A MoS2 /PTCDA Hybrid Heterojunction Synapse with Efficient Photoelectric Dual Modulation and Versatility

Visible-Photoactive Ferroelectric Semiconductor Incorporating Aromatic Dynamic Spacer Enables Optoelectronic Synaptic Plasticity

Photoferroelectrics are capturing the growing interest for their unique light-polarization coupling and optoelectronic applicability. However, the formidable challenge persists in coupling electric order and strong photoactivity through precise molecular design, hindering their further application in the field of optoelectronic memory. Herein, the molecular dynamics of aromatic cations in the 2D constrained environments are customized to construct perovskite photoferroelectrics, (4-tert-butylbenzylammonium)2(ethylammonium)2Pb3I10, showing a narrow bandgap (≈1.96 eV) and strong visible-photosensitivity. Notably, multi-level dynamic states of aromatic cations provide the impetus for inducing ferroelectric order and photo-ferroelectric effects. Such captivating characteristics can achieve light-induced multiple polarization, dielectric, and conductivity states. Accordingly, photoferroelectrics are integrated into heterojunction phototransistors that display robust electrical and optical modulation, including diversified synaptic plasticity with low optical program power (≈20 pJ) for each training process. As a noteworthy advancement in the photoferroelectric field, this work will enrich the understanding of the structure-property relationship and shed light on further exploration toward neuromorphic computing.

2D MoTe2 memristors for energy-efficient artificial synapses and neuromorphic applications

The potential of two-dimensional (2D) transition metal dichalcogenides (TMDs), especially molybdenum telluride (MoTe2), in sophisticated electrical and low-energy neuromorphic applications, has attracted a lot of interest. The creation, characteristics, and uses of MoTe2-based memristive devices are summarized in this review paper, with an emphasis on their potential as artificial synapses for neuromorphic computing. We thoroughly examine the special properties of MoTe2, such as its remarkable resistance switching response, excellent linearity in synaptic potentiation, and customizable phase states. These characteristics make it possible to implement basic computational functions with minimal energy consumption, including decimal arithmetic operations and the commutative principles of addition and multiplication. In addition to simulating intricate synaptic processes such as long-term potentiation (LTP), long-term depression (LTD), and spike-timing-dependent plasticity (STDP), the article emphasizes the experimental performances of MoTe2 memristors, which include their capacity to execute exact decimal arithmetic operations. The demonstration of centimeter-scale 2D MoTe2 film-based memristor arrays attaining over 90% recognition accuracy in handwritten digit identification tests further demonstrates the devices' great scalability, stability, and incorporation capabilities. Notwithstanding these developments, issues such as poor environmental robustness, phase transition sensitivity, and low thermal stability still exist. The creation of hybrid or composite materials, doping, and structural alteration are some of the methods to get beyond these obstacles that are covered in the paper. The need for scalable, economical synthesis techniques and a better comprehension of the material's mechanical, optical, and electrical properties through modeling and experiments are emphasized.

Light-induced tunable threshold voltage and synaptic behavior of a solution-processed indium oxide thin film transistor for logic computing and image denoising

Oxygen vacancies (VO) play a crucial role in promising amorphous metal oxide films for next-generation logic and synaptic computing. Here, a simple and reversible annealing-illumination method is introduced to control the concentration of VO in solution-processed amorphous indium oxide thin-film transistors (TFTs), resulting in the precise regulation of the threshold voltage (VTH) in a large range from 1.6 V to -21.7 V. Meanwhile, clear photo-synaptic behaviors are observed. These impressive behaviors result from the VO-related carrier trapping and detrapping processes. With the precise regulation of VTH by illumination, the TFTs are constructed as inverters, displaying tunable voltage gains from 5.7 to 10.6. Owing to the excellent photo-synaptic behavior, the TFTs are employed to demonstrate the optoelectronic logic functions of "OR", "AND", "NOR", and "NAND". Moreover, a 5 × 5 TFTs array is employed to demonstrate the real-time image preprocessing and image denoising functions, displaying an impressive accuracy of 96%. Furthermore, the improvement of the recognition accuracy will increase to a maximum value of 88%. This work shows the potential of amorphous indium oxide TFTs in future multifunctional logic circuits and efficient, all-optical neuromorphic vision systems.

Ternary Heterojunction Synaptic Transistors Based on Perovskite Quantum Dots

The traditional von Neumann architecture encounters significant limitations in computational efficiency and energy consumption, driving the development of neuromorphic devices. The optoelectronic synaptic device serves as a fundamental hardware foundation for the realization of neuromorphic computing and plays a pivotal role in the development of neuromorphic chips. This study develops a ternary heterojunction synaptic transistor based on perovskite quantum dots to tackle the critical challenge of synaptic weight modulation in organic synaptic devices. Compared to binary heterojunction synaptic transistor, the ternary heterojunction synaptic transistor achieves an enhanced hysteresis window due to the synergistic charge-trapping effects of acceptor material and perovskite quantum dots. The memory window decreases with increasing source-drain voltage (VDS) but expands with prolonged program/erase time, demonstrating effective carrier trapping modulation. Furthermore, the device successfully emulates typical photonic synaptic behaviors, including excitatory postsynaptic currents (EPSCs), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). This work provides a simplified strategy for high-performance optoelectronic synaptic transistors, showcasing significant potential for neuromorphic computing and adaptive intelligent systems.

