Electrochemical Memristive Devices Toward Brain‐Inspired Computing and In‐Memory Communication: Mechanisms, Materials, and Device Engineering

Electrochemical devices allowing to modulate their resistive state through ionic motion upon electric bias are known as memristors. Currently, memristors are positioned as a major candidate to overcome the bottlenecks in current electronic-based transistors in terms of downscaling capabilities and energy consumption. The vast majority of memristor are based on two types of ions: either oxygen vacancy migration, in the so-called Valence Change Memories (VCM), or a metal cation, usually Ag+ and Cu2+, in the so-called Electrochemical Metallization Cells (ECM). Despite the excellent performance of both systems, a widespread implementation of oxygen-based memristors in today's integrated circuits is delayed due to the need to address cycle-to-cycle and device-to-device variabilities while circumventing electroforming, which are inherent issues associated to the filamentary nature of the switching mechanism. Recently, Li-ionic memories are emerging as an alternative, given the higher diffusivity of Li+ when compared to oxygen, and the ability of Li-oxides to accumulate and deplete lithium at the interfaces and bulk. The scarce literature regarding Li-based memristors focuses mainly on well-known high-voltage cathode battery materials like LixCoO2, rather sluggish conductor as LiLaTiO3or magnetic spinels like LiFe5O8.1,2 Performance-wise, Li-based oxides have shown promising applications in neuromorphic computing thanks to a close-to-analog switching response. Despite their promise, it remains unclear how to design the defect chemistry and control the switching behavior of Li-based materials towards their performances. For instance, the material class of Lithium titanate compounds present intrinsic properties that could be beneficial to implement for a Li-operated memristor: a) the existence of a metal-insulator transition upon lithium intercalation, concomitant with a phase transition and valence change in the Ti cation, b) the spinel to rock-salt phase transition occurs with a neglectable volume change and c) there is a large difference in electronic conductivity and diffusivity between the two phases. In this work,3 we first discuss the iono-electronic structure requirements to define Li-oxides and select their chemistries for resistive switching applications and neuromorphic computing. Second, we exemplify the resistive switching capabilities of these systems for different lithium stoichiometries and discuss current challenges and tricks in processing to make these. Third, we conceptually discuss how the Li-stroichiometry can be used as a knob to alter switching performance to be either linear or non-linear for neural computation, and from a material perspective how models for that can be thought of. To exemplify this, we will report most recent results on non-volatile, and non-filamentary bipolar resistive switching characteristics of lithium titanates compounds, Li4+3xTi5O12, that can be operated either for deep or synaptic neural networks applications. Here, we employed a recently proposed strategy to overcome lithium loss during thin film deposition and finely control the final lithiation degree of the films4 to create a stoichiometrically lithiated Li4Ti5O12 spinel phase and a highly lithiated Li7Ti5O12 rock- salt phase memristive devices. Changing the Li-content from a stoichiometrically lithiated spinel phase to a highly lithiated rock-salt phase results in the capability to tune the performance in a wide range in terms of accessible resistance window (from ratios of 102 to 106 at low voltage operation, respectively), switching symmetry (from highly asymmetric to symmetric behavior, respectively) and retention (from few minutes up to 105 s at room temperature, respectively), among others. In other words, controlling the lithiation degree might offer a suitable path to reduce stochasticity from which current filamentary memristive devices inherently suffer, mainly due to the difficulties to control the amount of vacancies generated, and paves a way to further control of ionic migration for novel iono-electronic devices in neuromorphic computation.

Extremely low energy consumption neuromorphic computing is required to achieve massively parallel information processing on par with the human brain. To achieve this goal, resistive memories based on materials with ionic transport and extremely low operating current are required. Extremely low operating current allows for low power operation by minimizing the program, erase, and read currents. However, materials currently used in resistive memories, such as defective HfOx, AlOx, TaOx, etc., cannot suppress electronic transport (i.e., leakage current) while allowing good ionic transport. Here, we show that 2D Ruddlesden-Popper phase hybrid lead bromide perovskite single crystals are promising materials for low operating current nanodevice applications because of their mixed electronic and ionic transport and ease of fabrication. Ionic transport in the exfoliated 2D perovskite layer is evident via the migration of bromide ions. Filaments with a diameter of approximately 20 nm are visualized, and resistive memories with extremely low program current down to 10 pA are achieved, a value at least 1 order of magnitude lower than conventional materials. The ionic migration and diffusion as an artificial synapse is realized in the 2D layered perovskites at the pA level, which can enable extremely low energy neuromorphic computing.

