Reconfigurable 2D WSe2 -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity

High-Performance 2D Ambipolar MoTe2 Lateral Memristors by Mild Oxidation

2D transition metal dichalcogenides (TMDCs) have been intensively explored in memristors for brain-inspired computing. Oxidation, which is usually unavoidable and harmful in 2D TMDCs, could also be used to enhance their memristive performances. However, it is still unclear how oxidation affects the resistive switching behaviors of 2D ambipolar TMDCs. In this work, a mild oxidation strategy is developed to greatly enhance the resistive switching ratio of ambipolar 2H-MoTe2 lateral memristors by more than 10 times. Such an enhancement results from the amplified doping due to O2 and H2O adsorption and the optimization of effective gate voltage distribution by mild oxidation. Moreover, the ambipolarity of 2H-MoTe2 also enables a change of resistive switching direction, which is uncommon in 2D memristors. Consequently, as an artificial synapse, the MoTe2 device exhibits a large dynamic range (≈200) and a good linearity (1.01) in long-term potentiation and depression, as well as a high-accuracy handwritten digit recognition (>96%). This work not only provides a feasible and effective way to enhance the memristive performance of 2D ambipolar materials, but also deepens the understanding of hidden mechanisms for RS behaviors in oxidized 2D materials.

Programmable Optoelectronic Synaptic Transistors Based on MoS2/Ta2NiS5 Heterojunction for Energy-Efficient Neuromorphic Optical Operation

The optoelectronic synaptic transistors with various functions, broad spectral perception, and low power consumption are an urgent need for the development of advanced optical neural network systems. However, it remains a great challenge to realize the functional diversification of the systems on a single device. 2D van der Waals (vdW) materials can combine unique properties by stacking with each other to form heterojunctions, which may provide a strategy for solving this problem. Herein, an all-2D vdW heterojunction-based programmable optoelectronic synaptic transistor based on MoS2/Ta2NiS5 heterojunctions is demonstrated. The device implements reconfigurable, multilevel non-volatile memory (NVM) states through sequential modulation of multiple optical and electrical stimuli to achieve broadband (532-808 nm), energy-efficient (17.2 fJ), hetero-synaptic functionality in a bionic manner. The intrinsic working mechanisms of the photogating effect caused by band alignment and the interfacial trapping defect modulation induced by gate voltage are revealed by Kelvin-probe force microscopy (KPFM) measurements and carrier transport analysis. Overall, the (opto)electronic synaptic weight controllability for combined in-sensor and in-memory logic processors is realized by the heterojunction properties. The proposed findings facilitate the technical realization of generic all 2D hetero-synapses for future artificial vision systems, opto-logical systems, and Internet of Things (IoT) entities.

Ferroelectricity-Defects Synergistic Artificial Synapses for High Recognition Accuracy Neuromorphic Computing

The ability of ferroelectric memristors to modulate conductance and offer multilevel storage has garnered significant attention in the realm of artificial synapses. On one hand, the resistance change of ferroelectric memristors mainly depends on the polarization reversal. On the other hand, the defects such as oxygen vacancies, which are inevitable presence during high-temperature processes, can undergo diffusion drift with the polarization reversal, thereby change the interface potential barrier. Thus, it is both desirable and necessary to investigate the synergistic effect of ferroelectricity and defects. Here, we prepare BaTiO3 ferroelectric memristor by pulse laser deposition and achieve resistance switching through the synergistic effect of ferroelectricity and oxygen vacancies. The memristor shows excellent switching characteristics with a large switching ratio (104) and good stability (103 s). It effectively emulates the features of artificial synapses and accomplishes decimal logical neural computing. In the neuromorphic system crafted with the memristor, the recognition accuracy of the 28 × 28 pixel image reaches 94.9%. These findings strongly support the research of ferroelectric memristors in neuromorphic devices.

