Solution-processed LiGdO solid electrolyte for an In2O3 synaptic transistor and its application in neuromorphic computing

Human brain is capable of optimizing information flow and processing without energy-intensive data shuttling between processor and memory. At the core of this unique capability are billions of neurons connected through trillions of synapses—basic processing units of the brain. The action potentials or “spikes” based temporal processing using the regulated flow of ions across ion channels in neuron cells allows sparse and efficient transmission of data in the brain. Emerging systems based on confined fluidic systems have provided a framework for a new type of neuromorphic computing with lower energy consumption, hardware-level plasticity, and multiple information carriers that emulate natural processes and mechanisms of human brain. These systems mimic neuronal architectures by harnessing and modulating ion transport along artificial channels. The spikes-induced ion-to-surface interactions within these fluidic systems enables the control of ionic conductivity to achieve synaptic plasticity for the realization of brain-inspired functionalities such as memory effect and signal transmission. Herein, this review provides an overview of recent advances in fluidic devices such as memristors and other computing components, covering their basic operations, materials and architectures, as well as applications in neuromorphic computing. The review concludes with a brief outline of the challenges that these emerging technologies face and an outlook for the development of fluidic-based brain-inspired computing.

Artificial synaptic simulating pain-perceptual nociceptor and brain-inspired computing based on Au/Bi3.2La0.8Ti3O12/ITO memristor

The advent of memristors and the continuing research and development in the field of brain-inspired computing could allow realization of a veritable “thinking machine”. In this study, ZnO-based memristors were fabricated using a radio frequency magnetron sputtering method. The ZnO oxide layer was prepared by incorporating silver nanocrystals (NCs). Several synaptic functions, i.e. nonlinear transmission characteristics, short-term potentiation, long-term potentiation/depression, and pair-pulse facilitation, were imitated in the memristor successfully. Furthermore, the transition from synaptic behaviors to bipolar resistive switching behaviors of the device was also observed under repeated stimulus. It is speculated that the switching mechanism is due to the formation and rupture of the conductive Ag filaments and the corresponding electrochemical metallization. The experimental results demonstrate that the Ag/Ag:ZnO/Pt memristor with resistive switching and several synaptic behaviors has a potential application in neuromorphic computing and data storage systems.

Implementing memristors for neuromorphic computing demands ultralow power, suppressed variations, and compact structure. Previously reported artificial neurons are mostly demonstrated with micrometer size in which the stochastic formation/rupture of conductive filaments leads to significant temporal and spatial variations. Additionally, external current compliance is commonly applied to ensure volatile switching behavior, inevitably increasing design complexity and power consumption due to auxiliary circuitry. Here, an ultra‐scaled volatile memristor is demonstrated using carbon nanotube (CNT)/hBN/silver nanowire (Ag NW) cross‐point structure with a conducting area of only 120 nm2. Owing to the nanoscale geometry for ion migration, the memristor exhibits suppressed cycle‐to‐cycle and device‐to‐device variations. Self‐compliant memristive behavior is achieved, simplifying the overall system design. Furthermore, the power consumption of the cross‐point memristor‐based neuron is drastically reduced. The results provide guidelines for tailoring the critical electrical behavior of geometry‐scaled memristor, generating practical understanding of ultra‐scaled memristor and its potential application in neuromorphic computing.

Bistable ZTO-based neuromorphic memtransistors enabling mode-tunable synaptic diversity and their applications in neuromorphic computing

Ion transport through nanoscale channels enables advanced functionalities, such as ionic current rectification (ICR), with promising applications in neuromorphic computing and biomimetic signal processing. However, the fundamental mechanisms controlling the ion dynamics under nanoconfinement remain poorly understood. Using atomistic molecular dynamics simulations and free energy calculations, we demonstrate that multilayered Janus graphene oxide nanopores exhibit exceptional and tunable ICR performance mediated by interlayer coupling. These structures achieve a rectification ratio enhancement of up to 2 orders of magnitude─from ∼2 in a single layer to over 2000 at 3.5 V/nm in multilayered configurations─and a shift of the peak rectification field from 0.7 to 3.5 V/nm with increasing layer number. Ion distribution analyses reveal distinctive ionic enrichment-depletion behavior unique to multilayered architectures. Thermodynamically, we unveil that synergistic interlayer coupling fundamentally reshapes the free energy landscape, creating a highly asymmetric profile with multiple energy barriers and wells due to entropy─enthalpy competition. Importantly, entropy is identified to play a critical role in stabilizing energy wells and facilitating directional ion transport─a mechanism absent in single-layer systems. These insights provide a mechanistic basis for ion rectification and establish design principles, such as interlayer spacing or number control, for developing high-performance ionic membranes and nanofluidic devices.