In the post-Moore’s Law era, conventional Von Neumann architectures face critical limitations, such as the “memory wall” and excessive power consumption, particularly when processing unstructured data. Neuromorphic computing, inspired by the human brain, offers a promising solution through parallel processing and adaptive learning. Among the candidates for artificial synapses, memristors based on two-dimensional MXenes (specifically Ti3C2Tx) have attracted significant attention due to their unique layered structure, high metallic conductivity, and tunable physicochemical properties. This review provides a comprehensive analysis of MXene-based memristors, from material synthesis to system-level applications. We examine how different synthesis strategies, including etching methods, directly influence device performance and elucidate the underlying resistive switching mechanisms driven by ion migration, valence change, and interfacial processes. Furthermore, the review demonstrates the efficacy of MXenes in emulating biological synaptic functions—such as spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD)—and their application in tasks like handwritten digit recognition. Finally, we highlight emerging frontiers in flexible electronics and in-sensor computing, offering insights into the future trajectory of integrated sensing, memory, and computation.

It is now evident that ion migration in oxides governs the memristive behavior of oxide thin film. After various approaches, still there are some uncertainties about the relation between ion migration and memristive behaviors. Complexities lie in the the ion migration behavior and redox reaction in oxides and at interface between oxide electrolyte and electrode. The mode change from frost-like to filament growth of reduced domains are of another issue to be discussed. In the present study, the author employed in-situ measurement of photoemission spectroscopy (PES) using synchrotron radiation to look at the redox reaction in oxide just underneath the electrode. The author employed thin Au electrode with 3-5 nm in thickness, using hard X-ray, for the excitation of photoelectrons 3-5nm underneath the Au electrode. Various type of “electrolytes,” Gd-doped ceria(GDC), amorphous TaOx(a-TaO x ), amorphous silica(a-SiO2), and so on, are employed to look at the electrochemical redox reaction in the vicinity of electrode. In case of GDC, XAS measurements have been also made to look at the redox reaction in deeper region from the electrode/oxide interface. In-situ PES measurements on various oxide thin film systems examined with MIM-type two-electrode cells revealed the variation of Fermi level upon electrochemical polarization, suggesting variations of local electrochemical redox state by the ion migration. Surprisingly narrow electrochemical windows were suggested limited by a pinning at the donor state of component cations and a chemical pinning due to oxygen evolution, in contrast to the ultra-wide band gap of these almost insulating oxides systems. The importance of the reversibility and how to control the kinetics at the anodic electrode should be emphasized. One important conclusion is the system is identical to the simple two-electrode cell composed of the electrolyte that shows variation of Fermi level within an electrochemical window of oxide system by ion migration or electrochemical reaction and modulated overpotential at electrolyte/electrode interfaces, the latter of which gives huge influence to the cell behaviors. These suggest that oxide memristor is governed by the electrochemical properties of the cell. Finally, the author proposes both a new Pourbaix-type E-pO diagram to understand the redox and acid/base reaction in addition to new proposal of the definition of basicity of complex oxide based on electronic band structure, as a necessary framework of solid state electrochemistry for oxide systems.

The rapid expansion of artificial intelligence has led to significant challenges in energy consumption and computational efficiency. To address these issues, the exploration and development of all-optical controlled (AOC) synaptic devices represents a promising leap forward in neuromorphic computing, offering potential solutions to the inherent limitations of traditional von Neumann architectures. AOC synaptic devices, utilizing exclusively optical signals to emulate bidirectional modulation of synaptic weights, bypass the complexity and additional energy costs associated with conventional electrical or electro-optical hybrid signals. This review articulates the underlying framework and fundamental motivations for studying AOC synapses, while systematically reviewing current research progress. We particularly highlight the synergistic relationships among physical mechanisms, material behaviors, and device architectures, as well as neuromorphic computing based on optical writing and optical erasing of information. By systematically interpreting these multidimensional correlations, we propose scalable and reproducible strategies for device design. This work will certainly herald a substantial direction of AOC synapses, providing an ideal platform for exploring neuromorphic computing for artificial intelligence.

Inspired by the ion transport mechanisms in biological systems, ionic technologies have emerged as a transformative field that bridges biology and electronics. Unlike electrons, ions not only transmit electrical signals but also convey chemical information and exhibit ion-specific transport behaviors. At the center of iontronic devices lie ion channels, highly selective and efficient structures that control ion transport. These ion channels, whether biological nanopores or artificial nanofluidic channels, fundamentally determine the properties of the devices. Therefore, understanding, engineering, and integrating versatile ion channels into artificial systems are critical to advancing the field. This Review provides a comprehensive overview of iontronic devices and systems, mainly covering advances after 2010, beginning with the principles of ion transport in both biological and artificial ion channels. We then examine fabrications and characterizations, with a focus on how material and structural design influence ionic properties. Device architectures are summarized and compared across multiple dimensions and scales. We highlight emerging applications in bioiontronics, neuromorphic computing, energy harvesting, water treatments, and environmental sustainability. Despite significant advancements, we propose that challenges remain in achieving the desired ion selectivity, efficient ionic signal transduction, and seamless integration of iontronics with electronics and biology.