Electric Field-Driven Conformational Changes in Molecular Memristor and Synaptic Behavior

This paper demonstrates the use of molecular artificial synapses in neuromorphic computing systems designed for low energy consumption. A molecular junction, based on self-assembled monolayers (SAMs) of alkanethiolates terminated with 2,2'-bipyridine complexed with cobalt chloride, exhibits synaptic behaviors with an energy consumption of 8.0 pJ µm-2. Conductance can be modulated simply by applying pulses in the incoherent charge transport (CT) regime. Charge injection in this regime allows molecules to overcome the low energy barrier for C─C bond rotations, resulting in conformational changes in the SAMs. The reversible potentiation/depression process of conductance achieves 90% accuracy in recognizing patterns from the Modified National Institute of Standards and Technology (MNIST) handwritten digit database. The molecular junction further exhibits both rectifying and conductance hysteresis behaviors, showing potential for use in selector-free synaptic arrays that efficiently suppress sneak currents.

Versatile optoelectronic memristor based on wide-bandgap Ga2O3 for artificial synapses and neuromorphic computing

Optoelectronic memristors possess capabilities of data storage and mimicking human visual perception. They hold great promise in neuromorphic visual systems (NVs). This study introduces the amorphous wide-bandgap Ga2O3 photoelectric synaptic memristor, which achieves 3-bit data storage through the adjustment of current compliance (Icc) and the utilization of variable ultraviolet (UV-254 nm) light intensities. The "AND" and "OR" logic gates in memristor-aided logic (MAGIC) are implemented by utilizing voltage polarity and UV light as input signals. The device also exhibits highly stable synaptic characteristics such as paired-pulse facilitation (PPF), spike-intensity dependent plasticity (SIDP), spike-number dependent plasticity (SNDP), spike-time dependent plasticity (STDP), spike-frequency dependent plasticity (SFDP) and the learning experience behavior. Finally, when integrated into an artificial neural network (ANN), the Ag/Ga2O3/Pt memristive device mimicked optical pulse potentiation and electrical pulse depression with high pattern accuracy (90.7%). The single memristive cells with multifunctional features are promising candidates for optoelectronic memory storage, neuromorphic computing, and artificial visual perception applications.

Phase-Engineered In2Se3 Ferroelectric P-N Junctions in Phototransistors for Ultra-Low Power and Multiscale Reservoir Computing

Two-dimensional (2D) ferroelectric field-effect transistors (Fe-FETs) based on p-n junctions are the basic units of future neuromorphic hardware. The In2Se3 semiconductor with ferroelectric, photoelectric, and phase transition properties possesses great application potential for in-sensor computing, but its ferroelectric p-n junction (FePNJ) is not well investigated. Here, we present an optoelectronic synapse made of uniformly full-coverage α-In2Se3/WSe2 FePNJ, achieving ultralow-power classification recognition and multiscale signal processing. Using chemical vapor deposition (CVD), we can obtain β'-In2Se3/WSe2 subferroelectric p-n junctions by direct growth on SiO2/Si substrate and α-In2Se3/WSe2 FePNJ by phase transition. Modulated by the synergistic effect of the polarization electric field and the built-in electric field, the FePNJ exhibits significantly enhanced and highly tunable synaptic effects (memory retention >2500 s and >8 multilevel current states under single optical/electrical pulses), along with power consumption down to atto-joule levels. Utilizing these photoelectric properties, we constructed an all-ferroelectric in-sensor reservoir computing system, comprising both reservoir and readout networks, achieving ultralow-power handwritten digit recognition. We also created a multiscale reservoir computing system through the gate-voltage-modulated relaxation time scale of the FePNJ, which can efficiently detect motions in the 1 to 100 km h-1 speed range.

2D Atomic-Molecular Heterojunctions toward Brainoid Applications

Brainoid computing using 2D atomic crystals and their heterostructures, by emulating the human brain's remarkable efficiency and minimal energy consumption in information processing, poses a formidable solution to the energy-efficiency and processing speed constraints inherent in the von Neumann architecture. However, conventional 2D material based heterostructures employed in brainoid devices are beset with limitations, performance uniformity, fabrication intricacies, and weak interfacial adhesion, which restrain their broader application. The introduction of novel 2D atomic-molecular heterojunctions (2DAMH), achieved through covalent functionalization of 2D materials with functional molecules, ushers in a new era for brain-like devices by providing both stability and tunability of functionalities. This review chiefly delves into the electronic attributes of 2DAMH derived from the synergy of polymer materials with 2D materials, emphasizing the most recent advancements in their utilization within memristive devices, particularly their potential in replicating the functionality of biological synapses. Despite ongoing challenges pertaining to precision in modification, scalability in production, and the refinement of underlying theories, the proliferation of innovative research is actively pursuing solutions. These endeavors illuminate the vast potential for incorporating 2DAMH within brain-inspired intelligent systems, highlighting the prospect of achieving a more efficient and energy-conserving computing paradigm.

2D Van Der Waals Ferroelectric Materials and Devices for Neuromorphic Computing

2D van der Waals (vdW) ferroelectric materials are emerging as transformative components in modern electronics and neuromorphic computing. The atomic-scale thickness, coupled with robust ferroelectric properties and seamless integration into vdW engineering, offers unprecedented opportunities for the development of high-performance and low-power devices. Notably, 2D ferroelectric devices excel in enabling multistate storage and neuromorphic functionalities in emulating synapses or retinas, positioning them as prime candidates for next-generation in-sensor-and-memory units. Despite ongoing challenges such as scalability, material stability, and uniformity, rapid interdisciplinary advancements and advancing nanofabrication processes are driving the field forward. This review delves into the fundamental principles of 2D ferroelectricity, highlights typical materials, and examines key device structures along with their applications in non-von Neumann architecture development and neuromorphic computing. By providing an in-depth overview, this work underscores the potential of 2D ferroelectric materials to revolutionize the future of electronics.

