Electronic/Optoelectronic Memory Device Enabled by Tellurium-based 2D van der Waals Heterostructure for in-Sensor Reservoir Computing at the Optical Communication Band

Artificial Axon with Dendritic-like Plasticity by Biomimetic Interface Engineering of Anisotropic Two-Dimensional Tellurium

Spiking neural network (SNN) hardware relies on implicit assumptions that prioritize dendritic/synaptic learning above axon/synaptic concerns, compromising performances in signal capacity, accuracy, and compactness of SNN systems. Herein, we develop an artificial axon by utilizing the heterogeneity and interface state tunability in anisotropic two-dimensional (2D) tellurium (Te). By operating a multiterminal axon under the bioelectricity level, the device achieved neuron-like heterogeneous axon dynamics expansion (∼258%). An excellent dendritic-like tunability (∼197%) exhibits gain on the axons. The synergistic axon-dendrite optimization device exhibits 5-bit programmable conductance, signal filtering, and input enhancing. The accuracy of recognizing data sets based on the SNN algorithm demonstrates efficient optimization (5.2% higher accuracy) of networks by the device features, especially in the case of performing image preprocessing. This artificial neuron solution with anisotropic 2D materials utilizing biomimetic interface engineering provides a universal strategy for compact, high-precision parallel architecture of SNN hardware.

Neuromorphic Light-Responsive Organic Matter for in Materia Reservoir Computing

Materials able to sense and respond to external stimuli by adapting their internal state to process and store information, represent promising candidates for implementing neuromorphic functionalities and brain-inspired computing paradigms. In this context, neuromorphic systems based on light-responsive materials enable the use of light as information carrier, allowing to emulate basic functions of the human retina. In this work it is demonstrated that optically-induced molecular dynamics in azopolymers can be exploited for neuromorphic-type of data processing in the analog domain and for computing at the matter level (i.e., in materia). Besides showing that azopolymers can be exploited for data storage, it is demonstrated that the adaptiveness of these materials enables the implementation of synaptic functionalities including short-term memory, long-term memory, and visual memory. Results show that azopolymers allow event detection and motion perception, enabling physical implementation of information processing schemes requiring real-time analysis of spatio-temporal inputs. Furthermore, it is shown that light-induced dynamics can be exploited for the in materia implementation of the unconventional computing paradigm denoted as reservoir computing. This work underscores the potential of azopolymers as promising materials for developing adaptive, intelligent photo-responsive systems that mimic some of the complex processing abilities of biological systems.

Investigating Floating-Gate Topology Influence on van der Waals Memory Performance

As a critical storage technology, the material selection and structural design of flash memory devices are pivotal to their storage density and operational characteristics. Although van der Waals materials can potentially take over the scaling roadmap of silicon-based technologies, the scaling mechanisms and optimization principles at low-dimensional scales remain to be systematically unveiled. In this study, we experimentally demonstrated that the floating-gate length can significantly affect the memory window characteristics of memory devices. Experiments involving various floating-gate and tunneling-layer configurations, combined with TCAD simulations, were conducted to reveal the electrostatic coupling behaviors between floating gate and source/drain electrodes during shaping of the charge storage capabilities. Fundamental performance characteristics of the designed memory devices, including a large memory ratio (82.25%), good retention (>50,000 s, 8 states), and considerable endurance characteristics (>2000 cycles), further validate the role of floating-gate topological structures in manipulating low-dimensional memory devices, offering valuable insights to drive the development of next-generation memory technologies.

Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices

Upconversion nanoparticles (UCNPs), incorporating lanthanide (Ln) dopants, can convert low-energy near-infrared photons into higher-energy visible or ultraviolet light through nonlinear energy transfer processes. This distinctive feature has attracted considerable attention in both fundamental research and advanced optoelectronics. Challenges such as low energy-conversion efficiency and nonradiative losses limit the performance of UCNP-based optoelectronic devices. Recent advancements including optimized core-shell structures, tailed Ln-doping concentration, and surface modifications show significant promise for improving the efficiency and stability. In addition, combining UCNPs with functional materials can broaden their applications and improve device performance, paving the way for the innovation of next-generation optoelectronics. This paper first categorizes and elaborates on various upconversion mechanisms in UCNPs, focusing on strategies to boost energy transfer efficiency and prolong luminescence. Subsequently, an in-depth discussion of the various materials that can enhance the efficiency of UCNPs and expand their functionality is provided. Furthermore, a wide range of UCNP-based optoelectronic devices is explored, and multiple emerging applications in UCNP-based neuromorphic computing are highlighted. Finally, the existing challenges and potential solutions involved in developing practical UCNPs optoelectronic devices are considered, as well as an outlook on the future of UCNPs in advanced technologies is provided.

A Reliable High-Performance Floating-Gate Transistor Based on ZrS2 Native Oxidation for Optoelectronic Synergistic Artificial Synapses

Floating-gate transistors (FGTs), considered the most promising structure among three-terminal van der Waals (vdW) synaptic transistors, possess superiorities in improved data retention, excellent endurance properties, and multibit storage capacity, thereby overcoming the von Neumann bottleneck in conventional computing architectures. However, the dielectric layer in FGT devices typically depends on atomic layer deposition or mechanically transferred insulators, posing several challenges in terms of device compatibility, manufacturing complexity, and performance degradation. Therefore, it is crucial to discover dielectrics compatible with two-dimensional (2D) materials for further simplifying FGT structures and achieving optimal performance. Here, we present a controllable and reliable oxidation process to convert the 2D semiconductor ZrS2 into its native oxide ZrOx and combine ZrOx/ZrS2 with the MoS2 channel to form MoS2/ZrOx/ZrS2 FGT, which exhibits a high on/off ratio of 107, a wide memory window of 101 V, a long retention time of 103 s, a large storage capacity of 7 bits, an excellent PPF index of 269.4%, and low power consumption of 5 pJ. Under photoelectric stimulation, the device stimulates various biological synapse behaviors, including associative memory function and retina-like adaptation. In particular, the device achieves information storage and erasure under solely optical stimulation, exhibiting high consistency with synaptic weight modulation in optogenetics and outstanding optoelectronic storage performance. These results suggest that our work provides a novel and effective approach for simplifying FGT structures and enhancing their performance, holding significant potential for application in next-generation multifunctional memory devices and systems.

Two-Dimensional Materials for Brain-Inspired Computing Hardware

Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions.

Optoelectronic neuron based on transistor combined with volatile threshold switching memristors for neuromorphic computing

The human perception and learning heavily rely on the visual system, where the retina plays a vital role in preprocessing visual information. Developing neuromorphic vision hardware is based on imitating the neurobiological functions of the retina. In this work, an optoelectronic neuron is developed by combining a gate-modulated PDVT-10 channel with a volatile threshold switching memristor, enabling the achievement of optoelectronic performance through a resistance-matching mechanism. The optoelectronic spiking neuron exhibits the ability to alter its spiking behavior in a manner resembling that of a retina. Incorporating electrical and optical modulation, the artificial neuron accurately replicates neuronal signal transmission in a biologically manner. Moreover, it demonstrates inhibition of neuronal firing during darkness and activation upon exposure to light. Finally, the evaluation of a perceptron spiking neural network utilizing these leaky integrate-and-fire neurons is conducted through simulation to assess its capability in classifying image recognition algorithms. This research offers a hopeful direction for the development of easily expandable and hierarchically structured spiking electronics, broadening the range of potential applications in biomimetic vision within the emerging field of neuromorphic hardware.

