Ionic modulation and ionic coupling effects in MoS2 devices for neuromorphic computing

Quantized ferroelectricity in multivalent ion conductors with non-polar point groups

Ferroelectricity with switchable polarizations is generally associated with small ion-displacements and occurs only in 10 specific polar point groups, which is not a necessary requirement for ion conduction where the ions can also be electrically displaced but by much longer distances. Herein, through first-principles calculations, we predict the formation of unconventional ferroelectricity based on previous experimental reports on topotactic reaction with an aliovalent cation between trigonal layers of ion conductors. In such systems, the multivalent cations are surrounded by vacant sites that can simultaneously migrate by a much larger distance compared with conventional displacive ferroelectricity, giving rise to a quantized change in polarization even if the crystal lattices do not belong to the 10 polar groups. The deviation from classical principles can be attributed to the long ion displacements in ferroelectric ion conductors during switching that can lead to the transformation between multiple equivalent symmetrical stable states, which cannot be realized by the relatively small ion displacements in current ferroelectrics. The evenly distributed vacant sites due to Coulomb repulsion do not break the insulativity of the systems, while their inhomogeneous distribution under an electric field or in ferroelectric domain walls will give rise to high electrical conductance, which may be utilized for constructing nanoscale artificial ionic synapses that enable neuromorphic computing.

Imaging Anisotropic Proton Intercalation in Photochromic MoO3

Protonation represents a fundamental chemical process with promising applications in the fields of energy, environment, and memory devices. Probing the protonation mechanism, however, presents a formidable challenge owing to the elusiveness of intercalated protons. In this work, we utilize the atomic and electronic structure changes associated with protonation to directly image the proton intercalation pathways in α-MoO3 induced by UV illumination. We reveal the anisotropic intercalation behavior which is initiated by photocatalyzed water dissociation preferentially at the (001) edges and then propagates along the c axis, transforming α-MoO3 into HxMoO3 to realize photochromism. This photochromic process can be reversed via heating in air, leading to anisotropic proton deintercalation, also preferentially along the c axis. The observed anisotropic behavior can be attributed to the intrinsically low energy barriers for both proton migration along the c axis and water dissociation/formation at (001) edges.

Ultra-Wide Interlayered WxMo2xSy Alloy Electrode Patterning through High-Precision Controllable Photonic-Synthesis

Ultra-thin 2D materials have great potential as electrodes for micro-supercapacitors (MSCs) because of their facile ion transport channels. Here, a high-precision controllable photonic-synthesis strategy that provided 1 inch wafer-scale ultra-thin film arrays of alloyed WxMo2xSy with sulfur vacancies and expanded interlayer (13.2 Å, twice of 2H MoS2) is reported. This strategy regulates the nucleation and growth of transition metal dichalcogenides (TMDs) on the picosecond or even femtosecond scale, which induces Mo-W alloying, interlayer expansion, and sulfur loss. Therefore, the diffusion barrier of WxMo2xSy is reduced, with charge transfer and ion diffusion enhancing. The as-prepared symmetric MSCs with the size of 100 × 100 µm2 achieve ultrahigh specific capacitance (242.57 mF cm-2 and 242567.83 F cm-3), and energy density (21.56 Wh cm-3 with power density of 485.13 W cm3). The established synthesis strategy fits numerous materials, which provides a universal method for the flexible synthesis of electrodes in microenergy devices.

Biomimetic Neuromorphic Sensory System via Electrolyte Gated Transistors

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Ionic Photovoltaics-in-Memory in van der Waals Material

The photovoltaic effect is gaining growing attention in the optoelectronics field due to its low power consumption, sustainable nature, and high efficiency. However, the photovoltaic effects hitherto reported are hindered by the stringent band-alignment requirement or inversion symmetry-breaking, and are challenging for achieving multifunctional photovoltaic properties (such as reconfiguration, nonvolatility, and so on). Here, a novel ionic photovoltaic effect in centrosymmetric CdSb2Se3Br2 that can overcome these limitations is demonstrated. The photovoltaic effect displays significant anisotropy, with the photocurrent being most apparent along the CdBr2 chains while absent perpendicular to them. Additionally, the device shows electrically-induced nonvolatile photocurrent switching characteristics. The photovoltaic effect is attributed to the modulation of the built-in electric field through the migration of Br ions. Using these unique photovoltaic properties, a highly secure circuit with electrical and optical keys is successfully implemented. The findings not only broaden the understanding of the photovoltaic mechanism, but also provide a new material platform for the development of in-memory sensing and computing devices.

Proton Conducting Neuromorphic Materials and Devices

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Synthesis and Polymorph Manipulation of FeSe2 Monolayers

Polymorph engineering involves the manipulation of material properties through controlled structural modification and is a candidate technique for creating unique two-dimensional transition metal dichalcogenide (TMDC) nanodevices. Despite its promise, polymorph engineering of magnetic TMDC monolayers has not yet been demonstrated. Here we grow FeSe2 monolayers via molecular beam epitaxy and find that they have great promise for magnetic polymorph engineering. Using scanning tunneling microscopy (STM) and spectroscopy (STS), we find that FeSe2 monolayers predominantly display a 1T' structural polymorph at 5 K. Application of voltage pulses from an STM tip causes a local, reversible transition from the 1T' phase to the 1T phase. Density functional theory calculations suggest that this single-layer structural phase transition is accompanied by a magnetic transition from an antiferromagnetic to a ferromagnetic configuration. These results open new possibilities for creating functional magnetic devices with TMDC monolayers via polymorph engineering.