Submicron Memtransistors Made from Monocrystalline Molybdenum Disulfide

Multiterminal memtransistors made from two-dimensional (2D) materials have garnered increasing attention in the pursuit of low-power heterosynaptic neuromorphic circuits. However, existing 2D memtransistors tend to necessitate high set voltages (>1 V) or feature defective channels, posing concerns regarding material integrity and intrinsic properties. Herein, we present a monocrystalline monolayer MoS2 memtransistor designed for operation within submicron regimes. Under reverse drain bias sweeps, our experiments reveal memristive behavior within the device, further controllable through modulation of the gate terminal. This controllability facilitates the consistent manifestation of multistate memory effects. Notably, the memtransistor behavior becomes more significant as the channel length diminishes, particularly with channel lengths below 1.6 μm, showcasing an increase in the switching ratio alongside a decrease in the set voltage with the decreasing channel length. Our optimized memtransistor demonstrates the ability to exhibit individual resistance states spanning 5 orders of magnitude, with switching drain voltages of approximately 0.05 V. To elucidate these findings, we investigate hot carrier effects and their interplay with oxide traps within the HfO2 dielectric. This work highlights the importance of memtransisor behavior in highly scaled 2D transistors, particularly those featuring low contact resistances. This understanding holds the potential to tailor memory characteristics essential for the development of energy-efficient neuromorphic devices.

From Material to Cameras: Low-Dimensional Photodetector Arrays on CMOS

The last two decades have witnessed a dramatic increase in research on low-dimensional material with exceptional optoelectronic properties. While low-dimensional materials offer exciting new opportunities for imaging, their integration in practical applications has been slow. In fact, most existing reports are based on single-pixel devices that cannot rival the quantity and quality of information provided by massively parallelized mega-pixel imagers based on complementary metal-oxide semiconductor (CMOS) readout electronics. The first goal of this review is to present new opportunities in producing high-resolution cameras using these new materials. New photodetection methods and materials in the field are presented, and the challenges involved in their integration on CMOS chips for making high-resolution cameras are discussed. Practical approaches are then presented to address these challenges and methods to integrate low-dimensional material on CMOS. It is also shown that such integrations could be used for ultra-low noise and massively parallel testing of new material and devices. The second goal of this review is to present the colossal untapped potential of low-dimensional material in enabling the next-generation of low-cost and high-performance cameras. It is proposed that low-dimensional materials have the natural ability to create excellent bio-inspired artificial imaging systems with unique features such as in-pixel computing, multi-band imaging, and curved retinas.

An Artificial Olfactory System Based on a Memristor Can Simulate Organ Injury and Functions in Air Purification

Artificial olfactory systems are receiving increasing attention because of their potential applications in humanoid robots, artificial noses, and the next generation of human-computer interactions. However, simulating the human olfactory system, which recognizes, remembers, and automatically takes protective measures against gases, remains a challenge. In this paper, a WO3-TiO2@Ag NPs (silver nanoparticle) gas sensor was prepared by the sol-gel method, and an Al/pectin:AgNP/ITO memristor was prepared by spin coating and vacuum evaporation. The gas sensor has been combined with the memristor to simulate physical damage to humans in a dangerous gas environment for a long time, and an artificial olfactory system is constructed by field-programmable gate array external control. The WO3-TiO2@Ag NPs gas sensor can sense and identify ethanol vapor through changes in resistance, and the signal transmitted to the pectin-based memristor can switch the resistance state of the memristor to store gas information. Furthermore, the activation of the memristor can also trigger rotation of the fan to purify the gas and reduce damage caused by excessive exposure to dangerous gases. This artificial olfactory system provides a promising strategy for the development of artificial intelligence and human-computer interaction systems.

Reconfigurable Neuromorphic Computing: Materials, Devices, and Integration

Neuromorphic computing has been attracting ever-increasing attention due to superior energy efficiency, with great promise to promote the next wave of artificial general intelligence in the post-Moore era. Current approaches are, however, broadly designed for stationary and unitary assignments, thus encountering reluctant interconnections, power consumption, and data-intensive computing in that domain. Reconfigurable neuromorphic computing, an on-demand paradigm inspired by the inherent programmability of brain, can maximally reallocate finite resources to perform the proliferation of reproducibly brain-inspired functions, highlighting a disruptive framework for bridging the gap between different primitives. Although relevant research has flourished in diverse materials and devices with novel mechanisms and architectures, a precise overview remains blank and urgently desirable. Herein, the recent strides along this pursuit are systematically reviewed from material, device, and integration perspectives. At the material and device level, one comprehensively conclude the dominant mechanisms for reconfigurability, categorized into ion migration, carrier migration, phase transition, spintronics, and photonics. Integration-level developments for reconfigurable neuromorphic computing are also exhibited. Finally, a perspective on the future challenges for reconfigurable neuromorphic computing is discussed, definitely expanding its horizon for scientific communities.