The integration of quantum theory with biological modeling has generated new possibilities for understanding neural network dynamics in complex organisms. Traditional computational neuroscience frameworks rely on classical assumptions of synaptic transmission, electrochemical signaling, and network plasticity. However, these models often struggle to account for nonlinear, emergent, and context-dependent behaviors observed in biological cognition. This study investigates how quantum bio-systems modeling offers an expanded theoretical foundation for explaining neural processes by incorporating principles such as superposition, entanglement, and probability amplitude interference. Through qualitative synthesis of literature in quantum biology, cognitive neuroscience, and computational modeling, the research explores how quantum-level interactions—particularly within microtubules, ion channels, and molecular signaling networks—may influence large-scale neural dynamics. The analysis further examines how quantum-inspired mathematical structures can capture complex patterns such as rapid state transitions, adaptive learning, and multi-scale interactions in neural circuits. The findings suggest that quantum bio-systems modeling not only enhances theoretical understanding of cognition but also holds promise for applications in neuromorphic computing, mental health diagnostics, and bio-inspired artificial intelligence. Ultimately, the study positions quantum-biological frameworks as an emerging frontier for advancing our knowledge of neural complexity, bridging molecular processes with organism-level behavior.

Resistive switching devices, particularly memristors, have attracted considerable interest due to their promising applications in neuromorphic computing and data storage. However, achieving high performance and reliability remains a significant challenge, especially in the optimization of their ferroelectric and switching properties. In this study, we report a substantial enhancement of both resistive switching and ferroelectric properties in NaNbO3 (NNO)/PbTiO3 (PTO) multilayers, facilitated by interfacial modifications of the electronic structure induced by defects and strain. The well-defined interfaces and strain gradients within the PTO layers lead to substantial alterations in local electronic properties, including Ti 3d orbital hybridization and oxygen octahedral tilting. These structural modifications enhance charge trapping dynamics, resulting in an ON/OFF ratio of 104, compared with 102 in single-layer NNO films. The synergistic effects of enhanced polarization and electronic state modulation are shown to optimize both the ferroelectric and resistive switching behaviors, highlighting the pivotal role of interface engineering in achieving high-performance memristive devices.

Resistive switching devices are promising candidates to replace today’s nonvolatile memory device, and find applications in neuromorphic computing. In this study, bipolar resistive switching (BRS) and unipolar resistive switching (URS) behaviors at room temperature were shown for Ag/Ti/CeO2/Pt devices. Without forming step, the device exhibited low set voltage (about 0.3 V) and reset voltage (about −0.3 V to −0.6 V) in BRS behavior, which is beneficial for nonvolatile memory application. The I-V characteristics were analyzed, and it was concluded that the switching mechanism in BRS is dominated by the electrochemical metallization mechanism (ECM), while in URS, conduction is dominated by thermochemical mechanism (TCM).

The highly nonlinear switching behavior of hafnia memristor actually hinders its wide application in neuromorphic computing. The theoretical understanding of its switching mechanism has been focused on the processes of conductive filament (CF) generation and rupture, but the possible phase transition and crystallization around the region of CFs due to the variation of O content have been less studied or understood. In this paper, ${\mathrm{HfO}}_{\mathrm{x}}$ structural models covering the full stoichiometries from Hf to ${\mathrm{HfO}}_{2}$ are established, and the crystal structure evolution during the reduction process of hafnia is obtained through first-principles calculation. The electronic structures and O vacancy migration characteristics of these structures are analyzed. A criterion is prescribed to predict the mode of abrupt binary switching or gradual conductance modulation according to the structure evolution of the CFs. In particular, factors that influence the merging of tiny conductive channels into strong CFs are discussed intensively, including the anisotropy of O vacancy migration and the size effect. The feasibility of Mg doping to achieve robust gradual switching is discussed.

Ferroelectric oxide memristors are currently in the highlights of a thriving area of research aiming at the development of nonvolatile, adaptive memories for applications in neuromorphic computing. However, to date a precise control of synapse-like functionalities by adjusting the interplay between ferroelectric polarization and resistive switching processes is still an ongoing challenge. Here, it is shown that by means of controlled electron beam radiation, a prototypical ferroelectric film of BaTiO3 can be turned into a memristor with multiple configurable resistance states. Ex situ and in situ analyses of current/voltage characteristics upon electron beam exposure confirm the quasi-continuous variation of BaTiO3 resistance up to two orders of magnitude under the typical experimental conditions employed in electron beam patterning and characterization techniques. These results demonstrate an unprecedented effective route to locally and scalably engineering multilevel ferroelectric memristors via application of moderate electron beam radiation.