Conformal Neuromorphic Bioelectronics for Sense Digitalization

Sense digitalization, the process of transforming sensory experiences into digital data, is an emerging research frontier that links the physical world with human perception and interaction. Inspired by the adaptability, fault tolerance, robustness, and energy efficiency of biological senses, this field drives the development of numerous innovative digitalization techniques. Neuromorphic bioelectronics, characterized by biomimetic adaptability, stand out for their seamless bidirectional interactions with biological entities through stimulus-response and feedback loops, incorporating bio-neuromorphic intelligence for information exchange. This review illustrates recent progress in sensory digitalization, encompassing not only the digital representation of physical sensations such as touch, light, and temperature, correlating to tactile, visual, and thermal perceptions, but also the detection of biochemical stimuli such as gases, ions, and neurotransmitters, mirroring olfactory, gustatory, and neural processes. It thoroughly examines the material design, device manufacturing, and system integration, offering detailed insights. However, the field faces significant challenges, including the development of new device/system paradigms, forging genuine connections with biological systems, ensuring compatibility with the semiconductor industry and overcoming the absence of standardization. Future ambition includes realization of biocompatible neural prosthetics, exoskeletons, soft humanoid robots, and cybernetic devices that integrate smoothly with both biological tissues and artificial components.

Intercalation in 2D materials and in situ studies

Intercalation of atoms, ions and molecules is a powerful tool for altering or tuning the properties - interlayer interactions, in-plane bonding configurations, Fermi-level energies, electronic band structures and spin-orbit coupling - of 2D materials. Intercalation can induce property changes in materials related to photonics, electronics, optoelectronics, thermoelectricity, magnetism, catalysis and energy storage, unlocking or improving the potential of 2D materials in present and future applications. In situ imaging and spectroscopy technologies are used to visualize and trace intercalation processes. These techniques provide the opportunity for deciphering important and often elusive intercalation dynamics, chemomechanics and mechanisms, such as the intercalation pathways, reversibility, uniformity and speed. In this Review, we discuss intercalation in 2D materials, beginning with a brief introduction of the intercalation strategies, then we look into the atomic and intrinsic effects of intercalation, followed by an overview of their in situ studies, and finally provide our outlook.

General synthesis of ionic-electronic coupled two-dimensional materials

Two-dimensional (2D) AMX2 compounds are a family of mixed ionic and electronic conductors (where A is a monovalent metal ion, M is a trivalent metal, and X is a chalcogen) that offer a fascinating platform to explore intrinsic coupled ionic-electronic properties. However, the synthesis of 2D AMX2 compounds remains challenging due to their multielement characteristics and various by-products. Here, we report a separated-precursor-supply chemical vapor deposition strategy to manipulate the chemical reactions and evaporation of precursors, facilitating the successful fabrication of 20 types of 2D AMX2 flakes. Notably, a 10.4 nm-thick AgCrS2 flake shows superionic behavior at room temperature, with an ionic conductivity of 192.8 mS/cm. Room temperature ferroelectricity and reconfigurable positive/negative photovoltaic currents have been observed in CuScS2 flakes. This study not only provides an effective approach for the synthesis of multielement 2D materials with unique properties, but also lays the foundation for the exploration of 2D AMX2 compounds in electronic, optoelectronic, and neuromorphic devices.

Lithium Ion Intercalation-Induced Metal-Insulator Transition in Inclined-Standing Grown 2D Non-Layered Cr2S3 Nanosheets

Gate-controlled ionic intercalation in the van der Waals gap of 2D layered materials can induce novel phases and unlock new properties. However, this strategy is often unsuitable for densely packed 2D non-layered materials. The non-layered rhombohedral Cr2S3 is an intrinsic heterodimensional superlattice with alternating layers of 2D CrS2 and 0D Cr1/3. Here an innovative chemical vapor deposition method is reported, utilizing strategically modified metal precursors to initiate entirely new seed layers, yields ultrathin inclined-standing grown 2D Cr2S3 nanosheets with edge instead of face contact with substrate surfaces, enabling rapid all-dry transfer to other substrates while ensuring high crystal quality. The unconventional ordered vacancy channels within the 0D Cr1/3 layers, as revealed by cross-sectional scanning transmission electron microscope, permitting the insertion of Li+ ions. An unprecedented metal-insulator transition, with a resistance modulation of up to six orders of magnitude at 300 K, is observed in Cr2S3-based ionic field-effect transistors. Theoretical calculations corroborate the metallization induced by Li-ion intercalation. This work sheds light on the understanding of growth mechanism, structure-property correlation and highlights the diverse potential applications of 2D non-layered Cr2S3 superlattice.

Charge Transport and Ion Kinetics in 1D TiS2 Structures are Dependent on the Introduction of Selenium Extrinsic Atoms

Improving charge insertion into intercalation hosts is essential for crucial energy and memory technologies. The layered material TiS2 provides a promising template for study, but further development of this compound demands improvement to its ion kinetics. Here, we report the incorporation of Se atoms into TiS2 nanobelts to address barriers related to sluggish ion motion in the material. TiS1.8Se0.2 nanobelts are synthesized through a solid-state method, and structural and electrochemical characterizations reveal that solid solutions based on TiS1.8Se0.2 nanobelts display increased interlayer spacing and electrical conductivity compared to pure TiS2 nanobelts. Cyclic voltammetry and electrochemical impedance spectroscopy indicate that the capacitive behavior of the TiS2 electrode is improved upon Se incorporation, particularly at low depths of discharge in the materials. The presence of Se in the structure can be directly related to an increased pseudocapacitive contribution to electrode behavior at a low Li+ content in the material and thus to improved ion kinetics in the TiS1.8Se0.2 nanobelts.

