A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation

Advances in perovskite-based neuromorphic computing devices

Neuromorphic computing devices, inspired by the architecture and functionality of the human brain, offer a promising solution to the limitations imposed by the von Neumann bottleneck on contemporary computing systems. Perovskite materials are widely used in the photosensitive layer of neuromorphic computing devices due to their high light absorption coefficient and excellent carrier mobility. Here, we summarise the latest research progress on neural morphology computing devices based on perovskite materials with different structures and summarise different application scenarios. Finally, we discussed the issues that still need to be addressed and looked forward to the future development of neural morphology calculations based on perovskite materials.

Suppression of Tin Oxidation via Sn→B Bonding Interactions for High-Resolution Lead-Free Perovskite Neuromorphic Imaging Sensors

Lead-free tin-based perovskites, specifically (4-Cl-PEA)2SnI4, possess significant potential for the development of high-performance, robust neuromorphic imaging sensors, owing to their superior optoelectronic properties and compatibility with conventional complementary metal-oxide-semiconductor fabrication techniques and silicon-based readout circuits. However, the excessive oxidation of Sn2+ remains a significant obstacle, leading to suboptimal synaptic performance and low resolution in the neuromorphic imaging sensors due to increased recombination losses and poor film uniformity. This study first demonstrates that the introduction of novel Sn→B donor-acceptor bonding interactions effectively suppresses Sn2+ oxidation, enhancing uniformity, reducing nonradiative recombination, and improving synaptic plasticity. A vertical optoelectronic synapse demonstrates diverse synaptic behaviors, attributed to hole trapping and detrapping at the device interface. Additionally, the device enables applications in associative learning, neuromorphic computation, letter encoding, and handwritten digit recognition. Ultimately, integration with silicon circuits results in a high-resolution (32 × 32) neuromorphic imaging array, one of the highest reported resolutions for perovskite optoelectronic synapse arrays. The improved uniformity of boric acid-added (4-Cl-PEA)2SnI4 perovskite films significantly reduces photo response non-uniformity, enhances resolution, and improves memory capabilities. This neuromorphic imaging array successfully integrates sensing, storage, and computation, enabling advanced functionalities like letter recognition, memory, and processing, surpassing conventional image sensors.

Chelated tin halide perovskite for near-infrared neuromorphic imaging array enabling object recognition and motion perception

Neuromorphic imaging arrays integrate sensing, memory, and processing for efficient spatiotemporal fusion, enabling intelligent object and motion recognition in autonomous and surveillance systems. Halide perovskites offer potential for neuromorphic imaging by regulating photogenerated ions and charges, but lead toxicity and limited response range remain key limitations. Here, we present lead-free non-toxic formamidinium tin triiodide perovskites functionalized with bio-friendly quercetin molecules via a multi-site chelate strategy, achieving favorable near-infrared response and optoelectronic properties. Leveraging a non-equilibrium photogenerated carrier strategy, the formamidinium tin triiodide-quercetin based near-infrared optoelectronic synapses exhibit key synaptic features for practical applications, including quasi-linear time-dependent photocurrent generation, prolonged photocurrent decay, high stability, and low energy consumption. Ultimately, a 12 × 12 real-time neuromorphic near-infrared imaging array is constructed on thin-film transistor backplanes, enabling hardware-level spatiotemporal fusion for robust object recognition and motion perception in complex environments for autonomous and surveillance systems.

All-Inorganic Lead-Free Cs₂AgBiBr₆/ZnO Artificial Retina Synapse Based on Photoelectric Synergistic Dual-Mechanism for Neuromorphic Computing

Adaptive learning capability of optoelectronic synaptic hardware holds promising application prospects in next generation artificial intelligence, and the development of biometric retina perception is sternly hampered by three crucial issues, including well-balance between excitatory and inhibitory, non-volatile multi-state storage, and optimal energy consumption. In this work, a novel Cs2AgBiBr6/ZnO non-volatile optoelectronic synapse is proposed and successfully programmed with optical excitatory and electronic inhibitory in the light of dual-mechanism: Lead-free perovskite Cs2AgBiBr6 guarantees abundant photogenerated carrier concentration, and the process of carrier capture and release occurs in ZnO layer, which can collaboratively modulate various synaptic plasticity behaviors depending on distinct stimulus. Consequently, multi-bit storage is attained with the dual-mechanism non-volatile memory (DNVM) as a function of consecutive light spikes. The energy consumption of the DNVM is 1.85 nJ at a single light spike, and an ultra-low one of 13.8 fJ is triggered with a single electrical pulse, which approximatively meets the requirement of the biological synaptic event energy consumption. The performance of the DNVM is further evaluated with the Pavlov's classical conditioning experiment and visual hardware system, offering an exciting paradigm for implementing on-chip adaptive visual perception and neuromorphic computing.

Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points

Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neural-imitating electronics. This segregation hinders coordination and functionality within the neuromorphic system. Here, we present a flexible device tailored for neuromorphic computation and muscle actuation. Each individual device component emulates essential synaptic functions for neural computing, while the collective ensemble replicates muscle actuation in response to efferent neuromuscular commands. These properties stem from densely-packed, hydrophilic nanometer-sized channels, and the erection of a high-entropy, intricately silver nanowires to capture and store of hydrated cations. Leveraging the remarkable deformation effect, we demonstrate hazard detection-avoidance robot, and multidimensional integration for arbitrary programmed shapes like 360° panoramic information capture and soft-bodied biological deformations wherein localized responses to stimuli are harmoniously integrated to achieve arbitrary coordinated motion. These results provide a significant avenue for the development of future flexible electronics and bio-inspired systems.

Spontaneous bifacial capping of perovskite film for efficient and mechanically stable flexible solar cell

Flexible perovskite solar cells (F-PSCs) are appealing for their flexibility and high power-to-weight ratios. However, the fragile grain boundaries (GBs) in perovskite films can lead to stress and strain cracks under bending conditions, limiting the performance and stability of F-PSCs. Herein, we show that the perovskite film can facilely achieve in situ bifacial capping via introducing 4-(methoxy)benzylamine hydrobromide (MeOBABr) as the precursor additive. The spontaneously formed MeOBABr capping layers flatten the grain boundary grooves (GBGs), enable the release of the mechanical stress at the GBs during bending, rendering enhanced film robustness. They also contribute to the reduction of the residual strain and the passivation of the surface defects of the perovskite film. Besides, the molecular polarity of MeOBABr can result in surface band bending of the perovskite that favors the interfacial charge extraction. The corresponding inverted F-PSCs based on nickel oxide (NiOx)/poly(triaryl amine) (PTAA) hole transport bilayer reach a 23.7% power conversion efficiency (PCE) (22.9% certified) under AM 1.5 G illumination and a 42.46% PCE under 1000 lux indoor light illumination. Meanwhile, a robust bending durability of the device is also achieved.

Advances in Metal Halide Perovskite Memristors: A Review from a Co-Design Perspective

The memristor has recently demonstrated considerable potential in the field of large-scale data information processing. Metal halide perovskites (MHPs) have emerged as the leading contenders for memristors due to their sensitive optoelectronic response, low power consumption, and ability to be prepared at low temperatures. This work presents a comprehensive enumeration and analysis of the predominant research advancements in mechanisms of resistance switch (RS) behaviors in MHPs-based memristors, along with a summary of useful characterization techniques. The impact of diverse optimization techniques on the functionality of perovskite memristors is examined and synthesized. Additionally, the potential of MHPs memristors in data processing, physical encryption devices, artificial synapses, and brain-like computing advancement of MHPs memristors is evaluated. This review can prove a valuable reference point for the future development of perovskite memristors applications. In conclusion, the current challenges and prospects of MHPs-based memristors are discussed in order to provide insights into potential avenues for the development of next-generation information storage technologies and biomimetic applications.

An Organic Optoelectronic Synapse with Multilevel Memory Enabled by Gate Modulation

Artificial synaptic devices are emerging as contenders for next-generation computing systems due to their combined advantages of self-adaptive learning mechanisms, high parallel computation capabilities, adjustable memory level, and energy efficiency. Optoelectronic devices are particularly notable for their responsiveness to both voltage inputs and light exposure, making them attractive for dynamic modulation. However, engineering devices with reconfigurable synaptic plasticity and multilevel memory within a singular configuration present a fundamental challenge. Here, we have established an organic transistor-based synaptic device that exhibits both volatile and nonvolatile memory characteristics, modulated through gate voltage together with light stimuli. Our device demonstrates a range of synaptic behaviors, including both short/long-term plasticity (STP and LTP) as well as STP-LTP transitions. Further, as an encoding unit, it delivers exceptional read current levels, achieving a program/erase current ratio exceeding 105, with excellent repeatability. Additionally, a prototype 4 × 4 matrix demonstrates potential in practical neuromorphic systems, showing capabilities in the perception, processing, and memory retention of image inputs.

