 nm organic-inorganic hybrid electrolyte polymers synthesized via initiated chemical vapor deposition (iCVD). The iCVD technique enables precise modulation of the hydroxyl group density in the hybrid matrix, allowing the modulation of proton conduction, resulting in adjustable synaptic performance. By leveraging the tunable iCVD-based hybrid electrolyte, the fabricated EGSTs achieve remarkable attributes: a wide on/off ratio of 109, state retention exceeding 103, and linear conductance updates. Additionally, the device exhibits endurance surpassing 5 × 104 cycles, while maintaining a low energy consumption of 200 fJ/spike. To evaluate the practicality of these EGSTs, a subset of devices is employed in system-level simulations of MNIST handwritten digit recognition, yielding a recognition rate of 93.2%.

Ultrahigh Oxygen Ion Mobility in Ferroelectric Hafnia

Ferroelectrics and ionic conductors are important functional materials, each supporting a plethora of applications in information and energy technology. The underlying physics governing their functional properties is ionic motion, and yet studies of ferroelectrics and ionic conductors are often considered separate fields. Based on first-principles calculations and deep-learning-assisted large-scale molecular dynamics simulations, we report ferroelectric-switching-promoted oxygen ion transport in HfO_{2}, a wide-band-gap insulator with both ferroelectricity and ionic conductivity. Applying a unidirectional bias can activate multiple switching pathways in ferroelectric HfO_{2}, leading to polar-antipolar phase cycling that appears to contradict classical electrodynamics. This apparent conflict is resolved by the geometric-quantum-phase nature of electric polarization that carries no definite direction. Our molecular dynamics simulations demonstrate bias-driven successive ferroelectric transitions facilitate ultrahigh oxygen ion mobility at moderate temperatures, highlighting the potential of combining ferroelectricity and ionic conductivity for the development of advanced materials and technologies.

Differentiating Oxygen Exchange Reaction Mechanisms across Phase Boundaries

Triggering phase transitions by controlling the anion stoichiometry is an effective method of tuning the electrocatalytic activity of the functional oxides. However, understanding the potential differences in the reaction mechanism(s) of different phases requires the accurate mapping of phase boundaries during the electrochemical reactions, which can be quite challenging. In this work, we have established a feasible electrochemical method based on the measurement of chemical capacitance to resolve the critical stoichiometry at phase boundaries under operando conditions. We select a simple binary oxide PrOx as a proof-of-principle model system, which shows excellent activity for high-temperature oxygen incorporation and evolution reactions (OIR/OER). We show that the phase transition can be sensitively probed by quantifying the chemical capacitance, which can be further used for differentiating the OIR/OER mechanisms across the phase boundary of PrOx. Therefore, our findings provide a new framework for exploring phase engineering as a tool for the design of electrocatalysts.

Interface-type tunable oxygen ion dynamics for physical reservoir computing

Reservoir computing can more efficiently be used to solve time-dependent tasks than conventional feedforward network owing to various advantages, such as easy training and low hardware overhead. Physical reservoirs that contain intrinsic nonlinear dynamic processes could serve as next-generation dynamic computing systems. High-efficiency reservoir systems require nonlinear and dynamic responses to distinguish time-series input data. Herein, an interface-type dynamic transistor gated by an Hf0.5Zr0.5O2 (HZO) film was introduced to perform reservoir computing. The channel conductance of Mott material La0.67Sr0.33MnO3 (LSMO) can effectively be modulated by taking advantage of the unique coupled property of the polarization process and oxygen migration in hafnium-based ferroelectrics. The large positive value of the oxygen vacancy formation energy and negative value of the oxygen affinity energy resulted in the spontaneous migration of accumulated oxygen ions in the HZO films to the channel, leading to the dynamic relaxation process. The modulation of the channel conductance was found to be closely related to the current state, identified as the origin of the nonlinear response. In the time series recognition and prediction tasks, the proposed reservoir system showed an extremely low decision-making error. This work provides a promising pathway for exploiting dynamic ion systems for high-performance neural network devices.

Electrochemical-Memristor-Based Artificial Neurons and Synapses-Fundamentals, Applications, and Challenges

Artificial neurons and synapses are considered essential for the progress of the future brain-inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox-based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical-materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two- and three-terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state-of-the-art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low-power, high-information-density circuits are highlighted.

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.