Phase Engineering of Nanomaterials: Transition Metal Dichalcogenides

Crystal phase, a critical structural characteristic beyond the morphology, size, dimension, facet, etc., determines the physicochemical properties of nanomaterials. As a group of layered nanomaterials with polymorphs, transition metal dichalcogenides (TMDs) have attracted intensive research attention due to their phase-dependent properties. Therefore, great efforts have been devoted to the phase engineering of TMDs to synthesize TMDs with controlled phases, especially unconventional/metastable phases, for various applications in electronics, optoelectronics, catalysis, biomedicine, energy storage and conversion, and ferroelectrics. Considering the significant progress in the synthesis and applications of TMDs, we believe that a comprehensive review on the phase engineering of TMDs is critical to promote their fundamental studies and practical applications. This Review aims to provide a comprehensive introduction and discussion on the crystal structures, synthetic strategies, and phase-dependent properties and applications of TMDs. Finally, our perspectives on the challenges and opportunities in phase engineering of TMDs will also be discussed.

Resistive Memory Devices at the Thinnest Limit: Progress and Challenges

The Si-based integrated circuits industry has been developing for more than half a century, by focusing on the scaling-down of transistor. However, the miniaturization of transistors will soon reach its physical limits, thereby requiring novel material and device technologies. Resistive memory is a promising candidate for in-memory computing and energy-efficient synaptic devices that can satisfy the computational demands of the future applications. However, poor cycle-to-cycle and device-to-device uniformities hinder its mass production. 2D materials, as a new type of semiconductor, is successfully employed in various micro/nanoelectronic devices and have the potential to drive future innovation in resistive memory technology. This review evaluates the potential of using the thinnest advanced materials, that is, monolayer 2D materials, for memristor or memtransistor applications, including resistive switching behavior and atomic mechanism, high-frequency device performances, and in-memory computing/neuromorphic computing applications. The scaling-down advantages of promising monolayer 2D materials including graphene, transition metal dichalcogenides, and hexagonal boron nitride are presented. Finally, the technical challenges of these atomic devices for practical applications are elaborately discussed. The study of monolayer-2D-material-based resistive memory is expected to play a positive role in the exploration of beyond-Si electronic technologies.

Hydrogen-Driven Low-Temperature Topotactic Transition in Nanocomb Cobaltite for Ultralow Power Ionic-Magnetic Coupled Applications

We reversibly control ferromagnetic-antiferromagnetic ordering in an insulating ground state by annealing tensile-strained LaCoO3 films in hydrogen. This ionic-magnetic coupling occurs due to the hydrogen-driven topotactic transition between perovskite LaCoO3 and brownmillerite La2Co2O5 at a lower temperature (125-200 °C) and within a shorter time (3-10 min) than the oxygen-driven effect (500 °C, tens of hours). The X-ray and optical spectroscopic analyses reveal that the transition results from hydrogen-driven filling of correlated electrons in the Co 3d-orbitals, which successively releases oxygen by destabilizing the CoO6 octahedra into CoO4 tetrahedra. The transition is accelerated by surface exchange, diffusion of hydrogen in and oxygen out through atomically ordered oxygen vacancy "nanocomb" stripes in the tensile-strained LaCoO3 films. Our ionic-magnetic coupling with fast operation, good reproducibility, and long-term stability is a proof-of-principle demonstration of high-performance ultralow power magnetic switching devices for sensors, energy, and artificial intelligence applications, which are keys for attaining carbon neutrality.

Ionic Diffusive Nanomemristors with Dendritic Competition and Cooperation Functions for Ultralow Voltage Neuromorphic Computing

Realization of dendric signal processing in the human brain is of great significance for spatiotemporal neuromorphic engineering. Here, we proposed an ionic dendrite device with multichannel communication, which could realize synaptic behaviors even under an ultralow action potential of 80 mV. The device not only could simulate one-to-one information transfer of axons but also achieve a many-to-one modulation mode of dendrites. By the adjustment of two presynapses, Pavlov's dog conditioning experiment was learned successfully. Furthermore, the device also could emulate the biological synaptic competition and synaptic cooperation phenomenon through the comodulation of three presynapses, which are crucial for artificial neural network (ANN) implementation. Finally, an ANN was further constructed to realize highly efficient and anti-interference recognition of fashion patterns. By introducing the cooperative device, synaptic weight updates could be improved for higher linearity and larger dynamic regulation range in neuromorphic computing, resulting in higher recognition accuracy and efficiency. Such an artificial dendric device has great application prospects in the processing of more complex information and the construction of an ANN system with more functions.