Biomaterials for neuroengineering: applications and challenges

Neurological injuries and diseases are a leading cause of disability worldwide, underscoring the urgent need for effective therapies. Neural regaining and enhancement therapies are seen as the most promising strategies for restoring neural function, offering hope for individuals affected by these conditions. Despite their promise, the path from animal research to clinical application is fraught with challenges. Neuroengineering, particularly through the use of biomaterials, has emerged as a key field that is paving the way for innovative solutions to these challenges. It seeks to understand and treat neurological disorders, unravel the nature of consciousness, and explore the mechanisms of memory and the brain's relationship with behavior, offering solutions for neural tissue engineering, neural interfaces and targeted drug delivery systems. These biomaterials, including both natural and synthetic types, are designed to replicate the cellular environment of the brain, thereby facilitating neural repair. This review aims to provide a comprehensive overview for biomaterials in neuroengineering, highlighting their application in neural functional regaining and enhancement across both basic research and clinical practice. It covers recent developments in biomaterial-based products, including 2D to 3D bioprinted scaffolds for cell and organoid culture, brain-on-a-chip systems, biomimetic electrodes and brain-computer interfaces. It also explores artificial synapses and neural networks, discussing their applications in modeling neural microenvironments for repair and regeneration, neural modulation and manipulation and the integration of traditional Chinese medicine. This review serves as a comprehensive guide to the role of biomaterials in advancing neuroengineering solutions, providing insights into the ongoing efforts to bridge the gap between innovation and clinical application.

Molecular Cocrystal Strategy for Retinamorphic Vision with UV-Vis-NIR Perception and Fast Recognition

Neuromorphic vision sensors capable of multispectral perception and efficient recognition are highly desirable for bioretina emulation, but their realization is challenging. Here, we present a cocrystal strategy for preparing an organic nanowire retinamorphic vision sensor with UV-vis-NIR perception and fast recognition. By leveraging molecular-scale donor-acceptor interpenetration and charge-transfer interfaces, the cocrystal nanowire device exhibits ultrawide photoperception ranging from 350 to 1050 nm, fast photoresponse of 150 ms, high specific detectivity of 8.2 × 1012 Jones, and responsivity of 15 A W-1, as well as retina-like photosynaptic plasticity behaviors. Utilizing the sensor nerve and convolutional neural network, the architecture achieves 90% accuracy in recognizing colorful images. The cocrystal design offers an effective method for constructing nanowire photosynases with high performance in artificial visual systems.

Complementary Circuits with WSe2/Organic Semiconductor Heterostructure Field-Effect Transistors

A device architecture based on heterostructure WSe2/organic semiconductor field-effect transistors (FETs) is demonstrated in which ambipolar conduction is virtually eliminated, resulting in essentially unipolar FETs realized from an ambipolar semiconductor. For p-channel FETs, an electron-accepting organic semiconductor such as hexadecafluorocopperphthalocyanine (F16CuPc) is used to form a heterolayer on top of WSe2 to effectively trap any undesirable electron currents. For n-channel FETs, a hole-accepting organic semiconductor such as pentacene is used to reduce the hole currents without affecting the electron currents. Off-currents are reduced in FETs with heterolayers compared to WSe2 FETs without organic heterolayers, which will decrease static power dissipation in complementary circuits. In all FETs reported in this work, the organic heterolayers cover only part of the channel, which results in more effective trapping of the carrier type that must be reduced. This device design approach can be effectively combined with p-type doping and contact metal engineering to improve WSe2 based FETs and circuits. Complementary inverters realized with such heterostructured FETs exhibit excellent transfer characteristics. This design approach is also applicable to other ambipolar semiconductors besides WSe2.

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.

All-Optically Controlled Memristive Device Based on Cu2O/TiO2 Heterostructure Toward Neuromorphic Visual System

The optoelectronic memristor integrates the multifunctionalities of image sensing, storage, and processing, which has been considered as the leading candidate to construct novel neuromorphic visual system. In particular, memristive materials with all-optical modulation and complementary metal oxide semiconductor (CMOS) compatibility are highly desired for energy-efficient image perception. As a p-type oxide material, Cu2O exhibits outstanding theoretical photoelectric conversion efficiency and broadband photoresponse. In this work, an all-optically controlled memristor based on the Cu2O/TiO2/sodium alginate nanocomposite film is developed. Optical potentiation and depression behaviors have been implemented by utilizing visible (680 nm) and ultraviolet (350 nm) light. Furthermore, a 7 × 9 optoelectronic memristive array with satisfactory device variation and environment stability is constructed to emulate the image preprocessing function in biological retina. The random noise can be reduced effectively by utilizing bidirectional optical input. Beneficial from the image preprocessing function, the accuracy of handwritten digit classification increases more than 60%. Our work presents a pathway toward high-efficient neuromorphic visual system and promotes the development of artificial intelligence technology.

