Fast, Multi-Bit, and Vis-Infrared Broadband Nonvolatile Optoelectronic Memory with MoS2 /2D-Perovskite Van der Waals Heterojunction

Efficient Gate-Tunable Hot-Carrier Photocurrent from Perovskite Multiple Quantum Wells

Hot-carrier relaxation above the bandgap results in significant energy losses, making the extraction of hot carriers a critical challenge for efficient hot-carrier photocurrent generation in devices. In this study, we observe long-lived hot carriers in the metal-halide perovskite multiple quantum wells, (BA)2(MA)n-1PbnI3n+1 (n = 3), and demonstrate effective hot-hole photocurrent generation using 2D MoS₂ as an extraction layer. A high external quantum efficiency of short-circuit hot-carrier photocurrent of up to 35.4% is achieved. Further enhancement in photocurrent efficiency and open-circuit photovoltage is achieved when a gate electric field is applied, resulting in an external quantum efficiency of up to 61.9%. Evidence of hot-hole extraction is validated through operando transient reflection measurements on the working devices, with studies that depend on wavelength, carrier density, and gate voltage. DFT calculations on the heterostructure devices under different bias voltages further elucidate the mechanism of hot-hole extraction enhancement. These findings underscore the potential of perovskite multiple quantum wells as long-lived hot-carrier generators and highlight the role of 2D transition metal dichalcogenide semiconductors as efficient hot-carrier extraction electrodes for low-power optoelectronics.

Ferroelectric Perovskite/MoS2 Channel Heterojunctions for Wide-Window Nonvolatile Memory and Neuromorphic Computing

Ferroelectric materials commonly serve as gate insulators in typical field-effect transistors, where their polarization reversal enables effective modulation of the conductivity state of the channel material, thereby realizing non-volatile memory. Currently, novel 2D ferroelectrics unlock new prospects in next-generation electronics and neuromorphic computation. However, the advancement of these materials is impeded by limited selectivity and narrow memory windows. Here, new concepts of 2D ferroelectric perovskite/MoS2 channel heterostructures field-effect transistors are presented, in which 2D ferroelectric perovskite features customizable band structure, few-layered ferroelectricity, and submillimeter-size monolayer wafers. Further studies reveal that these devices exhibit unique charge polarity modulation (from n- to p-type channel) and remarkable nonvolatile memory behavior, especially record-wide hysteresis windows up to 177 V, which enables efficient imitation of biological synapses and achieves high recognition accuracy for electrocardiogram patterns. This result provides a device paradigm for future nonvolatile memory and artificial synaptic applications.

Wrinkled Rhenium Disulfide for Anisotropic Nonvolatile Memory and Multiple Artificial Neuromorphic Synapses

Two-dimensional materials are emerging as potential solutions for high-density nonvolatile memory and efficient neuromorphic computing. However, integrating multidimensional memory and an ideal linear weight updating synapse in a simple device configuration to achieve versatile biomimetic neuromorphic systems remains challenging. Here, we introduce a wrinkled rhenium disulfide (ReS2) transistor, where the wrinkled structure facilitates the carrier trapping/detrapping at the dielectric interface, thus enabling the fusion of nonvolatile memory and both electronic and optoelectronic synaptic functionalities. As a nonvolatile memory, anisotropic wrinkled ReS2 can yield three distinct sets of data across three crystal orientations under identical programming operations. Each set demonstrates exceptional retention and endurance properties. As a neuromorphic synapse, it realizes the linear and symmetric updates of conductance states up to 9 bits and 8 bits, the ultra-low-energy consumption of 75 fJ and 2.5 pJ under the electrical and optical stimuli, respectively. The artificial neural network (ANN) based on electronic synapses gives a superior recognition accuracy of 92.9% for the original handwritten digits. The anisotropic synaptic responses and multiwavelength sensitivities of optoelectronic synapses enable them to execute advanced memory and recognition functions for complex images that encompass a variety of pattern features or color information. This underscores its substantial potential for integration into efficient biomimetic visual systems.

Electro-Optically Configurable Synaptic Transistors With Cluster-Induced Photoactive Dielectric Layer for Visual Simulation and Biomotor Stimuli

The integration of visual simulation and biorehabilitation devices promises great applications for sustainable electronics, on-demand integration and neuroscience. However, achieving a multifunctional synergistic biomimetic system with tunable optoelectronic properties at the individual device level remains a challenge. Here, an electro-optically configurable transistor employing conjugated-polymer as semiconductor layer and an insulating polymer (poly(1,8-octanediol-co-citrate) (POC)) with clusterization-triggered photoactive properties as dielectric layer is shown. These devices realize adeptly transition from electrical to optical synapses, featuring multiwavelength and multilevel optical synaptic memory properties exceeding 3 bits. Utilizing enhanced optical memory, the images learning and memory function for visual simulation are achieved. Benefiting from rapid electrical response akin to biological muscle activation, increased actuation occurs under increased stimulus frequency of gate voltage. Additionally, the transistor on POC substrate can be effectively degraded in NaOH solution due to degradation of POC. Pioneeringly, the electro-optically configurability stems from light absorption and photoluminescence of the aggregation cluster in POC layer after 200 °C annealing. The enhancement of optical synaptic plasticity and integration of motion-activation functions within a single device opens new avenues at the intersection of optoelectronics, synaptic computing, and bioengineering.