Two-Dimensional Semiconductors and Transistors for Future Integrated Circuits

Silicon transistors are approaching their physical limit, calling for the emergence of a technological revolution. As the acknowledged ultimate version of transistor channels, 2D semiconductors are of interest for the development of post-Moore electronics due to their useful properties and all-in-one potentials. Here, the promise and current status of 2D semiconductors and transistors are reviewed, from materials and devices to integrated applications. First, we outline the evolution and challenges of silicon-based integrated circuits, followed by a detailed discussion on the properties and preparation strategies of 2D semiconductors and van der Waals heterostructures. Subsequently, the significant progress of 2D transistors, including device optimization, large-scale integration, and unconventional devices, are presented. We also examine 2D semiconductors for advanced heterogeneous and multifunctional integration beyond CMOS. Finally, the key technical challenges and potential strategies for 2D transistors and integrated circuits are also discussed. We envision that the field of 2D semiconductors and transistors could yield substantial progress in the upcoming years and hope this review will trigger the interest of scientists planning their next experiment.

Digital economy and carbon emission: The coupling effects of the economy in Qinghai region of China

This study provides an in-depth analysis of the complex relationship between the digital economy and carbon emissions, fully drawing on essential principles of environmental economics, coupled economics, and sustainable development theory. Focusing on the Qinghai region in the western province of China, the study employs highly sophisticated methods such as multiple regression analysis and system dynamics modeling to reveal the multidimensional coupling effects between digital economy development and carbon emission dynamics. The study's results clearly show that in the Qinghai region of China, the booming growth of the digital economy is related to carbon emissions. Of particular interest, the study finds that this relationship exhibits a high degree of complexity and non-linearity and evolves gradually over time. Initially, the rapid expansion of the digital economy, accompanied by high energy consumption and increased carbon emissions, posed a significant challenge to environmental protection. However, a clear inverted "U"-shaped relationship has emerged as the digital economy evolves. This key inflection point signals a shift in the landscape as the digital economy begins to deliver some ecological benefits, potentially reducing the trend of carbon emissions in the future. The findings of this study go beyond simple causality and reveal a complex and evolving dynamic relationship between the digital economy and carbon emissions. Through such insights, this study provides a solid academic foundation and carefully constructs actionable policy recommendations to drive sustainable development. These insights apply to the Qinghai region of China and provide valuable references and lessons for other areas facing similar challenges.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

MoS2 Field-Effect Transistor Performance Enhancement by Contact Doping and Defect Passivation via Fluorine Ions and Its Cyclic Field-Assisted Activation

MoS2-based field-effect transistors (FETs) and, in general, transition metal dichalcogenide channels are fundamentally limited by high contact resistance (RC) and intrinsic defects, which results in low drive current and lower carrier mobilities, respectively. This work addresses these issues using a technique based on CF4 plasma treatment in the contacts and further cyclic field-assisted drift and activation of the fluorine ions (F-), which get introduced into the contact region during the CF4 plasma treatment. The F- ions are activated using cyclic pulses applied across the source-drain (S/D) contacts, which leads to their migration to the contact edges via the channel. Further, using ab initio molecular dynamics and density functional theory simulations, these F- ions are found to bond at sulfur (S) vacancies, resulting in their passivation and n-type doping in the channel and near the S/D contacts. An increase in doping results in the narrowing of the Schottky barrier width and a reduction in RC by ∼90%. Additionally, the passivation of S vacancies in the channel enhances the mobility of the FET by ∼150%. The CF4 plasma treatment in contacts and further cyclic field-assisted activation of F- ions resulted in an ON-current (ION) improvement by ∼90% and ∼480% for exfoliated and CVD-grown MoS2, respectively. Moreover, this improvement in ION has been achieved without any deterioration in the ION/IOFF, which was found to be >7-8 orders.

Recent Advances in In-Memory Computing: Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial challenges, including high latency and energy consumption between memory and processing units. In response, in-memory computing has emerged as a promising alternative architecture, enabling computing operations within memory arrays to overcome these limitations. Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays, rapid response times, and ability to emulate biological synapses. Among these devices, two-dimensional (2D) material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing, thanks to their exceptional performance driven by the unique properties of 2D materials, such as layered structures, mechanical flexibility, and the capability to form heterojunctions. This review delves into the state-of-the-art research on 2D material-based memristive arrays, encompassing critical aspects such as material selection, device performance metrics, array structures, and potential applications. Furthermore, it provides a comprehensive overview of the current challenges and limitations associated with these arrays, along with potential solutions. The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing, leveraging the potential of 2D material-based memristive devices.

Synapse-Mimicking Memristors Based on 3,6-Di(tpy)-9-Phenylcarbazole Unimer and Its Copolymer with Cobalt(II) Ions

The title compound, unimer U (tpy stands for 2,2':6',2″-terpyridin-4'-yl end-group), by itself shows the memristor effect with a retention time of 18 h and persistence of 11 h. Its coordination copolymer with Co(II) ions, [CoU]n, exhibits multimodal resistance changes similar to the synaptic responses observed in biological systems. More than 320 cycles of potentiation and depression measured in continuous sequence occurred without observing a significant current change, confirming the operational stability and reproducibility of the device based on the [CoU]n polymer. The synaptic effect of a device with an indium tin oxide (ITO)/[CoU]n/top-electrode (TE) configuration is more pronounced for the device with TE = Au compared to devices with TE = Al or Ga. However, the latter TEs provide a cost-effective approach without any significant compromise in device plasticity. The detected changes in the synaptic weight, about 12% for pair-pulse facilitation and 80% for its depression, together with a millisecond trigger and reading pulses that decay exponentially on the time scale typical of neurosynapses, justify the device's ability to learn and memorize. These properties offer potential applications in neuromorphic computation and brain-inspired synaptic devices.