Two-Dimensional Organic-Inorganic van der Waals Hybrids

Two-dimensional organic-inorganic (2DOI) van der Waals hybrids (vdWhs) have emerged as a groundbreaking subclass of layer-stacked (opto-)electronic materials. The development of 2DOI-vdWhs via systematically integrating inorganic 2D layers with organic 2D crystals at the molecular/atomic scale extends the capabilities of traditional 2D inorganic vdWhs, thanks to their high synthetic flexibility and structural tunability. Constructing an organic-inorganic hybrid interface with atomic precision will unlock new opportunities for generating unique interfacial (opto-)electronic transport properties by combining the strengths of organic and inorganic layers, thus allowing us to satisfy the growing demand for multifunctional applications. Here, this review provides a comprehensive overview of the latest advancements in the chemical synthesis, structural characterization, and numerous applications of 2DOI-vdWhs. Firstly, we introduce the chemistry and the physical properties of the recently rising organic 2D crystals (O2DCs), which feature crystalline 2D nanostructures comprising carbon-rich repeated units linked by covalent/noncovalent bonds and exhibit strong in-plane extended π-conjugation and weak interlayer vdWs interaction. Simultaneously, representative inorganic 2D crystals (I2DCs) are briefly summarized. After that, the synthetic strategies will be systematically summarized, including synthesizing single-component O2DCs with dimensional control and their vdWhs with I2DCs. With these synthetic approaches, the control in the dimension, the stacking modes, and the composition of the 2DOI-vdWhs will be highlighted. Subsequently, a special focus will be given on the discussion of the optical and electronic properties of the single-component 2D materials and their vdWhs, which will be closely relevant to their structures, so that we can establish a general structure-property relationship of 2DOI-vdWhs. In addition to these physical properties, the (opto-)electronic devices such as transistors, photodetectors, sensors, spintronics, and neuromorphic devices as well as energy devices will be discussed. Finally, we provide an outlook to discuss the key challenges for the 2DOI-vdWhs and their future development. This review aims to provide a foundational understanding and inspire further innovation in the development of next-generation 2DOI-vdWhs with transformative technological potential.

Dye Molecule-Induced Optoelectronic Synaptic Behaviors of Monolayer MoSe2

Although MoSe2-based photodetectors have achieved excellent performance, the ultrafast photoresponse has limited their application as an optoelectronic synapse. In this paper, the enhancement of the rhodamine 6G molecule on the memory time of MoSe2 is reported. It is found that the memory time of monolayer MoSe2 can be obviously enhanced after assembly with rhodamine 6G exhibiting synaptic characteristics in comparison to pristine MoSe2. Furthermore, the synaptic functions, including excitatory postsynaptic current, pair-pulse facilitation, short-term memory, long-term memory, "learning-experience" behavior, and tunable learning and forgetting process, can be achieved successfully. The distinctive energy-level structure of R6G and its excellent light absorption properties give MoSe2 access to an additional source of electrons, thus enabling the proposed MoSe2/rhodamine 6G hybrid heterostructure optoelectronic synapse to provide an efficient protocol for realizing optoelectronic neuromorphic computing and enormously facilitate the advancement of neuromorphic electronics.

Nanomaterials for Flexible Neuromorphics

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design. Therefore, it is imperative to conduct an extensive and comprehensive analysis to summarize current progress. This review highlights the advancements and applications of flexible neuromorphics, involving inorganic nanomaterials (zero-/one-/two-dimensional, and heterostructure), carbon-based nanomaterials such as carbon nanotubes (CNTs) and graphene, and polymers. Additionally, a comprehensive comparison and summary of the structural compositions, design strategies, key performance, and significant applications of these devices are provided. Furthermore, the challenges and future directions pertaining to materials/devices/systems associated with flexible neuromorphics are also addressed. The aim of this review is to shed light on the rapidly growing field of flexible neuromorphics, attract experts from diverse disciplines (e.g., electronics, materials science, neurobiology), and foster further innovation for its accelerated development.

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.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

Wireless and Opto-Stimulated Flexible Implants: Artificial Retina Constructed by Ferroelectric BiFeO3-BaTiO3/P(VDF-TrFE) Composites

The degeneration of retinal photoreceptors is one of the primary causes of blindness, and the implantation of retinal prostheses offers hope for vision restoration in individuals who are completely blind. Flexible bioelectronic devices present a promising avenue for the next generation of retinal prostheses owing to their soft mechanical properties and tissue friendliness. In this study, we developed flexible composite films of ferroelectric BiFeO3-BaTiO3 (BFO-BTO) particles synthesized by the hydrothermal method and ferroelectric poly(vinyldene difluoride-trifluoroethylene) (P(VDF-TrFE)) polymer and investigated their applications in artificial retinas. Owing to the coupling of the photothermal effect of BFO-BTO particles and the pyroelectric effect of the P(VDF-TrFE) polymer, the composite films demonstrate a strong photoelectric response (a maximum peak-to-peak photovoltage > 80 V under blue light of 100 mW/cm2) in a wide wavelength range of light (from visible to infrared) with the inherent flexibility and ease of preparation, making it an attractive candidate for artificial retinal applications. Experimental results showed that blind rats implanted with artificial retinas of the composites display light-responsive behavior, showcasing the effectiveness of vision restoration. This study demonstrates a novel approach for employing ferroelectric materials in vision restoration and offers insights into future artificial retina design.

Low-Cost, High-Efficiency Aluminum Zinc Oxide Synaptic Transistors: Blue LED Stimulation for Enhanced Neuromorphic Computing Applications

Neuromorphic devices are electronic devices that mimic the information processing methods of neurons and synapses, enabling them to perform multiple tasks simultaneously with low power consumption and exhibit learning ability. However, their large-scale production and efficient operation remain a challenge. Herein, we fabricated an aluminum-doped zinc oxide (AZO) synaptic transistor via solution-based spin-coating. The transistor is characterized by low production costs and high performance. It demonstrates high responsiveness under UV laser illumination. In addition, it exhibits effective synaptic behaviors under blue LED illumination, indicating high-efficiency operation. The paired-pulse facilitation (PPF) index measured from optical stimulus modulation was 179.6%, indicating strong synaptic connectivity and effective neural communication and processing. Furthermore, by modulating the blue LED light pulse frequency, an excitatory postsynaptic current gain of 4.3 was achieved, demonstrating efficient neuromorphic functionality. This study shows that AZO synaptic transistors are promising candidates for artificial synaptic devices.