Emerging 2D Ferroelectric Devices for In-Sensor and In-Memory Computing

The quantity of sensor nodes within current computing systems is rapidly increasing in tandem with the sensing data. The presence of a bottleneck in data transmission between the sensors, computing, and memory units obstructs the system's efficiency and speed. To minimize the latency of data transmission between units, novel in-memory and in-sensor computing architectures are proposed as alternatives to the conventional von Neumann architecture, aiming for data-intensive sensing and computing applications. The integration of 2D materials and 2D ferroelectric materials has been expected to build these novel sensing and computing architectures due to the dangling-bond-free surface, ultra-fast polarization flipping, and ultra-low power consumption of the 2D ferroelectrics. Here, the recent progress of 2D ferroelectric devices for in-sensing and in-memory neuromorphic computing is reviewed. Experimental and theoretical progresses on 2D ferroelectric devices, including passive ferroelectrics-integrated 2D devices and active ferroelectrics-integrated 2D devices, are reviewed followed by the integration of perception, memory, and computing application. Notably, 2D ferroelectric devices have been used to simulate synaptic weights, neuronal model functions, and neural networks for image processing. As an emerging device configuration, 2D ferroelectric devices have the potential to expand into the sensor-memory and computing integration application field, leading to new possibilities for modern electronics.

Full van der Waals Ambipolar Ferroelectric Configurable Optical Hetero-Synapses for In-Sensor Computing

The rapid development of visual neuromorphic hardware can be attributed to their ability to capture, store and process optical signals from the environment. The main limitation of existing visual neuromorphic hardware is that the realization of complex functions is premised on the increase of manufacturing cost, hardware volume and energy consumption. In this study, we demonstrated an optical synaptic device based on a three-terminal van der Waals (vdW) heterojunction that can realize the sensing functions of light wavelength and intensity as well as short-term and long-term synaptic plasticity. In the image recognition task, we constructed an optical reservoir neural network (ORNN) and a visible light communication system (VLC) composed of this optical synaptic device. The ORNN has a recognition rate of up to 84.9% for 50 000 color images in 10 categories in the CIFAR-10 color image dataset, and the VLC system can achieve high-speed transmission with an ultra-low power consumption of only 0.4 nW. This work shows that through reasonable design, vdW heterojunction structures have great application potential in low-power multifunctional fusion application tasks such as visual bionics.

Ultralow Thermal Conductivity and Very High Seebeck Coefficient in Two-Dimensional TeSe2 Semiconductor

Emerging chalcogenide-based two-dimensional (2D) materials possess various unique yet fully explored properties and are thus considered promising candidates for next-generation optoelectronic and energy conversion applications. Here, TeSe2 crystals with interesting thermoelectric features were synthesized using a simple solid-state reaction. High-resolution transmission electron microscopy reveals that TeSe2 stabilizes in a 2D atomic structure with helical chains, resembling 2D tellurene. The thermoelectric properties were measured from 2 to 390 K in a polycrystalline pellet, showing an ultralow thermal conductivity below 0.25 W m-1 K-1 and a very high positive Seebeck coefficient of up to 865 μV K-1. Particularly, the thermal conductivity shows a hysteresis effect upon temperature cycling, which is tentatively explained as cracks opening and partially closing. Optical measurements indicate that TeSe2 is a semiconductor with two bandgaps at 1.43 and 1.65 eV. These results highlight that TeSe2 is an intriguing 2D semiconductor with complex thermoelectric properties, which provides a platform to further study the interplay of emerging 2D structure, thermal, and electronic properties.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm). The proposed device, featuring core-shell upconversion nanoparticle/poly (3-hexylthiophene) (P3HT) nanocomposite channels, enables the absorption and conversion of NIR into high-energy photons to excite more photo carriers in P3HT. The photon-electron-coupled dynamics under the synergy of photovoltaic and photogating effects influence the nonlinearity and high dimensionality of the RC system under narrow-band NIR irradiation. The device also exhibits multilevel data storage capability (≥8 levels), excellent stability (≥2000 s), and durability (≥100 cycles). The system accurately identifies NIR static and dynamic handwritten digit images, achieving recognition accuracies of 91.13% and 90.07%, respectively. Thus, the device tackles intricate computations like solving second-order nonlinear dynamic equations with minimal errors (normalized mean squared error of 1.06 × 10⁻3 during prediction).