Recent advances in artificial neuromorphic applications based on perovskite composites

High-performance perovskite materials with excellent physical, electronic, and optical properties play a significant role in artificial neuromorphic devices. However, the development of perovskites in microelectronics is inevitably hindered by their intrinsic non-ideal properties, such as high defect density, environmental sensitivity, and toxicity. By leveraging materials engineering, integrating various materials with perovskites to leverage their mutual strengths presents great potential to enhance ion migration, energy level alignment, photoresponsivity, and surface passivation, thereby advancing optoelectronic and neuromorphic device development. This review initially provides an overview of perovskite materials across different dimensions, highlighting their physical properties and detailing their applications and metrics in two- and three-terminal devices. Subsequently, we comprehensively summarize the application of perovskites in combination with other materials, including organics, nanomaterials, oxides, ferroelectrics, and crystalline porous materials (CPMs), to develop advanced devices such as memristors, transistors, photodetectors, sensors, light-emitting diodes (LEDs), and artificial neuromorphic systems. Lastly, we outline the challenges and future research directions in synthesizing perovskite composites for neuromorphic devices. Through the review and analysis, we aim to broaden the utilization of perovskites and their composites in neuromorphic research, offering new insights and approaches for grasping the intricate physical working mechanisms and functionalities of perovskites.

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.

Efficient memristor accelerator for transformer self-attention functionality

The adoption of transformer networks has experienced a notable surge in various AI applications. However, the increased computational complexity, stemming primarily from the self-attention mechanism, parallels the manner in which convolution operations constrain the capabilities and speed of convolutional neural networks (CNNs). The self-attention algorithm, specifically the matrix-matrix multiplication (MatMul) operations, demands a substantial amount of memory and computational complexity, thereby restricting the overall performance of the transformer. This paper introduces an efficient hardware accelerator for the transformer network, leveraging memristor-based in-memory computing. The design targets the memory bottleneck associated with MatMul operations in the self-attention process, utilizing approximate analog computation and the highly parallel computations facilitated by the memristor crossbar architecture. Remarkably, this approach resulted in a reduction of approximately 10 times in the number of multiply-accumulate (MAC) operations in transformer networks, while maintaining 95.47% accuracy for the MNIST dataset, as validated by a comprehensive circuit simulator employing NeuroSim 3.0. Simulation outcomes indicate an area utilization of 6895.7 μ m 2 , a latency of 15.52 seconds, an energy consumption of 3 mJ, and a leakage power of 59.55 μ W . The methodology outlined in this paper represents a substantial stride towards a hardware-friendly transformer architecture for edge devices, poised to achieve real-time performance.

Crosslinking-induced anion transport control for enhancing linearity in organic synaptic devices

Numerous studies on neuromorphic computing systems and their associated synaptic devices have been reported for the efficient processing of complex data. Among them, organic electrochemical transistors (OECTs) have attracted considerable attention owing to their advantages such as low cost, high scalability, and facile electrical modulation. However, the requirement of supplementary processing for ionic transport control to actualize or enhance synaptic attributes necessitates a compromise between their inherent benefits. Here, we developed a simple method, photoinduced crosslinking, which can control the structure of conjugated polymers in OECTs to improve ionic transport control. Crosslinked polymers increase the ion doping efficiency and allow sequential anion movements, which leads to high linearity in OECTs. The fabricated device also exhibited enhanced synaptic properties such as a long retention time, wide dynamic range, and high recognition accuracy. This innovative approach opens up new possibilities for the construction of next-generation artificial synapses.