Superior AlGaN/GaN-Based Phototransistors and Arrays with Reconfigurable Triple-Mode Functionalities Enabled by Voltage-Programmed Two-Dimensional Electron Gas for High-Quality Imaging

High-quality imaging units are indispensable in modern optoelectronic systems for accurate recognition and processing of optical information. To fulfill massive and complex imaging tasks in the digital age, devices with remarkable photoresponsive characteristics and versatile reconfigurable functions on a single-device platform are in demand but remain challenging to fabricate. Herein, an AlGaN/GaN-based double-heterostructure is reported, incorporated with a unique compositionally graded AlGaN structure to generate a channel of polarization-induced two-dimensional electron gas (2DEGs). Owing to the programmable feature of the 2DEGs by the combined gate and drain voltage inputs, with a particular capability of electron separation, collection and storage under different light illumination, the phototransistor shows reconfigurable multifunctional photoresponsive behaviors with superior characteristics. A self-powered mode with a responsivity over 100 A W-1 and a photoconductive mode with a responsivity of ≈108 A W-1 are achieved, with the ultimate demonstration of a 10 × 10 device array for imaging. More intriguingly, the device can be switched to photoelectric synapse mode, emulating synaptic functions to denoise the imaging process while prolonging the image storage ability. The demonstration of three-in-one operational characteristics in a single device offers a new path toward future integrated and multifunctional imaging units.

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.

Bioinspired Artificial Visual-Respiratory Synapse as Multimodal Scene Recognition System with Oxidized-Vacancies MXene

In the pursuit of artificial neural systems, the integration of multimodal plasticity, memory retention, and perceptual functions stands as a paramount objective in achieving neuromorphic perceptual components inspired by the human brain, to emulating the neurological excitability tuning observed in human visual and respiratory collaborations. Here, an artificial visual-respiratory synapse is presented with monolayer oxidized MXene (VRSOM) exhibiting synergistic light and atmospheric plasticity. The VRSOM enables to realize facile modulation of synaptic behaviors, encompassing postsynaptic current, sustained photoconductivity, stable facilitation/depression properties, and "learning-experience" behavior. These performances rely on the privileged photocarrier trapping characteristics and the hydroxyl-preferential selectivity inherent of oxidized vacancies. Moreover, environment recognitions and multimodal neural network image identifications are achieved through multisensory integration, underscoring the potential of the VRSOM in reproducing human-like perceptual attributes. The VRSOM platform holds significant promise for hardware output of human-like mixed-modal interactions and paves the way for perceiving multisensory neural behaviors in artificial interactive devices.

Multimodal Artificial Synapses for Neuromorphic Application

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

Unveiling cutting-edge developments: architectures and nanostructured materials for application in optoelectronic artificial synapses

One possible result of low-level characteristics in the traditional von Neumann formulation system is brain-inspired photonics technology based on human brain idea. Optoelectronic neural devices, which are accustomed to imitating the sensory role of biological synapses by adjusting connection measures, can be used to fabricate highly reliable neurologically calculating devices. In this case, nanosized materials and device designs are attracting attention since they provide numerous potential benefits in terms of limited cool contact, rapid transfer fluidity, and the capture of photocarriers. In addition, the combination of classic nanosized photodetectors with recently generated digital synapses offers promising results in a variety of practical applications, such as data processing and computation. Herein, we present the progress in constructing improved optoelectronic synaptic devices that rely on nanomaterials, for example, 0-dimensional (quantum dots), 1-dimensional, and 2-dimensional composites, besides the continuously developing mixed heterostructures. Furthermore, the challenges and potential prospects linked with this field of study are discussed in this paper.

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Toward Intelligent Display with Neuromorphic Technology

In the era of the Internet and the Internet of Things, display technology has evolved significantly toward full-scene display and realistic display. Incorporating "intelligence" into displays is a crucial technical approach to meet the demands of this development. Traditional display technology relies on distributed hardware systems to achieve intelligent displays but encounters challenges stemming from the physical separation of sensing, processing, and light-emitting modules. The high energy consumption and data transformation delays limited the development of intelligence display, breaking the physical separation is crucial to overcoming the bottlenecks of intelligence display technology. Inspired by the biological neural system, neuromorphic technology with all-in-one features is widely employed across various fields. It proves effective in reducing system power consumption, facilitating frequent data transformation, and enabling cross-scene integration. Neuromorphic technology shows great potential to overcome display technology bottlenecks, realizing the full-scene display and realistic display with high efficiency and low power consumption. This review offers a comprehensive summary of recent advancements in the application of neuromorphic technology in displays, with a focus on interoperability. This work delves into its state-of-the-art designs and potential future developments aimed at revolutionizing display technology.

Ab Initio Modeling of Mixed-Dimensional Heterostructures: A Path Forward

Understanding the electronic structure of mixed-dimensional heterostructures is essential for maximizing their application potential. However, accurately modeling such interfaces is challenging due to the complex interplay between the subsystems. We employ a computational framework integrating first-principles methods, including GW, density functional theory (DFT), and the polarizable continuum model, to elucidate the electronic structure of mixed-dimensional heterojunctions formed by free-base phthalocyanines and monolayer molybdenum disulfide. We assess the impact of dielectric screening across various scenarios, from isolated molecules to organic films on a substrate-supported monolayer. Our findings show that while polarization effects cause significant renormalization of molecular energy levels, band energies and alignments in the most relevant setup can be accurately predicted through DFT simulations of the individual subsystems. Additionally, we analyze orbital hybridization, revealing potential pathways for interfacial charge transfer. This study offers new insights into hybrid inorganic/organic interfaces and provides a practical computational protocol suitable for scaled-up studies.