Negative Capacitance Field Effect Transistors based on Van der Waals 2D Materials

Steep subthreshold swing (SS) is a decisive index for low energy consumption devices. However, the SS of conventional field effect transistors (FETs) has suffered from Boltzmann Tyranny, which limits the scaling of SS to sub-60 mV dec-1 at room temperature. Ferroelectric gate stack with negative capacitance (NC) is proved to reduce the SS effectively by the amplification of the gate voltage. With the application of 2D ferroelectric materials, the NC FETs can be further improved in performance and downscaled to a smaller dimension as well. This review introduces some related concepts for in-depth understanding of NC FETs, including the NC, internal gate voltage, SS, negative drain-induced barrier lowering, negative differential resistance, single-domain state, and multi-domain state. Meanwhile, this work summarizes the recent advances of the 2D NC FETs. Moreover, the electrical characteristics of some high-performance NC FETs are expressed as well. The factors which affect the performance of the 2D NC FETs are also presented in this paper. Finally, this work gives a brief summary and outlook for the 2D NC FETs.

Porous crystalline materials for memories and neuromorphic computing systems

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance. This review highlights the strategies for preparing porous crystalline materials films and discusses their advancements in memory and neuromorphic electronics. It also provides a detailed comparative analysis and presents the existing challenges and future research directions, which can attract the experts from various fields (e.g., materials scientists, chemists, and engineers) with the aim of promoting the applications of porous crystalline materials in memory and neuromorphic computing systems.

Reservoir Computing with Charge-Trap Memory Based on a MoS2 Channel for Neuromorphic Engineering

Novel memory devices are essential for developing low power, fast, and accurate in-memory computing and neuromorphic engineering concepts that can compete with the conventional complementary metal-oxide-semiconductor (CMOS) digital processors. 2D semiconductors provide a novel platform for advanced semiconductors with atomic thickness, low-current operation, and capability of 3D integration. This work presents a charge-trap memory (CTM) device with a MoS2 channel where memory operation arises, thanks to electron trapping/detrapping at interface states. Transistor operation, memory characteristics, and synaptic potentiation/depression for neuromorphic applications are demonstrated. The CTM device shows outstanding linearity of the potentiation by applied drain pulses of equal amplitude. Finally, pattern recognition is demonstrated by reservoir computing where the input pattern is applied as a stimulation of the MoS2 -based CTMs, while the output current after stimulation is processed by a feedforward readout network. The good accuracy, the low current operation, and the robustness to input random bit flip makes the CTM device a promising technology for future high-density neuromorphic computing concepts.

Quantum Topological Neuristors for Advanced Neuromorphic Intelligent Systems

Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning-relearning-forgetting stages is demonstrated. Critically, to emulate the real-time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision-making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next-gen neuromorphic computing for the development of intelligent machines and humanoids.

High-Performance Artificial Synapse Based on CVD-Grown WSe2 Flakes with Intrinsic Defects

High-performance artificial synaptic devices with rich functions are highly desired for the development of an advanced brain-like neuromorphic system. Here, we prepare synaptic devices based on a CVD-grown WSe₂ flake, which has an unusual morphology of nested triangles. The WSe₂ transistor exhibits robust synaptic behaviors such as excitatory postsynaptic current, paired-pulse facilitation, short-time plasticity, and long-time plasticity. Furthermore, due to its high sensitivity to light illumination, the WSe₂ transistor exhibits excellent light-dosage-dependent and light wavelength-dependent plasticity, which endow the synaptic device with more intelligent learning and memory functions. In addition, WSe₂ optoelectronic synapses can mimic "learning experience" behavior and associative learning behavior like the brain. An artificial neural network is simulated for pattern recognition of hand-written digital images in the MNIST data set and the best recognition accuracy could reach 92.9% based on weight updating training of our WSe₂ device. Detailed surface potential analysis and PL characterization reveal that the intrinsic defects generated in growth are dominantly responsible for the controllable synaptic plasticity. Our work suggests that the CVD-grown WSe₂ flake with intrinsic defects capable of robust trapping/de-trapping charges holds great application prospects in future high-performance neuromorphic computation.

Pseudo-transistors for emerging neuromorphic electronics

Artificial synaptic devices are the cornerstone of neuromorphic electronics. The development of new artificial synaptic devices and the simulation of biological synaptic computational functions are important tasks in the field of neuromorphic electronics. Although two-terminal memristors and three-terminal synaptic transistors have exhibited significant capabilities in the artificial synapse, more stable devices and simpler integration are needed in practical applications. Combining the configuration advantages of memristors and transistors, a novel pseudo-transistor is proposed. Here, recent advances in the development of pseudo-transistor-based neuromorphic electronics in recent years are reviewed. The working mechanisms, device structures and materials of three typical pseudo-transistors, including tunneling random access memory (TRAM), memflash and memtransistor, are comprehensively discussed. Finally, the future development and challenges in this field are emphasized.

Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network

Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture. As computing operations based on von Neumann hardware rely on continuous memory transport between processing units and memory, fundamental limitations arise in terms of power consumption and integration density. In biological synapses, chemical stimulation induces information transfer from the pre- to the post-neuron. The memristor operates as resistive random-access memory (RRAM) and is incorporated into the hardware for neuromorphic computing. Hardware composed of synaptic memristor arrays is expected to lead to further breakthroughs owing to their biomimetic in-memory processing capabilities, low power consumption, and amenability to integration; these aspects satisfy the upcoming demands of artificial intelligence for higher computational loads. Among the tremendous efforts toward achieving human-brain-like electronics, layered 2D materials have demonstrated significant potential owing to their outstanding electronic and physical properties, facile integration with other materials, and low-power computing. This review discusses the memristive characteristics of various 2D materials (heterostructures, defect-engineered materials, and alloy materials) used in neuromorphic computing for image segregation or pattern recognition. Neuromorphic computing, the most powerful artificial networks for complicated image processing and recognition, represent a breakthrough in artificial intelligence owing to their enhanced performance and lower power consumption compared with von Neumann architectures. A hardware-implemented CNN with weight control based on synaptic memristor arrays is expected to be a promising candidate for future electronics in society, offering a solution based on non-von Neumann hardware. This emerging paradigm changes the computing algorithm using entirely hardware-connected edge computing and deep neural networks.

Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

As the boom of data storage and processing, brain-inspired computing provides an effective approach to solve the current problem. Various emerging materials and devices have been reported to promote the development of neuromorphic computing. Thereinto, the neuromorphic device represented by memristor has attracted extensive research due to its outstanding property to emulate the brain's functions from synaptic plasticity, sensory-memory neurons to some intelligent behaviors of living creatures. Herein, we mainly review the progress of these brain functions mimicked by neuromorphic devices, concentrating on synapse (i.e. various synaptic plasticity trigger by electricity and/or light), neurons (including the various sensory nervous system) and intelligent behaviors (such as conditioned reflex represented by Pavlov's dog experiment). Finally, some challenges and prospects related to neuromorphic devices are presented.

Synaptic and Gradual Conductance Switching Behaviors in CeO2/Nb-SrTiO3 Heterojunction Memristors for Electrocardiogram Signal Recognition

The synaptic properties of memristors have been widely studied. However, researchers are still committed to solving various challenges, including the study of highly reliable memristors with comprehensive synaptic functions and memristors that simulate highly complex neurological learning rules. In this work, we report a CeO₂/Nb-SrTiO₃ heterojunction memristor whose conductance could be gradually tuned under both positive and negative pulse trains. Due to the gradual conductance switching behavior and the high switching ratio (10⁵), the CeO₂/Nb-SrTiO₃ heterojunction memristor could dutifully mimic biosynaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation and depression (PPF/PPD), spike amplitude-dependent plasticity (SADP), spike duration-dependent plasticity (SDDP), spike rate-dependent plasticity (SRDP), paired/triplet spiking-time-dependent plasticity (STDP), and Bienenstock-Cooper-Munro (BCM) rules. Moreover, a convolutional neural network based on the memristors is constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 93%. Besides, the recognition accuracy of the handwriting digit reaches 96%. These studies broaden the research scope of high-level synaptic behavior and lay a foundation for the future full synaptic memristor networks.

Emerging electrolyte-gated transistors for neuromorphic perception

With the rapid development of intelligent robotics, the Internet of Things, and smart sensor technologies, great enthusiasm has been devoted to developing next-generation intelligent systems for the emulation of advanced perception functions of humans. Neuromorphic devices, capable of emulating the learning, memory, analysis, and recognition functions of biological neural systems, offer solutions to intelligently process sensory information. As one of the most important neuromorphic devices, Electrolyte-gated transistors (EGTs) have shown great promise in implementing various vital neural functions and good compatibility with sensors. This review introduces the materials, operating principle, and performances of EGTs, followed by discussing the recent progress of EGTs for synapse and neuron emulation. Integrating EGTs with sensors that faithfully emulate diverse perception functions of humans such as tactile and visual perception is discussed. The challenges of EGTs for further development are given.