All-Inorganic CsPbBr3 Perovskite Planar-Type Memristors as Optoelectronic Synapses

Mimicking fundamental synaptic working principles with memristors contributes an essential step toward constructing brain-inspired, high-efficiency neuromorphic systems that surpass von Neumann system computers. Here, an electroforming-free planar-type memristor based on a CsPbBr3 single crystal is proposed and exhibits excellent resistive switching (RS) behaviors including stable endurance, ultralow power consumption, and fast switching speed. Furthermore, an optically tunable RS performance is demonstrated by manipulating irradiation intensity and wavelength. Optical analysis techniques such as steady-state photoluminescence and time-resolved photoluminescence are employed to investigate the distribution of Br ions and vacancies before and after quantitative polarization, describing migration dynamic processes to elucidate the RS mechanism. Importantly, a CsPbBr3 single crystal, as the optoelectronic synapse, shows unique potential to emulate photoenhanced synaptic functions such as excitatory postsynaptic current, paired-pulse facilitation, long-term potentiation/depression, spike-timing-dependent plasticity, spike-voltage-dependent plasticity, and learning-forgetting-relearning process with ultralow per synapse event energy consumption. A classical Pavlov's dog experiment is simulated with a combination of optical and electrical stimulation. Finally, pattern recognition with simulated artificial neural networks based on our synapse reached an accuracy of 93.11%. The special strategy and superior RS characteristics of optoelectronic synapses provide a pathway toward high-performance, energy-efficient neuromorphic electronics.

Synaptic Transistors Using Scalable Graphene Nanoribbons

Graphene has demonstrated potential for use in neuromorphic electronics due to its superior electrical properties. However, these devices are all based on graphene sheets without patterning, restricting its applications. Here, we demonstrate a graphene nanoribbon synaptic transistor (GNST), with the graphene nanoribbon (GNR) channels fabricated using an electro-hydrodynamically printed nanowire array as lithographic masks for scalable fabrication. The GNST shows tunable synaptic plasticity by spike duration, frequency, and number. Moreover, the device is energy-efficient and ambipolar and shows a regulated response by nanoribbon width. The characteristics of GNSTs are applicable to pattern recognition, showing an accuracy of 84.5%. The device is applicable to Pavlov's classical conditioning. This study reports the first synaptic transistor based on GNRs, providing new insights into future neuromorphic electronics.

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.

Toward a Brain-Neuromorphics Interface

Brain-computer interfaces (BCIs) that enable human-machine interaction have immense potential in restoring or augmenting human capabilities. Traditional BCIs are realized based on complementary metal-oxide-semiconductor (CMOS) technologies with complex, bulky, and low biocompatible circuits, and suffer with the low energy efficiency of the von Neumann architecture. The brain-neuromorphics interface (BNI) would offer a promising solution to advance the BCI technologies and shape the interactions with machineries. Neuromorphic devices and systems are able to provide substantial computation power with extremely high energy-efficiency by implementing in-materia computing such as in situ vector-matrix multiplication (VMM) and physical reservoir computing. Recent progresses on integrating neuromorphic components with sensing and/or actuating modules, give birth to the neuromorphic afferent nerve, efferent nerve, sensorimotor loop, and so on, which has advanced the technologies for future neurorobotics by achieving sophisticated sensorimotor capabilities as the biological system. With the development on the compact artificial spiking neuron and bioelectronic interfaces, the seamless communication between a BNI and a bioentity is reasonably expectable. In this review, the upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

Thermally and Mechanically Stable Perovskite Artificial Synapse as Tuned by Phase Engineering for Efferent Neuromuscular Control

The doping of perovskites with mixed cations and mixed halides is an effective strategy to optimize phase stability. In this study, we introduce a cubic black phase perovskite CsyFA(1-y)Pb(BrxI(1-x))3 artificial synapse, using phase engineering by adjusting the cesium-bromide content. Low-bromine mixed perovskites are suitable to improve the electric pulse excitation sensitivity and stability of the device. Specifically, the low-bromine and low-cesium mixed perovskite (x = 0.15, y = 0.22) annealed at 373 K allows the device to maintain logic response even after 1000 mechanical flex/flat cycles. The device also shows good thermal stability up to temperatures of 333 K. We have demonstrated reflex-arc behavior with MCMHP synaptic units, capable of making sensory warnings at high frequency. This compositionally engineered, dual-mixed perovskite synaptic device provides significant potential for perceptual soft neurorobotic systems and prostheses.

Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory

With the increasing demands and complexity of the neuromorphic computing schemes utilizing highly efficient analog resistive switching devices, understanding the apparent capacitive and inductive effects in device operation is of paramount importance. Here, we present a systematic array of characterization methods that unravel two distinct voltage-dependent regimes demonstrating the complex interplay between the dynamic capacitive and inductive effects in volatile perovskite-based memristors: (1) a low voltage capacitance-dominant and (2) an inductance-dominant regime evidenced by the highly correlated hysteresis type with nonzero crossing, the impedance responses, and the transient current characteristics. These dynamic capacitance- and inductance-dominant regimes provide fundamental insight into the resistive switching of memristors governing the synaptic depression and potentiation functions, respectively. More importantly, the pulse width-dependent and long-term transient current measurements further demonstrate a dynamic transition from a fast capacitive to a slow inductive response, allowing for the tailored stimulus programming of memristor devices to mimic synaptic functionality.