Bio-Inspired Sensory Receptors for Artificial-Intelligence Perception

In the era of artificial intelligence (AI), there is a growing interest in replicating human sensory perception. Selective and sensitive bio-inspired sensory receptors with synaptic plasticity have recently gained significant attention in developing energy-efficient AI perception. Various bio-inspired sensory receptors and their applications in AI perception are reviewed here. The critical challenges for the future development of bio-inspired sensory receptors are outlined, emphasizing the need for innovative solutions to overcome hurdles in sensor design, integration, and scalability. AI perception can revolutionize various fields, including human-machine interaction, autonomous systems, medical diagnostics, environmental monitoring, industrial optimization, and assistive technologies. As advancements in bio-inspired sensing continue to accelerate, the promise of creating more intelligent and adaptive AI systems becomes increasingly attainable, marking a significant step forward in the evolution of human-like sensory perception.

Visualized in-sensor computing

In artificial nervous systems, conductivity changes indicate synaptic weight updates, but they provide limited information compared to living organisms. We present the pioneering design and production of an electrochromic neuromorphic transistor employing color updates to represent synaptic weight for in-sensor computing. Here, we engineer a specialized mechanism for adaptively regulating ion doping through an ion-exchange membrane, enabling precise control over color-coded synaptic weight, an unprecedented achievement. The electrochromic neuromorphic transistor not only enhances electrochromatic capabilities for hardware coding but also establishes a visualized pattern-recognition network. Integrating the electrochromic neuromorphic transistor with an artificial whisker, we simulate a bionic reflex system inspired by the longicorn beetle, achieving real-time visualization of signal flow within the reflex arc in response to environmental stimuli. This research holds promise in extending the biomimetic coding paradigm and advancing the development of bio-hybrid interfaces, particularly in incorporating color-based expressions.

Flexible and Energy-Efficient Synaptic Transistor with Quasi-Linear Weight Update Protocol by Inkjet Printing of Orientated Polar-Electret/High-k Oxide Composite Dielectric

Inkjet printing artificial synapse is cost-effective but challenging in emulating synaptic dynamics with a sufficient number of effective weight states under ultralow voltage spiking operation. A synaptic transistor gated by inkjet-printed composite dielectric of polar-electret polyvinylpyrrolidone (PVP) and high-k zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear weight update with a large variation margin is obtained through the coupling effect and the facilitation of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows. Crucial features of biological synapses including long-term plasticity, spike-timing-dependence-plasticity (STDP), "Learning-Experience" behavior, and ultralow energy consumption (<10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1%) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

Reconfigurable optoelectronic transistors for multimodal recognition

Biological nervous system outperforms in both dynamic and static information perception due to their capability to integrate the sensing, memory and processing functions. Reconfigurable neuromorphic transistors, which can be used to emulate different types of biological analogues in a single device, are important for creating compact and efficient neuromorphic computing networks, but their design remains challenging due to the need for opposing physical mechanisms to achieve different functions. Here we report a neuromorphic electrolyte-gated transistor that can be reconfigured to perform physical reservoir and synaptic functions. The device exhibits dynamics with tunable time-scales under optical and electrical stimuli. The nonlinear volatile property is suitable for reservoir computing, which can be used for multimodal pre-processing. The nonvolatility and programmability of the device through ion insertion/extraction achieved via electrolyte gating, which are required to realize synaptic functions, are verified. The device's superior performance in mimicking human perception of dynamic and static multisensory information based on the reconfigurable neuromorphic functions is also demonstrated. The present study provides an exciting paradigm for the realization of multimodal reconfigurable devices and opens an avenue for mimicking biological multisensory fusion.

Exciton Dynamics of TiOPc/WSe2 Heterostructure

The van der Waals (vdW) heterostructures composed of two-dimensional (2D) transition metal dichalcogenides (TMDs) and organic semiconductors demonstrate numerous compelling optoelectronic properties. However, the influence of the vdW epitaxial effect and temperature on the optoelectronic properties and interface exciton dynamics of heterostructures remains unclear. This study systematically investigates the fluorescence properties of TiOPc/WSe2 heterostructure. Comprehensive spectral characterization elucidates that the emission behavior of the TiOPc/WSe2 heterostructure arises from charge/energy transfer at the heterostructure interfaces and the structural ordering of the organic layer on the 2D monolayer WSe2 induced by vdW epitaxy. The interface exciton dynamic features probed by ultrafast transient spectroscopy reveal that the face-to-face molecular stacking configuration of TiOPc exhibits ultrafast exciton dynamics. In particular, we observe picosecond-scale absorption of organic molecular dimer cations, providing direct evidence of interface charge transfer at room temperature. Moreover, energy transfer from the TiOPc to WSe2 may exist based on the tunability in the fluorescence emission of the TiOPc/WSe2 heterostructure as the temperature changes. This study unveils the critical role of vdW epitaxy and temperature in the exciton dynamics of organic/2D TMDs hybrid systems and provides guidance for studying interlayer charge and energy transfer in organic/inorganic heterostructures.