Coexisting Phases in Transition Metal Dichalcogenides: Overview, Synthesis, Applications, and Prospects

Over the past decade, significant advancements have been made in phase engineering of two-dimensional transition metal dichalcogenides (TMDCs), thereby allowing controlled synthesis of various phases of TMDCs and facile conversion between them. Recently, there has been emerging interest in TMDC coexisting phases, which contain multiple phases within one nanostructured TMDC. By taking advantage of the merits from the component phases, the coexisting phases offer enhanced performance in many aspects compared with single-phase TMDCs. Herein, this review article thoroughly expounds the latest progress and ongoing efforts on the syntheses, properties, and applications of TMDC coexisting phases. The introduction section overviews the main phases of TMDCs (2H, 3R, 1T, 1T', 1Td), along with the advantages of phase coexistence. The subsequent section focuses on the synthesis methods for coexisting phases of TMDCs, with particular attention to local patterning and random formations. Furthermore, on the basis of the versatile properties of TMDC coexisting phases, their applications in magnetism, valleytronics, field-effect transistors, memristors, and catalysis are discussed. Lastly, a perspective is presented on the future development, challenges, and potential opportunities of TMDC coexisting phases. This review aims to provide insights into the phase engineering of 2D materials for both scientific and engineering communities and contribute to further advancements in this emerging field.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

Filament-free memristors for computing

Memristors have attracted increasing attention due to their tremendous potential to accelerate data-centric computing systems. The dynamic reconfiguration of memristive devices in response to external electrical stimuli can provide highly desirable novel functionalities for computing applications when compared with conventional complementary-metal-oxide-semiconductor (CMOS)-based devices. Those most intensively studied and extensively reviewed memristors in the literature so far have been filamentary type memristors, which typically exhibit a relatively large variability from device to device and from switching cycle to cycle. On the other hand, filament-free switching memristors have shown a better uniformity and attractive dynamical properties, which can enable a variety of new computing paradigms but have rarely been reviewed. In this article, a wide range of filament-free switching memristors and their corresponding computing applications are reviewed. Various junction structures, switching properties, and switching principles of filament-free memristors are surveyed and discussed. Furthermore, we introduce recent advances in different computing schemes and their demonstrations based on non-filamentary memristors. This Review aims to present valuable insights and guidelines regarding the key computational primitives and implementations enabled by these filament-free switching memristors.

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.

Multifunctional Multigate One-Transistor with Thin Advanced Materials, Logic-in-Memory, and Artificial Synaptic Behaviors

The high device density and fabrication complexity have hampered the development of the electronics. The advanced designs, which could implement the functions of the circuits with higher device density but less fabrication complexity, are hence required. Meanwhile, the MoS2-based devices have recently attracted considerable attention owing to their advantages such as the ultrathin thickness. However, the MoS2-based multifunctional multigate one-transistor (MGT) designs with logic-in-memory and artificial synaptic functions have rarely been reported. Here, an MGT structure based on the MoS2 channel is proposed, with both the logic-in-memory and artificial synaptic behaviors and with more controllable processes than the manual transfer. The proposed MoS2-based MGT functions could be attributed to the semijunction mechanism and enhanced effect of the additional terminals with improved controllability. This study is the first to demonstrate that the neuromorphic computing, logic gate, and memory functions can all be achieved in a MoS2 MGT device without using any additional layers or plasticity to a transistor. The reported results provide a new strategy for developing brain-like systems and next-generation electronics using multifunctional designs and ultrathin materials.

Recent Progress on Phase Engineering of Nanomaterials

As a key structural parameter, phase depicts the arrangement of atoms in materials. Normally, a nanomaterial exists in its thermodynamically stable crystal phase. With the development of nanotechnology, nanomaterials with unconventional crystal phases, which rarely exist in their bulk counterparts, or amorphous phase have been prepared using carefully controlled reaction conditions. Together these methods are beginning to enable phase engineering of nanomaterials (PEN), i.e., the synthesis of nanomaterials with unconventional phases and the transformation between different phases, to obtain desired properties and functions. This Review summarizes the research progress in the field of PEN. First, we present representative strategies for the direct synthesis of unconventional phases and modulation of phase transformation in diverse kinds of nanomaterials. We cover the synthesis of nanomaterials ranging from metal nanostructures such as Au, Ag, Cu, Pd, and Ru, and their alloys; metal oxides, borides, and carbides; to transition metal dichalcogenides (TMDs) and 2D layered materials. We review synthesis and growth methods ranging from wet-chemical reduction and seed-mediated epitaxial growth to chemical vapor deposition (CVD), high pressure phase transformation, and electron and ion-beam irradiation. After that, we summarize the significant influence of phase on the various properties of unconventional-phase nanomaterials. We also discuss the potential applications of the developed unconventional-phase nanomaterials in different areas including catalysis, electrochemical energy storage (batteries and supercapacitors), solar cells, optoelectronics, and sensing. Finally, we discuss existing challenges and future research directions in PEN.

A review on advanced band-structure engineering with dynamic control for nonvolatile memory based 2D transistors

With advancements in information technology, an enormous amount of data is being generated that must be quickly accessible. However, conventional Si memory cells are approaching their physical limits and will be unable to meet the requirements of intense applications in the future. Notably, 2D atomically thin materials have demonstrated multiple novel physical and chemical properties that can be used to investigate next-generation electronic devices and breakthrough physical limits to continue Moore's law. Band structure is an important semiconductor parameter that determines their electrical and optical properties. In particular, 2D materials have highly tunable bandgaps and Fermi levels that can be achieved through band structure engineering methods such as heterostructure, substrate engineering, chemical doping, intercalation, and electrostatic doping. In particular, dynamic control of band structure engineering can be used in recent advancements in 2D devices to realize nonvolatile storage performance. This study examines recent advancements in 2D memory devices that utilize band structure engineering. The operational mechanisms and memory characteristics are described for each band structure engineering method. Band structure engineering provides a platform for developing new structures and realizing superior performance with respect to nonvolatile memory.