Electronics and Optoelectronics Based on Tellurium

As a true 1D system, group-VIA tellurium (Te) is composed of van der Waals bonded molecular chains within a triangular crystal lattice. This unique crystal structure endows Te with many intriguing properties, including electronic, optoelectronic, thermoelectric, piezoelectric, chirality, and topological properties. In addition, the bandgap of Te exhibits thickness dependence, ranging from 0.31 eV in bulk to 1.04 eV in the monolayer limit. These diverse properties make Te suitable for a wide range of applications, addressing both established and emerging challenges. This review begins with an elaboration of the crystal structures and fundamental properties of Te, followed by a detailed discussion of its various synthesis methods, which primarily include solution phase, and chemical and physical vapor deposition technologies. These methods form the foundation for designing Te-centered devices. Then the device applications enabled by Te nanostructures are introduced, with an emphasis on electronics, optoelectronics, sensors, and large-scale circuits. Additionally, performance optimization strategies are discussed for Te-based field-effect transistors. Finally, insights into future research directions and the challenges that lie ahead in this field are shared.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

Spatially Resolved Light-Induced Ferroelectric Polarization in α-In2Se3/Te Heterojunctions

Light-induced ferroelectric polarization in 2D layered ferroelectric materials holds promise in photodetectors with multilevel current and reconfigurable capabilities. However, translating this potential into practical applications for high-density optoelectronic information storage remains challenging. In this work, an α-In2Se3/Te heterojunction design that demonstrates spatially resolved, multilevel, nonvolatile photoresponsivity is presented. Using photocurrent mapping, the spatially localized light-induced poling state (LIPS) is visualized in the junction region. This localized ferroelectric polarization induced by illumination enables the heterojunction to exhibit enhanced photoresponsivity. Unlike previous reports that observe multilevel polarization enhancement in electrical resistance, the device shows nonvolatile photoresponsivity enhancement under illumination. After polarization saturation, the photocurrent increases up to 1000 times, from 10-12 to 10-9 A under the irradiation of a 520 nm laser with a power of 1.69 nW, compared to the initial state in a self-driven mode. The photodetector exhibits high detectivity of 4.6×1010 Jones, with a rise time of 27 µs and a fall time of 28 µs. Furthermore, the device's localized poling characteristics and multilevel photoresponse enable spatially multiplexed optical information storage. These results advance the understanding of LIPS in 2D ferroelectric materials, paving the way for optoelectronic information storage technologies.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing

In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.

Bio-Inspired Photosensory Artificial Synapse Based on Functionalized Tellurium Multiropes for Neuromorphic Computing

Nanomaterials like graphene and transition metal dichalcogenides are being explored for developing artificial photosensory synapses with low-power optical plasticity and high retention time for practical nervous system implementation. However, few studies are conducted on Tellurium (Te)-based nanomaterials due to their direct and small bandgaps. This paper reports the superior photo-synaptic properties of covalently bonded Tellurium sulfur oxide (TeSOx) and Tellurium selenium oxide (TeSeOx)nanomaterials, which are fabricated by incorporating S and Se atoms on the surface of Te multiropes using vapor deposition. Unlike pure Te multiropes, the TeSOx and TeSeOx multiropes exhibit controllable temporal dynamics under optical stimulation. For example, the TeSOx multirope-based transistor displays a photosensory synaptic response to UV light (λ = 365 nm). Furthermore, the TeSeOx multirope-based transistor exhibits photosensory synaptic responses to UV-vis light (λ = 365, 565, and 660 nm), reliable electrical performance, and a combination of both photodetector and optical artificial synaptic properties with a maximum responsivity of 1500 AW-1 to 365 nm UV light. This result is among the highest reported for Te-heterostructure-based devices, enabling optical artificial synaptic applications with low voltage spikes (1 V) and low light intensity (21 µW cm-2), potentially useful for optical neuromorphic computing.