Lateral heterostructures of WS2 and MoS2 monolayers for photo-synaptic transistor

Von Neumann architecture-based computing, while widely successful in personal computers and embedded systems, faces inherent challenges including the von Neumann bottleneck, particularly amidst the ongoing surge of data-intensive tasks. Neuromorphic computing, designed to integrate arithmetic, logic, and memory operations, has emerged as a promising solution for improving energy efficiency and performance. This approach requires the construction of an artificial synaptic device that can simultaneously perform signal processing, learning, and memory operations. We present a photo-synaptic device with 32 analog multi-states by exploiting field-effect transistors based on the lateral heterostructures of two-dimensional (2D) WS2 and MoS2 monolayers, formed through a two-step metal-organic chemical vapor deposition process. These lateral heterostructures offer high photoresponsivity and enhanced efficiency of charge trapping at the interface between the heterostructures and SiO2 due to the presence of the WS2 monolayer with large trap densities. As a result, it enables the photo-synaptic transistor to implement synaptic behaviors of long-term plasticity and high recognition accuracy. To confirm the feasibility of the photo-synapse, we investigated its synaptic characteristics under optical and electrical stimuli, including the retention of excitatory post-synaptic currents, potentiation, habituation, nonlinearity factor, and paired-pulse facilitation. Our findings suggest the potential of versatile 2D material-synapse with a high density of device integration.

Recent progress in emerging two-dimensional organic-inorganic van der Waals heterojunctions

Two-dimensional (2D) materials have attracted significant attention in recent decades due to their exceptional optoelectronic properties. Among them, to meet the growing demand for multifunctional applications, 2D organic-inorganic van der Waals (vdW) heterojunctions have become increasingly popular in the development of optoelectronic devices. These heterojunctions demonstrate impressive capability to synergistically combine the favourable characteristics of organic and inorganic materials, thereby offering a wide range of advantages. Also, they enable the creation of innovative device structures and introduce novel functionalities in existing 2D materials, avoiding the need for lattice matching in different material systems. Presently, researchers are actively working on improving the performance of devices based on 2D organic-inorganic vdW heterojunctions by focusing on enhancing the quality of 2D materials, precise stacking methods, energy band regulation, and material selection. Therefore, this review presents a thorough examination of the emerging 2D organic-inorganic vdW heterojunctions, including their classification, fabrication, and corresponding devices. Additionally, this review offers profound and comprehensive insight into the challenges in this field to inspire future research directions. It is expected to propel researchers to harness the extraordinary capabilities of 2D organic-inorganic vdW heterojunctions for a wider range of applications by further advancing the understanding of their fundamental properties, expanding the range of available materials, and exploring novel device architectures. The ongoing research and development in this field hold potential to unlock captivating advancements and foster practical applications across diverse industries.

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.

Stretchable and neuromorphic transistors for pain perception and sensitization emulation

Pain perception nociceptors (PPN), an important type of sensory neuron, are capable of sending out alarm signals when the human body is exposed to destructive stimuli. Simulating the human ability to perceive the external environment and spontaneously avoid injury is a critical function of neural sensing of artificial intelligence devices. The demand for developing artificial PPN has subsequently increased. However, due to the application scenarios of bionic electronic devices such as human skin, electronic prostheses, and robot bodies, where a certain degree of surface deformation constantly occurs, the ideal artificial PPN should have the stretchability to adapt to real scenarios. Here, an organic semiconductor nanofiber artificial pain perception nociceptor (NAPPN) based on a pre-stretching strategy is demonstrated to achieve key pain aspects such as threshold, sensitization, and desensitization. Remarkably, while stretching up to 50%, the synaptic behaviors and injury warning ability of NAPPN can be retained. To verify the wearability of the device, NAPPN was attached to a curved human finger joint, on which PPN behaviors were successfully mimicked. This provides a promising strategy for realizing neural sensing function on either deformed or mobile electronic devices.

Neuromorphic electro-stimulation based on atomically thin semiconductor for damage-free inflammation inhibition

Inflammation, caused by accumulation of inflammatory cytokines from immunocytes, is prevalent in a variety of diseases. Electro-stimulation emerges as a promising candidate for inflammatory inhibition. Although electroacupuncture is free from surgical injury, it faces the challenges of imprecise pathways/current spikes, and insufficiently defined mechanisms, while non-optimal pathway or spike would require high current amplitude, which makes electro-stimulation usually accompanied by damage and complications. Here, we propose a neuromorphic electro-stimulation based on atomically thin semiconductor floating-gate memory interdigital circuit. Direct stimulation is achieved by wrapping sympathetic chain with flexible electrodes and floating-gate memory are programmable to fire bionic spikes, thus minimizing nerve damage. A substantial decrease (73.5%) in inflammatory cytokine IL-6 occurred, which also enabled better efficacy than commercial stimulator at record-low currents with damage-free to sympathetic neurons. Additionally, using transgenic mice, the anti-inflammation effect is determined by β2 adrenergic signaling from myeloid cell lineage (monocytes/macrophages and granulocytes).

Humidity/Oxygen-Insensitive Organic Synaptic Transistors Based on Optical Radical Effect

For most organic synaptic transistors based on the charge trapping effect, different atmosphere conditions lead to significantly different device performance. Some devices even lose the synaptic responses under vacuum or inert atmosphere. The stable device performance of these organic synaptic transistors under varied working environments with different humidity and oxygen levels can be a challenge. Herein, a moisture- and oxygen-insensitive organic synaptic device based on the organic semiconductor and photoinitiator molecules is reported. Unlike the widely reported charge trapping effect, the photoinduced free radical is utilized to realize the photosynaptic performance. The resulting synaptic transistor displays typical excitatory postsynaptic current, paired-pulse facilitation, learning, and forgetting behaviors. Furthermore, the device exhibits decent and stable photosynaptic performances under high humidity and vacuum conditions. This type of organic synaptic device also demonstrates high potential in ultraviolet B perception based on its environmental stability and broad ultraviolet detection capability. Finally, the contrast-enhanced capability of the device is successfully validated by the single-layer-perceptron/double-layer network based Modified National Institute of Standards and Technology pattern recognition. This work could have important implications for the development of next-generation environment-stable organic synaptic devices and systems.