Reconfigurable, non-volatile neuromorphic photovoltaics

The neural network image sensor-which mimics neurobiological functions of the human retina-has recently been demonstrated to simultaneously sense and process optical images. However, highly tunable responsivity concurrent with non-volatile storage of image data in the neural network would allow a transformative leap in compactness and function of these artificial neural networks. Here, we demonstrate a reconfigurable and non-volatile neuromorphic device based on two-dimensional semiconducting metal sulfides that is concurrently a photovoltaic detector. The device is based on a metal-semiconductor-metal (MSM) two-terminal structure with pulse-tunable sulfur vacancies at the M-S junctions. By modulating sulfur vacancy concentrations, the polarities of short-circuit photocurrent can be changed with multiple stable magnitudes. The bias-induced motion of sulfur vacancies leads to highly reconfigurable responsivities by dynamically modulating the Schottky barriers. A convolutional neuromorphic network is finally designed for image processing and object detection using the same device. The results demonstrated that neuromorphic photodetectors can be the key components of visual perception hardware.

Ferroelectric artificial synapse for neuromorphic computing and flexible applications

Research of artificial synapses is increasing in popularity with the development of bioelectronics and the appearance of wearable devices. Because the high-temperature treatment process of inorganic materials is not compatible with flexible substrates, organic ferroelectric materials that are easier to process have emerged as alternatives. An organic synaptic device based on P(VDF-TrFE) was prepared in this study. The device showed reliable P/E endurance over 104 cycles and a data storage retention capability at 80 °C over 104 s. Simultaneously, it possessed excellent synaptic functions, including short-term/ long-term synaptic plasticity and spike-timing-dependent plasticity. In addition, the ferroelectric performance of the device remained stable even under bending (7 mm bending radius) or after 500 bending cycles. This work shows that low-temperature processed organic ferroelectric materials can provide new ideas for the future development of wearable electronics and flexible artificial synapses.

Chemical Modulation of Metal-Insulator Transition toward Multifunctional Applications in Vanadium Dioxide Nanostructures

The metal-insulator transition (MIT) of vanadium dioxide (VO2 ) has been of great interest in materials science for both fundamental understanding of strongly correlated physics and a wide range of applications in optics, thermotics, spintronics, and electronics. Due to the merits of chemical interaction with accessibility, versatility, and tunability, chemical modification provides a new perspective to regulate the MIT of VO2 , endowing VO2 with exciting properties and improved functionalities. In the past few years, plenty of efforts have been devoted to exploring innovative chemical approaches for the synthesis and MIT modulation of VO2 nanostructures, greatly contributing to the understanding of electronic correlations and development of MIT-driven functionalities. Here, this comprehensive review summarizes the recent achievements in chemical synthesis of VO2 and its MIT modulation involving hydrogen incorporation, composition engineering, surface modification, and electrochemical gating. The newly appearing phenomena, mechanism of electronic correlation, and structural instability are discussed. Furthermore, progresses related to MIT-driven applications are presented, such as the smart window, optoelectronic detector, thermal microactuator, thermal radiation coating, spintronic device, memristive, and neuromorphic device. Finally, the challenges and prospects in future research of chemical modulation and functional applications of VO2 MIT are also provided.

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.

Tuning and exploiting interlayer coupling in two-dimensional van der Waals heterostructures

Two-dimensional (2D) layered materials can stack into new material systems, with van der Waals (vdW) interaction between the adjacent constituent layers. This stacking process of 2D atomic layers creates a new degree of freedom-interlayer interface between two adjacent layers-that can be independently studied and tuned from the intralayer degree of freedom. In such heterostructures (HSs), the physical properties are largely determined by the vdW interaction between the individual layers,i.e.interlayer coupling, which can be effectively tuned by a number of means. In this review, we summarize and discuss a number of such approaches, including stacking order, electric field, intercalation, and pressure, with both their experimental demonstrations and theoretical predictions. A comprehensive overview of the modulation on structural, optical, electrical, and magnetic properties by these four approaches are also presented. We conclude this review by discussing several prospective research directions in 2D HSs field, including fundamental physics study, property tuning techniques, and future applications.

Emerging Opportunities for 2D Materials in Neuromorphic Computing

Recently, two-dimensional (2D) materials and their heterostructures have been recognized as the foundation for future brain-like neuromorphic computing devices. Two-dimensional materials possess unique characteristics such as near-atomic thickness, dangling-bond-free surfaces, and excellent mechanical properties. These features, which traditional electronic materials cannot achieve, hold great promise for high-performance neuromorphic computing devices with the advantages of high energy efficiency and integration density. This article provides a comprehensive overview of various 2D materials, including graphene, transition metal dichalcogenides (TMDs), hexagonal boron nitride (h-BN), and black phosphorus (BP), for neuromorphic computing applications. The potential of these materials in neuromorphic computing is discussed from the perspectives of material properties, growth methods, and device operation principles.