Flexible and printable memristors are emerging as transformative platforms at the intersection of materials science, electronics, and neuromorphic computing. By integrating mechanical flexibility with resistive-switching functionality, these devices open new opportunities for low-power, flexible, and next-generation wearable electronics. This review provides a comprehensive overview of recent advances in flexible memristors, highlighting progress in flexible substrates, scalable fabrication techniques, novel functional materials, and their diverse application domains. Key materials include polymer dielectrics, two-dimensional (2D) materials, metal oxides on flexible substrates, and organic–inorganic hybrids, engineered into thin films, nanosheets, nanorods, and nanocrystals through vapour deposition and solution-based routes. We discuss how material composition, deposition methodology, interface engineering, and nanostructuring approaches govern key performance metrics, including
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endurance, retention, switching speed, and mechanical robustness under bending or stretching. The evolution of switching mechanisms, from filamentary conduction to interface-mediated processes and ion migration, is contextualized with the emerging applications, including neuromorphic computing, flexible memory arrays, logic circuits, and bio-interfaced electronics, such as artificial skin and wearable health monitors. Further, we address the challenges associated with the practical applications of the flexible memristive devices and discuss the future directions of research that can be pivotal in shaping the future of intelligent, responsive electronics.

Internal ion transport in ionic 2D CuInP2S6 enabling multi-state neuromorphic computing with low operation current

Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies. In fact, new computing solutions and new hardware platforms are expected to emerge to answer to the new needs and challenges of our societies. In this revolution, lots of candidates’ technologies are explored and will require leveraging of the pros and cons. In this perspective paper belonging to the special issue on neuromorphic engineering of Journal of Applied Physics, we focus on the current achievements in the field of organic electronics and the potentialities and specificities of this research field. We highlight how unique material features available through organic materials can be used to engineer useful and promising bio-inspired devices and circuits. We also discuss the opportunities that organic electronics offer for future research directions in the neuromorphic engineering field.

The presence of hysteresis in perovskite solar cells (PSCs) complicates the reliable evaluation of cell performance for practical applications. Numerous efforts have been made to figure out the reasons behind this phenomenon and to resolve the hysteresis, but it still needs to be explored for better understanding. This chapter is mainly focused on theoretical and experimental studies to reveal the origin of the hysteresis and discuss the remedies to eliminate the hysteric behavior in PSCs. In the last few years, the PSC has emerged as one of the fastest growing photovoltaic technologies that achieved high-power conversion efficiency (>25%) in a short span of time. Despite the high efficiency attained, PSCs suffer from current density-voltage (J-V) hysteresis when J-V characteristics were traced in forward and reverse scans. The presence of hysteresis in PSCs significantly influences the photovoltaic (PV) properties and most importantly device stability. Generally, the hysteric behavior in a PSC arises due to ferroelectric polarization, charge carrier trapping/detrapping, and ion migration in the perovskite materials. A systematic discussion on the key factors involved in the hysteresis generation and how it can be eliminated from PSCs, which includes improvement in morphology by either increasing grain sizes, additive doping, interface engineering, device architecture, etc. On the other hand, the hysteresis can also be positively utilized in other applications such as memristive switching devices.

2D ferroelectric materials have recently emerged as a promising class of atomically thin semiconductors capable of integrating sensing, memory, and computation within a single device. Their unique combination of spontaneous switchable polarization, strong light-matter coupling, and van der Waals (vdW) interface compatibility provides an ideal platform for next-generation optoelectronic vision sensors. Coupling ferroelectric polarization with photoresponse, 2D ferroelectric materials such as α-In2Se3, CuInP2S6 (CIPS), SnS, and WTe3 enable non-volatile modulation of photocarrier transport, facilitating adaptive visual perception analogous to the human retina. These 2D ferroelectric photonic devices demonstrate synaptic plasticity, short-term and long-term memory, and optical potentiation and depression characteristics under visible and near-infrared excitation. Integrating ferroelectricity into optoelectronic architectures addresses the von-Neumann bottleneck by enabling in-sensor computing, where data are sensed, stored, and processed locally, minimizing latency and energy consumption. This review provides a comprehensive overview of 2D ferroelectric materials and their device architectures in the memristive and memtransistors devices structures for optoelectronic vision sensors, highlighting their polarization mechanism, light-driven conductance modulation, and neuromorphic functionalities. Additionally, current challenges, such as scalability, polarization fatigue, and interface engineering, have also been extensively discussed together with heterostructure design and hybrid ferroelectric-semiconductor integration toward energy-efficient bio-inspired vision systems.