Atomically Thin Synapse Networks on Van Der Waals Photo-Memtransistors

A new type of atomically thin synaptic network on van der Waals (vdW) heterostructures is reported, where each ultrasmall cell (≈2 nm thick) built with trilayer WS₂ semiconductor acts as a gate-tunable photoactive synapse, i.e., a photo-memtransistor. A train of UV pulses onto the WS₂ memristor generates dopants in atomic-level precision by direct light-lattice interactions, which, along with the gate tunability, leads to the accurate modulation of the channel conductance for potentiation and depression of the synaptic cells. Such synaptic dynamics can be explained by a parallel atomistic resistor network model. In addition, it is shown that such a device scheme can generally be realized in other 2D vdW semiconductors, such as MoS₂ , MoSe₂ , MoTe₂ , and WSe₂ . Demonstration of these atomically thin photo-memtransistor arrays, where the synaptic weights can be tuned for the atomistic defect density, provides implications for a new type of artificial neural networks for parallel matrix computations with an ultrahigh integration density.

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems

Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile memory (NVM) based on resistive switches, phase-change memory, and magnetic tunnel junctions has shown potential for implementing neural networks, additional multi-terminal device concepts are required for more sophisticated bio-realistic functions. Of particular interest are memtransistors based on low-dimensional nanomaterials, which are capable of electrostatically tuning memory and learning behavior at the device level. Herein, a conceptual overview of the memtransistor is provided in the context of neuromorphic circuits. Recent progress is surveyed for memtransistors and related multi-terminal NVM devices including dual-gated floating-gate memories, dual-gated ferroelectric transistors, and dual-gated van der Waals heterojunctions. The different materials systems and device architectures are classified based on the degree of control and relative tunability of synaptic behavior, with an emphasis on device concepts that harness the reduced dimensionality, weak electrostatic screening, and phase-changes properties of nanomaterials. Finally, strategies for achieving wafer-scale integration of memtransistors and multi-terminal NVM devices are delineated, with specific attention given to the materials challenges for practical neuromorphic circuits.

Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2-x Heterostructure Memtransistor

Electrically tunable resistive switching of a polycrystalline MoS₂-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS₂ grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS₂/ZrO_2-x heterostructure. Compared to the control device with the MoS₂ semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO_2-x layer below the MoS₂, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS₂/ZrO_2-x memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS₂/ZrO_2-x memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.

Large Memory Window of van der Waals Heterostructure Devices Based on MOCVD-Grown 2D Layered Ge4 Se9

Van der Waals (vdW) heterostructures have drawn much interest over the last decade owing to their absence of dangling bonds and their intriguing low-dimensional properties. The emergence of 2D materials has enabled the achievement of significant progress in both the discovery of physical phenomena and the realization of superior devices. In this work, the group IV metal chalcogenide 2D-layered Ge₄ Se₉ is introduced as a new selection of insulating vdW material. 2D-layered Ge₄ Se₉ is synthesized with a rectangular shape using the metalcorganic chemical vapor deposition system using a liquid germanium precursor at 240 °C. By stacking the Ge₄ Se₉ and MoS₂ , vdW heterostructure devices are fabricated with a giant memory window of 129 V by sweeping back gate range of ±80 V. The gate-independent decay time reveals that the large hysteresis is induced by the interfacial charge transfer, which originates from the low band offset. Moreover, repeatable conductance changes are observed over the 2250 pulses with low non-linearity values of 0.26 and 0.95 for potentiation and depression curves, respectively. The energy consumption of the MoS₂ /Ge₄ Se₉ device is about 15 fJ for operating energy and the learning accuracy of image classification reaches 88.3%, which further proves the great potential of artificial synapses.

Advances in Emerging Photonic Memristive and Memristive-Like Devices

Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. Recently, the potential of various emerging materials and structures for photonic memristive and memristive-like devices has attracted tremendous research efforts, generating various novel theories, mechanisms, and applications. Limited by the ambiguity of the mechanism and the reliability of the material, the development and commercialization of such devices are still rare and in their infancy. Therefore, a detailed and systematic review of photonic memristive and memristive-like devices is needed to further promote its development. In this review, the resistive switching mechanisms of photonic memristive and memristive-like devices are first elaborated. Then, a systematic investigation of the active materials, which induce a pivotal influence in the overall performance of photonic memristive and memristive-like devices, is highlighted and evaluated in various indicators. Finally, the recent advanced applications are summarized and discussed. In a word, it is believed that this review provides an extensive impact on many fields of photonic memristive and memristive-like devices, and lay a foundation for academic research and commercial applications.