Quantum imaging of the reconfigurable VO2 synaptic electronics for neuromorphic computing

Neuromorphic computing has shown remarkable capabilities in silicon-based artificial intelligence, which can be optimized by using Mott materials for functional synaptic connections. However, the research efforts focus on two-terminal artificial synapses and envisioned the networks controlled by silicon-based circuits, which is difficult to develop and integrate. Here, we propose a dynamic network with laser-controlled conducting filaments based on electric field-induced local insulator-metal transition of vanadium dioxide. Quantum sensing is used to realize conductivity-sensitive imaging of conducting filament. We find that the location of filament formation is manipulated by focused laser, which is applicable to simulate the dynamical synaptic connections between the neurons. The ability to process signals with both long-term and short-term potentiation is further demonstrated with ~60 times on/off ratio while switching the pathways. This study opens the door to the development of dynamic network structures depending on easily controlled conduction pathways, mimicking the biological nervous systems.

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.

Phase Engineering and Synchrotron-Based Study on Two-Dimensional Energy Nanomaterials

In recent years, there has been significant interest in the development of two-dimensional (2D) nanomaterials with unique physicochemical properties for various energy applications. These properties are often derived from the phase structures established through a range of physical and chemical design strategies. A concrete analysis of the phase structures and real reaction mechanisms of 2D energy nanomaterials requires advanced characterization methods that offer valuable information as much as possible. Here, we present a comprehensive review on the phase engineering of typical 2D nanomaterials with the focus of synchrotron radiation characterizations. In particular, the intrinsic defects, atomic doping, intercalation, and heterogeneous interfaces on 2D nanomaterials are introduced, together with their applications in energy-related fields. Among them, synchrotron-based multiple spectroscopic techniques are emphasized to reveal their intrinsic phases and structures. More importantly, various in situ methods are employed to provide deep insights into their structural evolutions under working conditions or reaction processes of 2D energy nanomaterials. Finally, conclusions and research perspectives on the future outlook for the further development of 2D energy nanomaterials and synchrotron radiation light sources and integrated techniques are discussed.

Photoinduced Nonvolatile Resistive Switching Behavior in Oxygen-Doped MoS2 for a Neuromorphic Vision System

Controlling resistance by external fields provides fascinating opportunities for the development of novel devices and circuits, such as temperature-field-induced superconductors, magnetic-field-triggered giant magnetoresistance devices, and electric-field-operated flash memories. In this work, we demonstrate a light-triggered nonvolatile resistive switching behavior in oxygen-doped MoS2. The two-terminal devices exhibit stable light-modulated resistive switching characteristics and optically tunable synaptic properties with an on/off ratio of up to 104. The integrated device with crossbar architecture enables simultaneous image sensing, preprocessing, and storage in a single device, thereby increasing the training efficiency and recognition rate of image recognition tasks. This work presents a novel pathway to develop the next generation of light-controlled memory and artificial vision systems for neuromorphic computing.

Complex Oxides for Brain-Inspired Computing: A Review

The fields of brain-inspired computing, robotics, and, more broadly, artificial intelligence (AI) seek to implement knowledge gleaned from the natural world into human-designed electronics and machines. In this review, the opportunities presented by complex oxides, a class of electronic ceramic materials whose properties can be elegantly tuned by doping, electron interactions, and a variety of external stimuli near room temperature, are discussed. The review begins with a discussion of natural intelligence at the elementary level in the nervous system, followed by collective intelligence and learning at the animal colony level mediated by social interactions. An important aspect highlighted is the vast spatial and temporal scales involved in learning and memory. The focus then turns to collective phenomena, such as metal-to-insulator transitions (MITs), ferroelectricity, and related examples, to highlight recent demonstrations of artificial neurons, synapses, and circuits and their learning. First-principles theoretical treatments of the electronic structure, and in situ synchrotron spectroscopy of operating devices are then discussed. The implementation of the experimental characteristics into neural networks and algorithm design is then revewed. Finally, outstanding materials challenges that require a microscopic understanding of the physical mechanisms, which will be essential for advancing the frontiers of neuromorphic computing, are highlighted.

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.

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.

Thermal Management in Neuromorphic Materials, Devices, and Networks

Machine learning has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." Biological systems inspire the fundamental approach for this new computing paradigm: using neural networks to classify large amounts of data into sorting categories. Current machine-learning schemes implement simulated neurons and synapses on standard computers based on a von Neumann architecture. This approach is inefficient in energy consumption, and thermal management, motivating the search for hardware-based systems that imitate the brain. Here, the present state of thermal management of neuromorphic computing technology and the challenges and opportunities of the energy-efficient implementation of neuromorphic devices are considered. The main features of brain-inspired computing and quantum materials for implementing neuromorphic devices are briefly described, the brain criticality and resistive switching-based neuromorphic devices are discussed, the energy and electrical considerations for spiking-based computation are presented, the fundamental features of the brain's thermal regulation are addressed, the physical mechanisms for thermal management and thermoelectric control of materials and neuromorphic devices are analyzed, and challenges and new avenues for implementing energy-efficient computing are described.