VO2/MoO3 Heterojunctions Artificial Optoelectronic Synapse Devices for Near-Infrared Optical Communication

Artificial optoelectronic synapses (OES) have attracted extensive attention in brain-inspired information processing and neuromorphic computing. However, OES at near-infrared wavelengths have rarely been reported, seriously limiting the application in modern optical communication. Herein, high-performance near-infrared OES devices based on VO2/MoO3 heterojunctions are presented. The textured MoO3 films are deposited on the sputtered VO2 film by using the glancing-angle deposition technique to form a heterojunction device. Through tuning the oxygen defects in the VO2 film, the fabricated VO2/MoO3 heterojunction exhibits versatile electrical synaptic functions. Benefiting from the highly efficient light harvesting and the unique interface effect, the photonic synaptic characteristics, mainly including the short/long-term plasticity and learning experience behavior are successfully realized at the O (1342 nm) and C (1550 nm) optical communication wavebands. Moreover, a single OES device can output messages accurately by converting light signals of the Morse code to distinct synaptic currents. More importantly, a 3 × 3 artificial OES array is constructed to demonstrate the impressive image perceiving and learning capabilities. This work not only indicates the feasibility of defect and interface engineering in modulating the synaptic plasticity of OES devices, but also provides effective strategies to develop advanced artificial neuromorphic visual systems for next-generation optical communication systems.

Centimeter-Scale Tellurium Oxide Films for Artificial Optoelectronic Synapses with Broadband Responsiveness and Mechanical Flexibility

Prevailing over the bottleneck of von Neumann computing has been significant attention due to the inevitableness of proceeding through enormous data volumes in current digital technologies. Inspired by the human brain's operational principle, the artificial synapse of neuromorphic computing has been explored as an emerging solution. Especially, the optoelectronic synapse is of growing interest as vision is an essential source of information in which dealing with optical stimuli is vital. Herein, flexible optoelectronic synaptic devices composed of centimeter-scale tellurium dioxide (TeO2) films detecting and exhibiting synaptic characteristics to broadband wavelengths are presented. The TeO2-based flexible devices demonstrate a comprehensive set of emulating basic optoelectronic synaptic characteristics; i.e., excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), conversion of short-term to long-term memory, and learning/forgetting. Furthermore, they feature linear and symmetric conductance synaptic weight updates at various wavelengths, which are applicable to broadband neuromorphic computations. Based on this large set of synaptic attributes, a variety of applications such as logistic functions or deep learning and image recognition as well as learning simulations are demonstrated. This work proposes a significant milestone of wafer-scale metal oxide semiconductor-based artificial synapses solely utilizing their optoelectronic features and mechanical flexibility, which is attractive toward scaled-up neuromorphic architectures.

Two-Terminal Perovskite Optoelectronic Synapse for Rapid Trained Neuromorphic Computation with High Accuracy

Emerging neural morphological vision sensors inspired by biological systems that integrate image perception, memory, and information computing are expected to transform the landscape of machine vision and artificial intelligence. However, stable and reconfigurable light-induced synaptic behavior always relies on independent gateport modulation. Despite its potential, the limitations of uncontrollable defects and ionic characteristics have led to simpler, smaller, and more integration-friendly two-terminal devices being used as sidelines. In this work, the synergy between ion migration barriers and readout voltage is proven to be the key to realizing stable, reconfigurable, and precisely controllable postsynaptic current in two-terminal devices. Following the same mechanism, optical and electrical signal synchronous triggering is proposed to serve as a preprocessing method to achieve a recognition accuracy of 96.5%. Impressively, the gradual ion accumulation during the training process induces photocurrent evolution, serving as a reference for the dynamic learning rate and boosting accuracy to 97.8% in just 10 epochs. The PSC modulation potential under short optical pulse of 20 ns is also revealed. This optoelectronic device with perception, memory, and computation capabilities can promote the development of new devices for future photonic neural morphological circuits and artificial vision.