In the post-Moore era, semifloating gate devices have great potential to be developed into next-generation devices for their excellent nonvolatile memory and reconfigurable logic. 2D materials have been focused due to their atomically flat surfaces, high carrier mobility, and excellent photoelectrical response. The 2D ReSe2 is selected as a channel material for its ambipolar characteristic and outstanding optoelectronic response. Here, we fabricated ReSe2/h-BN/Gr multifunctional semifloating gate (MFSFG) devices, which can work as bidirectional nonvolatile reconfigurable multistate P-N and N-P homojunctions, photodetectors, and artificial synaptic, reconfigurable logical, and half-wave rectification devices. The device exhibits large rectification ratios of ∼106 (P-N) and ∼104 (N-P) with great endurance (1000 cycles) and retention (1000 s). As a photodetector, it obtains the highest responsivity and detectivity of 1.98 A W-1 and 6.39 × 1012 Jones (N-P) and 0.93 A W-1 and 2.00 × 1012 Jones (P-N), respectively, under 532 nm illumination. The synaptic plasticity is perfectly achieved, and the convolutional neural network built based on synaptic data has the highest classification recognition accuracies of 96.54 and 88.99%. The logical ″XOR″, ″XNOR″, ″NAND″, ″OR″, and half-wave rectification functions are achieved on a single device under photoelectrical hybrid regulations. The integration of these various functions into a single ReSe2/h-BN/Gr MFSFG device not only broadens the possibilities for utilizing 2D materials in multifunctional devices but also opens up new avenues for their application in neuromorphic computing and logic-in-memory chips.

Metal halide perovskites, renowned for their exceptional optoelectronic properties, have emerged as promising candidates for next-generation semiconductor materials, addressing critical challenges such as the energy crisis and the increasing demands of information technology. Building on these advantages, perovskite solar cells have achieved significant advancements in recent years, while perovskite memristors have also risen as a promising frontier. However, the path toward commercialization remains hindered by several challenges, including large-scale production, material toxicity, long-term stability, and other factors.<br/>In this thesis, we present the first exploration of blade coating technology for fabricating high-efficiency, stable, and low-toxicity lead-tin (Pb-Sn) and tin (Sn)-based perovskite solar cells, achieving efficiencies exceeding 16% and 10%, respectively. These results rank among the highest efficiencies for large-scale production methods. Additionally, we investigated the use of non-toxic solvents for perovskite device fabrication. Moreover, low-dimensional Pb-Sn perovskites were applied to memristors for the first time, exhibiting repeatable and highly stable memristive properties with potential applications in neuromorphic computing. The excellent performance of these devices contributes to advancing sustainable and multifunctional applications of metal halide perovskites.

Neuromorphic systems that can emulate the behavior of neurons have garnered increasing interest across interdisciplinary fields due to their potential applications in neuromorphic computing, artificial intelligence and brain-machine interfaces. However, the optical modulation of nanofluidic ion transport for neuromorphic functions has been scarcely reported. Herein, inspired by biological systems that rely on ions as signal carriers for information perception and processing, we present a nanofluidic transistor based on a metal-organic framework membrane (MOFM) with optically modulated ion transport properties, which can mimic the functions of biological synapses. Through the dynamic modulation of synaptic weight, we successfully replicate intricate learning-experience behaviors and Pavlovian associate learning processes by employing sequential optical stimuli. Additionally, we demonstrate the application of the International Morse Code with the nanofluidic device using patterned optical pulse signals, showing its encoding and decoding capabilities in information processing process. This study would largely advance the development of nanofluidic neuromorphic devices for biomimetic iontronics integrated with sensing, memory and computing functions.

In the emerging fields of flexible electronics and bioelectronics, protein-based materials have attracted widespread attention due to their biocompatibility, biodegradability, and processability. Among these materials, silk fibroin (SF), a protein derived from natural silk, has demonstrated significant potential in biomedical applications such as medical sensing and bone tissue engineering, as well as in the development of advanced biosensors. This is primarily due to its highly ordered β-sheet structure, mechanical properties, and processability. Furthermore, SF-based memristors provided a material choice for producing flexible wearable, and even implantable bioelectronic devices, which are expected to advance intelligent health monitoring, electronic skin (e-skin), brain-computer interface (BCI), and other frontier bioelectronic technologies. This review systematically summarizes the latest research progress in SF-based memristors concerning structural design, performance optimization, device integration, and application prospects, particularly highlighting their potential applications in neuromorphic computing and memristive sensors. Concurrently, we objectively analyzed the challenges currently faced by SF-based memristors and prospectively discussed their future development trends. This review provides a theoretical foundation and technological roadmap for biomaterials-based memristor devices, aiming to realize applications in flexible electronics and bioelectronics.