High-Throughput Computational Study and Machine Learning Prediction of Electronic Properties in Transition Metal Dichalcogenide/Two-Dimensional Layered Halide Perovskite Heterostructures

Heterostructures formed by transition metal dichalcogenides (TMDs) and two-dimensional layered halide perovskites (2D-LHPs) have attracted significant attention due to their unique optoelectronic properties. However, theoretical studies face challenges due to the large number of atoms and the need for lattice matching. With the discovery of more 2D-LHPs, there is an urgent need for methods to rapidly predict and screen TMDs/2D-LHPs heterostructures. This study employs first-principles calculations to perform high-throughput computations on 602 TMDs/2D-LHPs heterostructures. Results show that different combinations exhibit diverse band alignments, with MoS2 and WS2 more likely to form type-II heterostructures with 2D-LHPs. The highest photoelectric conversion efficiency of type-II structures reaches 23.26%, demonstrating potential applications in solar cells. Notably, some MoS2/2D-LHPs form type-S structures, showing promise in photocatalysis. Furthermore, we found that TMDs can significantly affect the conformation of organic molecules in 2D-LHPs, thus modulating the electronic properties of the heterostructures. To overcome computational cost limitations, we constructed a crystal graph convolutional neural network model based on the calculated data to predict the electronic properties of TMDs/2D-LHPs heterostructures. Using this model, we predicted the bandgaps and band alignment types of 9,360 TMDs/2D-LHPs heterostructures, providing a comprehensive theoretical reference for research in this field.

Artificial optoelectronic synapse based on CdSe nanobelt photosensitized MoS2 transistor with long retention time for neuromorphic application

Optoelectronic synaptic devices have been regarded as the key component in constructing neuromorphic computing systems. However, the optoelectronic synapses based on conventional 2D transistor are still suffering low photosensitivity and volatile retention behavior, which can affect the recognition accuracy and long-term memory. Here, a novel optoelectronic synaptic device based on surface-state-rich CdSe nanobelt photosensitized 2D MoS2 transistor is demonstrated. Benefiting from the excellent light absorption of CdSe and effective charge trapping at the hetero-interface, the device exhibits not only high photosensitivity but also long retention time (>1,500 s). In addition, typical synaptic functions including the excitatory postsynaptic current, paired-pulse facilitation, the transformation from short-term to long-term plasticity, the transformation from short-term to long-term plasticity, spike-amplitude-dependent plasticity, and learning-forgetting-relearning process are successfully simulated and modulated by light stimulation. Most importantly, an artificial neural network is simulated based on the optical potentiation and electrical habituation characteristics of the synaptic devices, with recognition accuracy rates of 89.2, 93.8, and 91.9 % for file type datasets, small digits, and large digits are achieved. This study demonstrates a simple and efficient way to fabricate highly photosensitive optoelectronic synapse for artificial neural networks by combining the merits of specific materials and device architecture.

Human somatosensory systems based on sensor-memory-integrated technology

As a representative artificial neural network (ANN) for incorporating sensing functions and memory functions into one system to achieve highly miniaturized and highly integrated devices or systems, artificial sensory systems (ASSs) can have a far-reaching influence on precise instrumentation, sensing, and automation engineering. Artificial sensory systems have enjoyed considerable progress in recent years, from low degree integrations to highly advanced sophisticated integrations, from single-modal perceptions to multimode-fused perceptions. However, there are issues around the large hardware area, power consumption, and communication bandwidth needed during the processes where multimodal sensing signals are converted into a digital mode before they can be processed by a digital processor. Therefore, deepening the research into sensory integration is of great importance. In this review, we briefly introduce fundamental knowledge about the memristor mechanism, describe some representative human somatosensory systems, and elucidate the relationship between the properties of memristor devices and the structure. The electronic character of the sensors, future prospects, and key challenges surrounding sensor-memory integrated technologies are also discussed.