Gap State-Modulated Van Der Waals Short-Term Memory with Broad Band Negative Photoconductance

Floating gate memory (FGM), composed of van der Waals (vdW) junctions with an atomically thin floating layer for charge storage, is widely employed to develop logic-in memories and in-sensor computing devices. Most research efforts of FGM are spent on achieving long-term charge storage and fast reading/writing for flash and random-access memory. However, dynamic modulation of memory time via a tunneling barrier and vdW interfaces, which is critical for synaptic computing and machine vision, is still lacking. Here, a van der Waals short-term memory with tunable memory windows and retention times from milliseconds to thousands of seconds is reported, which is approximately exponentially proportional to the thickness h-BN (hexagonal boron nitride) barrier. The specific h-BN barrier with fruitful gap states provides charge release channels for trapped charges, making the vdW device switchable between positive photoconductance and negative photoconductance with a broadband light from IR to UV range. The dynamic short-term memory with tunable photo response highlights the design strategy of novel vdW memory vis interface engineering for further intelligent information storage and optoelectronic detection.

Ferroelectric 2D SnS2 Analog Synaptic FET

In this study, the development and characterization of 2D ferroelectric field-effect transistor (2D FeFET) devices are presented, utilizing nanoscale ferroelectric HfZrO2 (HZO) and 2D semiconductors. The fabricated device demonstrated multi-level data storage capabilities. It successfully emulated essential biological characteristics, including excitatory/inhibitory postsynaptic currents (EPSC/IPSC), Pair-Pulse Facilitation (PPF), and Spike-Timing Dependent Plasticity (STDP). Extensive endurance tests ensured robust stability (107 switching cycles, 105 s (extrapolated to 10 years)), excellent linearity, and high Gmax/Gmin ratio (>105), all of which are essential for realizing multi-level data states (>7-bit operation). Beyond mimicking synaptic functionalities, the device achieved a pattern recognition accuracy of ≈94% on the Modified National Institute of Standards and Technology (MNIST) handwritten dataset when incorporated into a neural network, demonstrating its potential as an effective component in neuromorphic systems. The successful implementation of the 2D FeFET device paves the way for the development of high-efficiency, ultralow-power neuromorphic hardware which is in sub-femtojoule (48 aJ/spike) and fast response (1 µs), which is 104 folds faster than human synapse (≈10 ms). The results of the research underline the potential of nanoscale ferroelectric and 2D materials in building the next generation of artificial intelligence technologies.

Anti-Ambipolar Heterojunctions: Materials, Devices, and Circuits

Anti-ambipolar heterojunctions are vital in constructing high-frequency oscillators, fast switches, and multivalued logic (MVL) devices, which hold promising potential for next-generation integrated circuit chips and telecommunication technologies. Thanks to the strategic material design and device integration, anti-ambipolar heterojunctions have demonstrated unparalleled device and circuit performance that surpasses other semiconducting material systems. This review aims to provide a comprehensive summary of the achievements in the field of anti-ambipolar heterojunctions. First, the fundamental operating mechanisms of anti-ambipolar devices are discussed. After that, potential materials used in anti-ambipolar devices are discussed with particular attention to 2D-based, 1D-based, and organic-based heterojunctions. Next, the primary device applications employing anti-ambipolar heterojunctions, including anti-ambipolar transistors (AATs), photodetectors, frequency doublers, and synaptic devices, are summarized. Furthermore, alongside the advancements in individual devices, the practical integration of these devices at the circuit level, including topics such as MVL circuits, complex logic gates, and spiking neuron circuits, is also discussed. Lastly, the present key challenges and future research directions concerning anti-ambipolar heterojunctions and their applications are also emphasized.