Multilevel cell (MLC) capability of a memory device is an attractive alternative to realise high integration density of memory arrays. In addition, power efficient memory cell along with MLC capability has huge potential in neuromorphic computing and data storage applications. In this study low power, high speed and 3-bit multilevel bipolar resistance switching operation is reported in TiO2 thin films where copper is used as counter electrode. On account of using an oxidisable electrode, low programming voltages (∼+0.2 V, −0.1 V) along with low reset current (∼10 µA) are observed, thereby ensuing low power (∼µW) memory operation. Additionally, observation of fast switching (∼250 ns) translated to ultra-low energy (∼pJ) consumption. By varying compliance current (IC) from 25 µA to 1.6 mA during the set process, eight distinct non-volatile resistance states are reliably obtained, which showed statistically significant and non-overlapping resistance distribution. The multilevel resistive switching in Cu/TiO2/Pt device is explained by considering filamentary the widening phenomenon in the electrochemical metallization memory cell. Numerical calculations revealed that increase in the diameter of the metallic conducting filament from ∼3 nm to 70 nm with increasing IC is accountable for multiple low resistance states. The results of this study are promising towards realisation of emerging memory devices with low operating power, fast switching operation and high spatial density.

The development of brain-inspired neuromorphic computing, including artificial intelligence (AI) and machine learning, is of considerable importance because of the rapid growth in hardware and software capacities, which allows for the efficient handling of big data. Devices for neuromorphic computing must satisfy basic requirements such as multilevel states, high operating speeds, low energy consumption, and sufficient endurance, retention and linearity. In this study, inorganic perovskite-type amorphous strontium vanadate (a-SrVOx: a-SVO) synthesized at room temperature is utilized to produce a high-performance memristor that demonstrates nonvolatile multilevel resistive switching and synaptic characteristics. Analysis of the electrical characteristics indicates that the a-SVO memristor illustrates typical bipolar resistive switching behavior. Multilevel resistance states are also observed in the off-to-on and on-to-off transition processes. The retention resistance of the a-SVO memristor is shown to not significantly change for a period of 2 × 104 s. The conduction mechanism operating within the Ag/a-SVO/Pt memristor is ascribed to the formation of Ag-based filaments. Nonlinear neural network simulations are also conducted to evaluate the synaptic behavior. These results demonstrate that a-SVO-based memristors hold great promise for use in high-performance neuromorphic computing devices.

The performance of polymer and hybrid solar cells is also strongly dependent on their efficiency in harvesting light, exciton dissociation, charge transport, and charge collection at the metal/organic/metal oxide or the metal/perovskite/metal oxide interfaces. Our laboratory employs a molecular engineering approach to develop processible low band-gap polymers with high charge carrier mobility that can enhance power conversion efficiency of the single junction solar cells to values as high as ~11%. We have also developed several innovative strategies to modify the interface of bulk-heterojunction devices and create new device architectures to fully explore their potential for solar applications. In this talk, the integrated approach of combining material design, interface, and device engineering to significantly improve the performance of polymer and hybrid perovskite photovoltaic cells will be discussed. Specific emphasis will be placed on the development of low band-gap polymers with reduced reorganizational energy and proper energy levels, formation of optimized morphology of active layer, and minimized interfacial energy barriers using functional conductive surfactants. At the end, several new device architectures and optical engineering strategies to make tandem cells and semitransparent solar cells will be discussed to explore the full promise of polymer and perovskite hybrid solar cells.

The development of neuromorphic computation faces the appreciable challenge of implementing hardware with energy consumption on the level of a femtojoule per synaptic event to be comparable with the energy consumption of human brain. Controllable ultrathin conductive filaments are needed to achieve such extremely low energy consumption in memristive synapses but their formation is difficult to control owing to their stochastic morphology and unexpected overgrowth. Herein, a zeolite-based memristive synapse is demonstrated for the first time, in which Ag exchange in the sub-nanometer pore closely resembles synaptic Ca2+ dynamics across biomembrane channel. Particularly, the confined ultrasmall pore and low Ag ion migration barrier give the zeolite-based memristive synapse ultralow energy consumption below 10 fJ per synaptic spike, on par with the biological counterpart. Experimental results reveal that the gradual memristive effect is attributed to the dimension modulation of Ag clusters. In addition to emulating inherent cognitive functions through electrical stimulations, the experience-dependent transition of short-term plasticity to long-term plasticity using a chemical modulation method is achieved by treating the initial Ag quantity as a learning experience. The proposed memristors can be used to develop highly efficient memristive neural networks and are considered as a candidate for application in neuromorphic computation.