Reinforcing Synaptic Plasticity of Defect-Tolerant States in Alloyed 2D Artificial Transistors

While two-dimensional (2D) materials possess the desirable future of neuromorphic computing platforms, unstable charging and de-trapping processes, which are inherited from uncontrollable states, such as the interface trap between nanocrystals and dielectric layers, can deteriorate the synaptic plasticity in field-effect transistors. Here, we report a facile and effective strategy to promote artificial synaptic devices by providing physical doping in 2D transition-metal dichalcogenide nanomaterials. Our experiments demonstrate that the introduction of niobium (Nb) into 2D WSe2 nanomaterials produces charge trap levels in the band gap and retards the decay of the trapped charges, thereby accelerating the artificial synaptic plasticity by encouraging improved short-/long-term plasticity, increased multilevel states, lower power consumption, and better symmetry and asymmetry ratios. Density functional theory calculations also proved that the addition of Nb to 2D WSe2 generates defect tolerance levels, thereby governing the charging and de-trapping mechanisms of the synaptic devices. Physically doped electronic synapses are expected to be a promising strategy for the development of bioinspired artificial electronic devices.

Two-Terminal MoS2 Memristor and the Homogeneous Integration with a MoS2 Transistor for Neural Networks

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS₂ memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS₂ transistors to realize one-transistor-one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning

Multi-terminal memristor and memtransistor (MT-MEMs) has successfully performed complex functions of heterosynaptic plasticity in synapse. However, theses MT-MEMs lack the ability to emulate membrane potential of neuron in multiple neuronal connections. Here, we demonstrate multi-neuron connection using a multi-terminal floating-gate memristor (MT-FGMEM). The variable Fermi level (E_F) in graphene allows charging and discharging of MT-FGMEM using horizontally distant multiple electrodes. Our MT-FGMEM demonstrates high on/off ratio over 10⁵ at 1000 s retention about ~10,000 times higher than other MT-MEMs. The linear behavior between current (I_D) and floating gate potential (V_FG) in triode region of MT-FGMEM allows for accurate spike integration at the neuron membrane. The MT-FGMEM fully mimics the temporal and spatial summation of multi-neuron connections based on leaky-integrate-and-fire (LIF) functionality. Our artificial neuron (150 pJ) significantly reduces the energy consumption by 100,000 times compared to conventional neurons based on silicon integrated circuits (11.7 μJ). By integrating neurons and synapses using MT-FGMEMs, a spiking neurosynaptic training and classification of directional lines functioned in visual area one (V1) is successfully emulated based on neuron's LIF and synapse's spike-timing-dependent plasticity (STDP) functions. Simulation of unsupervised learning based on our artificial neuron and synapse achieves a learning accuracy of 83.08% on the unlabeled MNIST handwritten dataset.

Multiple-State Nonvolatile Memory Based on Ultrathin Indium Oxide Film via Liquid Metal Printing

In this work, the ultrathin two-dimensional (2D) indium oxide (InO_x) with a large area of more than 100 μm² and a high degree of uniformity was automatically peeled off from indium by the liquid-metal printing technique. Raman and optical measurements revealed that 2D-InO_x has a polycrystalline cubic structure. By altering the printing temperature which affects the crystallinity of 2D-InO_x, the mechanism of the existence and disappearance of memristive characteristics was established. The tunable characteristics of the 2D-InO_x memristor with reproducible one-order switching was manifest from the electrical measurements. Further adjustable multistate characteristics of the 2D-InO_x memristor and its resistance switching mechanism were evaluated. A detailed examination of the memristive process demonstrated the Ca²⁺ mimic dynamic in 2D-InO_x memristors as well as the fundamental principles underlying biological and artificial synapses. These surveys allow us to comprehend a 2D-InO_x memristor using the liquid-metal printing technique and could be applied to future neuromorphic applications and in the field of revolutionary 2D material exploration.

Nanograin network memory with reconfigurable percolation paths for synaptic interactions

The development of memory devices with functions that simultaneously process and store data is required for efficient computation. To achieve this, artificial synaptic devices have been proposed because they can construct hybrid networks with biological neurons and perform neuromorphic computation. However, irreversible aging of these electrical devices causes unavoidable performance degradation. Although several photonic approaches to controlling currents have been suggested, suppression of current levels and switching of analog conductance in a simple photonic manner remain challenging. Here, we demonstrated a nanograin network memory using reconfigurable percolation paths in a single Si nanowire with solid core/porous shell and pure solid core segments. The electrical and photonic control of current percolation paths enabled the analog and reversible adjustment of the persistent current level, exhibiting memory behavior and current suppression in this single nanowire device. In addition, the synaptic behaviors of memory and erasure were demonstrated through potentiation and habituation processes. Photonic habituation was achieved using laser illumination on the porous nanowire shell, with a linear decrease in the postsynaptic current. Furthermore, synaptic elimination was emulated using two adjacent devices interconnected on a single nanowire. Therefore, electrical and photonic reconfiguration of the conductive paths in Si nanograin networks will pave the way for next-generation nanodevice technologies.

Hardware Trojans based on two-dimensional memtransistors

Hardware Trojans (HTs) have emerged as a major security threat for integrated circuits (ICs) owing to the involvement of untrustworthy actors in the globally distributed semiconductor supply chain. HTs are intentional malicious modifications, which remain undetectable through simple electrical measurements but can cause catastrophic failure in the functioning of ICs in mission critical applications. In this article, we show how two-dimensional (2D) material based in-memory computing elements such as memtransistors can be used as hardware Trojans. We found that logic gates based on 2D memtransistors can be made to malfunction by exploiting their inherent programming capabilities. While we use 2D memtransistor-based ICs as the testbed for our demonstration, the results are equally applicable to any state-of-the-art and emerging in-memory computing technologies.