Artificial Tactile Sensing Neuron with Tactile Sensing Ability Based on a Chitosan Memristor

Owing to the highly parallel network structure of the biological neural network and its triggered processing mode, tactile sensory neurons can realize the perception of external signals and the functions of perception, memory, and data processing by adjusting the synaptic weight. In this paper, a piezoresistive pressure sensor is combined with a memristor to design an artificial tactile sensory neuron. The polyurethane sponge sensor has excellent sensitivity and can convert physical stimuli into electrical signals, and the chitosan-based memristor has stable bipolar resistive switching characteristics, allowing further information to be memorized and processed. The neuron can respond to tactile stimuli of different degrees, durations, and frequencies; realize potentiation/depression modulation, paired-pulse facilitation, and spike-timing-dependent plasticity; exhibit spike-rate-dependent plasticity; and store and erase tactile information through memistor state switching, which has great application potential in biological sensing systems.

Low-Power Perovskite Neuromorphic Synapse with Enhanced Photon Efficiency for Directional Motion Perception

The advancement of artificial intelligent vision systems heavily relies on the development of fast and accurate optical imaging detection, identification, and tracking. Framed by restricted response speeds and low computational efficiency, traditional optoelectronic information devices are facing challenges in real-time optical imaging tasks and their ability to efficiently process complex visual data. To address the limitations of current optoelectronic information devices, this study introduces a novel photomemristor utilizing halide perovskite thin films. The fabrication process involves adjusting the iodide proportion to enhance the quality of the halide perovskite films and minimize the dark current. The photomemristor exhibits a high external quantum efficiency of over 85%, which leads to a low energy consumption of 0.6 nJ. The spike timing-dependent plasticity characteristics of the device are leveraged to construct a spiking neural network and achieve a 99.1% accuracy rate of directional perception for moving objects. The notable results offer a promising hardware solution for efficient optoneuromorphic and edge computing applications.

Unveiling the role of linear alkyl organic cations in 2D layered tin halide perovskite field-effect transistors

Two-dimensional (2D) tin halide perovskites are promising semiconductors for field-effect transistors (FETs) owing to their fascinating electronic properties. However, the correlation between the chemical nature of organic cations and charge carrier transport is still far from understanding. In this study, the influence of chain length of linear alkyl ammonium cations on film morphology, crystallinity, and charge transport in 2D tin halide perovskites is investigated. The carbon chain lengths of the organic spacers vary from propylammonium to heptanammonium. The increase of alkyl chain length leads to enhanced local charge carrier transport in the perovskite film with mobilities of up to 8 cm2 V-1 s-1, as confirmed by optical-pump terahertz spectroscopy. A similar improved macroscopic charge transport is also observed in FETs, only to the chain length of HA, due to the synergistic enhancement of film morphology and molecular organization. While the mobility increases with the temperature rise from 100 K to 200 K due to the thermally activated transport mechanism, the device performance decreases in the temperature range of 200 K to 295 K because of ion migration. These results provide guidelines on rational design principles of organic spacer cations for 2D tin halide perovskites and contribute to other optoelectronic applications.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

High-Density Artificial Synapse Array Consisting of Homogeneous Electrolyte-Gated Transistors

The artificial synapse array with an electrolyte-gated transistor (EGT) as an array unit presents considerable potential for neuromorphic computation. However, the integration of EGTs faces the drawback of the conflict between the polymer electrolytes and photo-lithography. This study presents a scheme based on a lateral-gate structure to realize high-density integration of EGTs and proposes the integration of 100 × 100 EGTs into a 2.5 × 2.5 cm2 glass, with a unit density of up to 1600 devices cm-2 . Furthermore, an electrolyte framework is developed to enhance the array performance, with ionic conductivity of up to 2.87 × 10-3  S cm-1 owing to the porosity of zeolitic imidazolate frameworks-67. The artificial synapse array realizes image processing functions, and exhibits high performance and homogeneity. The handwriting recognition accuracy of a representative device reaches 92.80%, with the standard deviation of all the devices being limited to 9.69%. The integrated array and its high performance demonstrate the feasibility of the scheme and provide a solid reference for the integration of EGTs.