A floating-gate field-effect transistor memory device based on organic crystals with a built-in tunneling dielectric by a one-step growth strategy

A floating-gate organic field-effect transistor (FG-OFET) memory device is becoming a promising candidate for emerging non-volatile memory applications due to the advantages of its sophisticated data-storage mechanism and reliable long-term data retention capacity. However, a conventional FG-OFET memory device suffers from complex fabrication technologies and poor mechanical flexibility, which limits its practical applications. Here, we propose a facile one-step liquid-surface drag coating strategy to fabricate a layered stack of 2,8-difluoro-5,11-bis(triethylsilylethynyl) anthradithiophene (Dif-TES-ADT) crystals and high-quality insulating polymer polystyrene (PS). The liquid surface enhances the spreading area of an organic solution and facilitates the unidirectional growth of organic crystals. In the bilayer-structured blend, the bottom PS polymer and the top Dif-TES-ADT semiconductor serve as a tunneling dielectric and an active memory layer of an FG-OFET memory device, respectively. Consequently, a flexible FG-OFET memory device with a large memory window of 41.4 V, a long retention time of 5000 s, and a high current ON/OFF ratio of 105 could be achieved, showing the best performance ever reported for organic thin film-based FG-OFET memory devices. In addition, multi-level data storage (3 bits per cell) can be achieved by tuning the gate voltage magnitude. Our work not only provides a general strategy for the growth of high-quality organic crystals, but also paves the way towards high-performance flexible memory devices.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

Integrated In-Memory Sensor and Computing of Artificial Vision Based on Full-vdW Optoelectronic Ferroelectric Field-Effect Transistor

The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104  s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.

Next-Generation Photodetectors beyond Van Der Waals Junctions

With the continuous advancement of nanofabrication techniques, development of novel materials, and discovery of useful manipulation mechanisms in high-performance applications, especially photodetectors, the morphology of junction devices and the way junction devices are used are fundamentally revolutionized. Simultaneously, new types of photodetectors that do not rely on any junction, providing a high signal-to-noise ratio and multidimensional modulation, have also emerged. This review outlines a unique category of material systems supporting novel junction devices for high-performance detection, namely, the van der Waals materials, and systematically discusses new trends in the development of various types of devices beyond junctions. This field is far from mature and there are numerous methods to measure and evaluate photodetectors. Therefore, it is also aimed to provide a solution from the perspective of applications in this review. Finally, based on the insight into the unique properties of the material systems and the underlying microscopic mechanisms, emerging trends in junction devices are discussed, a new morphology of photodetectors is proposed, and some potential innovative directions in the subject area are suggested.

Harnessing 2D Ruddlesden-Popper Perovskite with Polar Organic Cation for Ultrasensitive Multibit Nonvolatile Transistor-Type Photomemristors

Photomemristors have been regarded as one of the most promising candidates for next-generation hardware-based neuromorphic computing due to their potentials of fast data transmission and low power consumption. However, intriguingly, so far, photomemristors seldom display truly nonvolatile memory characteristics with high light sensitivity. Herein, we demonstrate ultrasensitive photomemristors utilizing two-dimensional (2D) Ruddlesden-Popper (RP) perovskites with a highly polar donor-acceptor-type push-pull organic cation, 4-(5-(2-aminoethyl)thiophen-2-yl)benzonitrile+ (EATPCN+), as charge-trapping layers. High linearity and almost zero-decay retention are observed in (EATPCN)2PbI4 devices, which are very distinct from that of the traditional 2D RP perovskite devices consisting of nonpolar organic cations, such as phenethylamine+ (PEA+) and octylamine+ (OA+), and traditional 3D perovskite devices consisting of methylamine+ (MA+). The 2-fold advantages, including desirable spatial crystal arrangement and engineered energetic band alignment, clarify the mechanism of superior performance in (EATPCN)2PbI4 devices. The optimized (EATPCN)2PbI4 photomemristor also shows a memory window of 87.9 V and an on/off ratio of 106 with a retention time of at least 2.4 × 105 s and remains unchanged after >105 writing-reading-erasing-reading endurance cycles. Very low energy consumptions of 1.12 and 6 fJ for both light stimulation and the reading process of each status update are also demonstrated. The extremely low power consumption and high photoresponsivity were simultaneously achieved. The high photosensitivity surpasses that of a state-of-the-art commercial pulse energy meter by several orders of magnitude. With their outstanding linearity and retention, rabbit images have been rebuilt by (EATPCN)2PbI4 photomemristors, which truthfully render the image without fading over time. Finally, by utilizing the powerful ∼8 bits of nonvolatile potentiation and depression levels of (EATPCN)2PbI4 photomemristors, the accuracies of the recognition tasks of CIFAR-10 image classification and MNIST handwritten digit classification have reached 89% and 94.8%, respectively. This study represents the first report of utilizing a functional donor-acceptor type of organic cation in 2D RP perovskites for high-performance photomemristors with characteristics that are not found in current halide perovskites.