van der Waals 2D transition metal dichalcogenide/organic hybridized heterostructures: recent breakthroughs and emerging prospects of the device

The near-atomic thickness and organic molecular systems, including organic semiconductors and polymer-enabled hybrid heterostructures, of two-dimensional transition metal dichalcogenides (2D-TMDs) can modulate their optoelectronic and transport properties outstandingly. In this review, the current understanding and mechanism of the most recent and significant breakthrough of novel interlayer exciton emission and its modulation by harnessing the band energy alignment between TMDs and organic semiconductors in a TMD/organic (TMDO) hybrid heterostructure are demonstrated. The review encompasses up-to-date device demonstrations, including field-effect transistors, detectors, phototransistors, and photo-switchable superlattices. An exploration of distinct traits in 2D-TMDs and organic semiconductors delves into the applications of TMDO hybrid heterostructures. This review provides insights into the synthesis of 2D-TMDs and organic layers, covering fabrication techniques and challenges. Band bending and charge transfer via band energy alignment are explored from both structural and molecular orbital perspectives. The progress in emission modulation, including charge transfer, energy transfer, doping, defect healing, and phase engineering, is presented. The recent advancements in 2D-TMDO-based optoelectronic synaptic devices, including various 2D-TMDs and organic materials for neuromorphic applications are discussed. The section assesses their compatibility for synaptic devices, revisits the operating principles, and highlights the recent device demonstrations. Existing challenges and potential solutions are discussed. Finally, the review concludes by outlining the current challenges that span from synthesis intricacies to device applications, and by offering an outlook on the evolving field of emerging TMDO heterostructures.

Neuromorphic Photonics Circuits: Contemporary Review

Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-μM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

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.

Bio-inspired synaptic behavior simulation in thin-film transistors based on molybdenum disulfide

Synaptic behavior simulation in transistors based on MoS2 has been reported. MoS2 was utilized as the active layer to prepare ambipolar thin-film transistors. The excitatory postsynaptic current phenomenon was simulated, observing a gradual voltage decay following the removal of applied pulses, ultimately resulting in a response current slightly higher than the initial current. Subsequently, ±5 V voltages were separately applied for ten consecutive pulse voltage tests, revealing short-term potentiation and short-term depression behaviors. After 92 consecutive positive pulses, the device current transitioned from an initial value of 0.14 to 28.3 mA. Similarly, following 88 consecutive negative pulses, the device current changed, indicating long-term potentiation and long-term depression behaviors. We also employed a pair of continuous triangular wave pulses to evaluate paired-pulse facilitation behavior, observing that the response current of the second stimulus pulse was ∼1.2× greater than that of the first stimulus pulse. The advantages and prospects of using MoS2 as a material for thin-film transistors were thoroughly displayed.

Wavelength-Controlled Photoconductance Polarity Switching via Harnessing Defects in Doped PdSe2 for Artificial Synaptic Features

Optoelectronic synapses are currently drawing significant attention as fundamental building blocks of neuromorphic computing to mimic brain functions. In this study, a two-terminal synaptic device based on a doped PdSe2 flake is proposed to imitate the key neural functions in an optical pathway. Due to the wavelength-dependent desorption of oxygen clusters near the intrinsic selenide vacancy defects, the doped PdSe2 photodetector achieves a high negative photoresponsivity of -7.8 × 103 A W-1 at 473 nm and a positive photoresponsivity of 181 A W-1 at 1064 nm. This wavelength-selective bi-direction photoresponse endows an all-optical pathway to imitate the fundamental functions of artificial synapses on a device level, such as psychological learning and forgetting capability, as well as dynamic logic functions. The underpinning photoresponse is further demonstrated on a flexible platform, providing a viable technology for neuromorphic computing in wearable electronics. Furthermore, the p-type doping results in an effective increase of the channel's electrical conductivity and a significant reduction in power consumption. Such low-power-consuming optical synapses with simple device architecture and low-dimensional features demonstrate tremendous promise for building multifunctional artificial neuromorphic systems in the future.

Artificial Visual Systems Fabricated with Ferroelectric van der Waals Heterostructure for In-Memory Computing Applications

Rapid developments in artificial neural network techniques and retina-inspired artificial visual systems are required to realize the sensing, processing, and memorization of an optical signal in a single device. Herein, a ferroelectric field-effect transistor fabricated with CuInP2S6 and α-In2Se3 van der Waals heterostructures is proposed and demonstrated for the development of an artificial visual system. The dipole polarizations are coupled and bidirectionally locked inside the ferroelectric α-In2Se3 along the in-plane and out-of-plane directions and are controlled by the gate voltages. Furthermore, light-induced polarization can change the order of polarization of the dipoles inside α-In2Se3. We demonstrate that using the combined control of these electrical and optical signals, the device may function like a retina-inspired vision system. The device can operate across a wide wavelength range (405-850 nm) and detect very low incident light (0.03 mW/cm2). Color recognition, high paired-pulse facilitation (∼170%), and short- to long-term memory transitions through quick learning are observed using this device. Additionally, this device demonstrates different complex processing abilities, including pattern recognition, light adaptation, optical logic operation, and event learning. The proposed ferroelectric heterostructure-based artificial visual system can serve as an essential bridge for fulfilling the future requirements of all-in-one sensing and memory-processing devices.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.

Emerging Opportunities for 2D Materials in Neuromorphic Computing

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