Technology and Integration Roadmap for Optoelectronic Memristor

Optoelectronic memristors (OMs) have emerged as a promising optoelectronic Neuromorphic computing paradigm, opening up new opportunities for neurosynaptic devices and optoelectronic systems. These OMs possess a range of desirable features including minimal crosstalk, high bandwidth, low power consumption, zero latency, and the ability to replicate crucial neurological functions such as vision and optical memory. By incorporating large-scale parallel synaptic structures, OMs are anticipated to greatly enhance high-performance and low-power in-memory computing, effectively overcoming the limitations of the von Neumann bottleneck. However, progress in this field necessitates a comprehensive understanding of suitable structures and techniques for integrating low-dimensional materials into optoelectronic integrated circuit platforms. This review aims to offer a comprehensive overview of the fundamental performance, mechanisms, design of structures, applications, and integration roadmap of optoelectronic synaptic memristors. By establishing connections between materials, multilayer optoelectronic memristor units, and monolithic optoelectronic integrated circuits, this review seeks to provide insights into emerging technologies and future prospects that are expected to drive innovation and widespread adoption in the near future.

A 2D Heterostructure-Based Multifunctional Floating Gate Memory Device for Multimodal Reservoir Computing

The demand for economical and efficient data processing has led to a surge of interest in neuromorphic computing based on emerging two-dimensional (2D) materials in recent years. As a rising van der Waals (vdW) p-type Weyl semiconductor with many intriguing properties, tellurium (Te) has been widely used in advanced electronics/optoelectronics. However, its application in floating gate (FG) memory devices for information processing has never been explored. Herein, an electronic/optoelectronic FG memory device enabled by Te-based 2D vdW heterostructure for multimodal reservoir computing (RC) is reported. When subjected to intense electrical/optical stimuli, the device exhibits impressive nonvolatile electronic memory behaviors including ≈108 extinction ratio, ≈100 ns switching speed, >4000 cycles, >4000-s retention stability, and nonvolatile multibit optoelectronic programmable characteristics. When the input stimuli weaken, the nonvolatile memory degrades into volatile memory. Leveraging these rich nonlinear dynamics, a multimodal RC system with high recognition accuracy of 90.77% for event-type multimodal handwritten digit-recognition is demonstrated.

Ferroelectricity-Driven Self-Powered Weak Temperature and Broadband Light Detection in MoS2/CuInP2S6/WSe2 van der Waals Heterojunction Nanoarchitectonics

Two-dimensional ferroelectric materials enrich the modulation degrees of freedom in self-powered van der Waals temperature/light detectors by incorporating pyroelectric and bulk photovoltaic effects. However, in addition to the low polarization, the practical applications of these materials are limited due to the significant challenge posed by their ultrathin nature, which affects their polarization stability. In this report, we introduce a design for a dual heterostructure-stabilized van der Waals heterojunction that addresses this challenge by improving the performance and extending the operational lifetime of self-powered van der Waals temperature/light detectors. The design is demonstrated using the MoS2/CuInP2S6 (CIPS)/WSe2 van der Waals heterojunction, which exhibits sensitivity to small temperature changes induced by weak light across the ultraviolet to mid-infrared spectrum. It can generate a noticeable pyroelectric current without the need for an external voltage, and its pyroelectric coefficient exceeds 130 and 978 μC/m2 K for 45 and 70 nm CIPS, respectively. The heterojunction offers high detection accuracy, with a temperature variation sensitivity as small as 0.1 K and an optical power intensity detection range from low to 1 μW/cm2. Additionally, the heterojunction exhibits exceptional detectivity (D*) for different light wavelengths. Remarkably, the self-powered detection performance remains stable for months without obvious degradation in the natural environment. These results offer a promising solution for high-performance, self-sustaining temperature/light detection applications and pave the way for the development of future ferroelectricity-driven photodetection technologies.