A multifunctional nano-hydroxyapatite/MXene scaffold for the photothermal/dynamic treatment of bone tumours and simultaneous tissue regeneration

After resection of bone tumour, the risk of cancer recurrence and numerous bone defects continues to threaten the health of patients. To overcome the challenge, we developed a novel multifunctional scaffold material consisting mainly of nano-hydroxyapatite particles (n-HA), MXene nanosheets and g-C3N4 to prevent tumour recurrence and promote bone formation. N-HA has the potential to restrict the growth of osteosarcoma cells, and the combination of MXene and g-C3N4 enables the scaffolds to produce photodynamic and photothermal effects simultaneously under near infrared (NIR) irradiation. Surprisingly, n-HA can further enhance the synergistic anti-tumour function of photodynamic and photothermal, and the scaffolds can eradicate osteosarcoma cells in only 10 min at a mild temperature of 45 ℃. Moreover, the scaffold exhibit exceptional cytocompatibility and possesses the capacity to induce osteogenic differentiation of bone marrow mesenchymal stem cells. Therefore, this multifunctional scaffold can not only inhibits the proliferation of bone tumour cells and rapidly eradicate bone tumour through NIR irradiation, but also enhances osteogenic activity. This promising measure can be used to treat tissue damage after bone tumour resection.

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.

Carrier transfer in quasi-2D perovskite/MoS2 monolayer heterostructure

Two-dimensional layered semiconductors have attracted intense interest in recent years. The van der Waals coupling between the layers tolerates stacking various materials and establishing heterostructures with new characteristics for a wide range of optoelectronic applications. The interlayer exciton dynamics at the interface within the heterostructure are vitally important for the performance of the photodetector and photovoltaic device. Here, a heterostructure comprising two-dimensional organic-inorganic Ruddlesden-Popper perovskites and transition metal dichalcogenide monolayer was fabricated and its ultrafast charge separation processes were systematically studied by using femtosecond time-resolved transient absorption spectroscopy. Significant hole and electron transfer processes in the ps and fs magnitude at the interface of the heterostructure were observed by tuning pump wavelengths of the pump-probe geometries. The results emphasize the realization of the exciton devices based on semiconductor heterostructures of two-dimensional perovskite and transition metal dichalcogenide.

Defect and Donor Manipulated Highly Efficient Electron-Hole Separation in a 3D Nanoporous Schottky Heterojunction

Given the rapid recombination of photogenerated charge carriers and photocorrosion, transition metal sulfide photocatalysts usually suffer from modest photocatalytic performance. Herein, S-vacancy-rich ZnIn2S4 (VS-ZIS) nanosheets are integrated on 3D bicontinuous nitrogen-doped nanoporous graphene (N-npG), forming 3D heterostructures with well-fitted geometric configuration (VS-ZIS/N-npG) for highly efficient photocatalytic hydrogen production. The VS-ZIS/N-npG presents ultrafast interfacial photogenerated electrons captured by the S vacancies in VS-ZIS and holes neutralization behaviors by the extra free electrons in N-npG during photocatalysis, which are demonstrated by in situ XPS, femtosecond transient absorption (fs-TA) spectroscopy, and transient-state surface photovoltage (TS-SPV) spectra. The simulated interfacial charge rearrangement behaviors from DFT calculations also verify the separation tendency of photogenerated charge carriers. Thus, the optimized VS-ZIS/N-npG 3D hierarchical heterojunction with 1.0 wt % N-npG exhibits a comparably high hydrogen generation rate of 4222.4 μmol g-1 h-1, which is 5.6-fold higher than the bare VS-ZIS and 12.7-fold higher than the ZIS without S vacancies. This work sheds light on the rational design of photogenerated carrier transfer paths to facilitate charge separation and provides further hints for the design of hierarchical heterostructure photocatalysts.

PZT-Enabled MoS2 Floating Gate Transistors: Overcoming Boltzmann Tyranny and Achieving Ultralow Energy Consumption for High-Accuracy Neuromorphic Computing

Low-power electronic devices play a pivotal role in the burgeoning artificial intelligence era. The study of such devices encompasses low-subthreshold swing (SS) transistors and neuromorphic devices. However, conventional field-effect transistors (FETs) face the inherent limitation of the "Boltzmann tyranny", which restricts SS to 60 mV decade-1 at room temperature. Additionally, FET-based neuromorphic devices lack sufficient conductance states for highly accurate neuromorphic computing due to a narrow memory window. In this study, we propose a pioneering PZT-enabled MoS2 floating gate transistor (PFGT) configuration, demonstrating a low SS of 46 mV decade-1 and a wide memory window of 7.2 V in the dual-sweeping gate voltage range from -7 to 7 V. The wide memory window provides 112 distinct conductance states for PFGT. Moreover, the PFGT-based artificial neural network achieves an outstanding facial-recognition accuracy of 97.3%. This study lays the groundwork for the development of low-SS transistors and highly energy efficient artificial synapses utilizing two-dimensional materials.

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.