Vitality of Intralayer Vibration in hBN for Effective Long-Range Interlayer Hole Transfer across High Barriers in MoSe2/hBN/WSe2 Heterostructures

Introducing the two-dimensional (2D) hexagonal boron nitride (hBN) between 2D transition metal dichalcogenide (TMD) layers promises convenient manipulation of the interlayer exciton (IX) and interlayer charge transfer in TMD/hBN/TMD heterostructures, while the role of inserted hBN layers during IX formation is controversial. Employing ab initio nonadiabatic molecular dynamics (NAMD) simulations and the electron-phonon coupling model, we systematically investigate interlayer hole transfer in MoSe2/WSe2 bilayers intercalated by hBN layers with various thicknesses. The conventional direct hole transfer from MoSe2 to WSe2 is decelerated by 2-3 orders of magnitude after the hBN insertion. Meanwhile, a novel channel intermediated by a deeper hole of WSe2 becomes dominant, where the intralayer shear mode of hBN plays a crucial role by reducing the energy barriers for this new channel. The unique role of hBN layers is revealed for the first time, enriching the knowledge of the underlying microscopic mechanisms and providing instructive guidance to practical van der Waals optoelectronic devices.

Microfluidic-Assembled Covalent Organic Frameworks@Ti3 C2 Tx MXene Vertical Fibers for High-Performance Electrochemical Supercapacitors

The delicate design of innovative and sophisticated fibers with vertical porous skeleton and eminent electrochemical activity to generate directional ionic pathways and good faradic charge accessibility is pivotal but challenging for realizing high-performance fiber-shaped supercapacitors (FSCs). Here, hierarchically ordered hybrid fiber combined vertical-aligned and conductive Ti3 C2 Tx MXene (VA-Ti3 C2 Tx ) with interstratified electroactive covalent organic frameworks LZU1 (COF-LZU1) by one-step microfluidic synthesis is developed. Due to the incorporation of vertical channels, abundant redox active sites and large accessible surface area throughout the electrode, the VA-Ti3 C2 Tx @COF-LZU1 fibers express exceptional gravimetric capacitance of 787 F g-1 in a three-electrode system. Additionally, the solid-state asymmetric FSCs deliver a prominent energy density of 27 Wh kg-1 , capacitance of 398 F g-1 and cycling life of 20 000 cycles. The key to high energy storage ability originates from the decreased ions adsorption energy and ameliorative charge density distribution in vertically aligned and active hybrid fiber, accelerating ions transportation/accommodation and interfacial electrons transfer. Benefiting from excellent electrochemical performance, the FSCs offer sufficient energy supply to power watches, flags, and digital display tubes as well as be integrated with sensors to detect pulse signals, which opens a promising route for architecting advanced fiber toward the carbon neutrality market beyond energy-storage technology.

Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform

Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate a system comprising of multilayer hexagonal boron nitride (hBN) films contacted with silver (Ag), which can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between the high resistance state (HRS) and the low resistance state (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, two different neural networks are realized in a single CMOS compatible, 2D material platform.

Uncovering the Role of Crystal Phase in Determining Nonvolatile Flash Memory Device Performance Fabricated from MoTe2-Based 2D van der Waals Heterostructures

Although the crystal phase of two-dimensional (2D) transition metal dichalcogenides (TMDs) has been proven to play an essential role in fabricating high-performance electronic devices in the past decade, its effect on the performance of 2D material-based flash memory devices still remains unclear. Here, we report the exploration of the effect of MoTe₂ in different phases as the charge-trapping layer on the performance of 2D van der Waals (vdW) heterostructure-based flash memory devices, where a metallic 1T'-MoTe₂ or semiconducting 2H-MoTe₂ nanoflake is used as the floating gate. By conducting comprehensive measurements on the two kinds of vdW heterostructure-based devices, the memory device based on MoS₂/h-BN/1T'-MoTe₂ presents much better performance, including a larger memory window, faster switching speed (100 ns), and higher extinction ratio (10⁷), than that of the device based on the MoS₂/h-BN/2H-MoTe₂ heterostructure. Moreover, the device based on the MoS₂/h-BN/1T'-MoTe₂ heterostructure also shows a long cycle (>1200 cycles) and retention (>3000 s) stability. Our study clearly demonstrates that the crystal phase of 2D TMDs has a significant impact on the performance of nonvolatile flash memory devices based on 2D vdW heterostructures, which paves the way for the fabrication of future high-performance memory devices based on 2D materials.