Mixed-Dimensional Nanoparticle-Nanowire Channels for Flexible Optoelectronic Artificial Synapse with Enhanced Photoelectric Response and Asymmetric Bidirectional Plasticity

A mixed-dimensional dual-channel synaptic transistor composed of inorganic nanoparticles and organic nanowires was fabricated to expand the photoelectric gain range. The device can actualize the sensitization features of the nociceptor and shows improved responsiveness to visible light. Under electrical pulses with different polarities, the apparatus exhibits reconfigurable asymmetric bidirectional plasticity. Moreover, the devices demonstrate good operational tolerance and mechanical stability, retaining more than 60% of their maximum responsiveness after 100 consecutive/bidirectional and 1000 flex/flat operations. The improved photoelectric response of the device endows a high image recognition accuracy of greater than 80%. Asymmetric bidirectional plasticity is used as punishment/reward in a psychological experiment to emulate the improvement of learning motivation and enables real-time forward and backward deflection (+7 and -25°) of artificial muscle. The mixed-dimensional optoelectronic artificial synapses with switchable behavior and electron/hole transport type have important prospects for neuromorphic processing and artificial somatosensory nerves.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Metal-Oxide Heterojunction Optoelectronic Synapse and Multilevel Memory Devices Enabled by Broad Spectral Photocarrier Modulation

Broad spectral response and high photoelectric conversion efficiency are key milestones for realizing multifunctional, low-power optoelectronic devices such as artificial synapse and reconfigurable memory devices. Nevertheless, the wide bandgap and narrow spectral response of metal-oxide semiconductors are problematic for efficient metal-oxide optoelectronic devices such as photonic synapse and optical memory devices. Here, a simple titania (TiO2 )/indium-gallium-zinc-oxide (IGZO) heterojunction structure is proposed for efficient multifunctional optoelectronic devices, enabling widen spectral response range and high photoresponsivity. By overlaying a TiO2 film on IGZO, the light absorption range extends to red light, along with enhanced photoresponsivity in the full visible light region. By implementing the TiO2 /IGZO heterojunction structure, various synaptic behaviors are successfully emulated such as short-term memory/long-term memory and paired pulse facilitation. Also, the TiO2 /IGZO synaptic transistor exhibits a recognition rate up to 90.3% in recognizing handwritten digit images. Moreover, by regulating the photocarrier dynamics and retention behavior using gate-bias modulation, a reconfigurable multilevel (≥8 states) memory is demonstrated using visible light.

Imidazole-based artificial synapses for neuromorphic computing: a cluster-type conductive filament via controllable nanocluster nucleation

Memristive synapses based on conductive bridging RAMs (CBRAMs) utilize a switching layer having low binding energy with active metals for excellent analog conductance modulation, but the resulting unstable conductive filaments cause fluctuation and drift of the conductance. This tunability-stability dilemma makes it difficult to implement practical neuromorphic computing. A novel method is proposed to enhance the stability and controllability of conductive filaments by introducing imidazole groups that boost the nucleation of Cu nanoclusters in the ultrathin polymer switching layer through the initiated chemical vapor deposition (iCVD) process. It is confirmed that conductive filaments based on nanoclusters with specific gaps are generated in the copolymer medium using this method. Furthermore, by modulating the tunneling gaps, an ultra-wide conductance range of analog tunable conductive filaments is achieved from several hundreds of nS to a few mS with a sub-1 V driving voltage. Through this, both reliable and stable analog switching are achieved with low cycle-to-cycle and device-to-device weight update variations and separable state retention with 32 states. This approach paves the way for the extension of state availability in synaptic devices to overcome the tunability-stability dilemma, which is essential for the synaptic elements in neuromorphic systems.

Mechanistic and Kinetic Analysis of Perovskite Memristors with Buffer Layers: The Case of a Two-Step Set Process

With the increasing demand for artificially intelligent hardware systems for brain-inspired in-memory and neuromorphic computing, understanding the underlying mechanisms in the resistive switching of memristor devices is of paramount importance. Here, we demonstrate a two-step resistive switching set process involving a complex interplay among mobile halide ions/vacancies (I^-/V_I⁺) and silver ions (Ag⁺) in perovskite-based memristors with thin undoped buffer layers. The resistive switching involves an initial gradual increase in current associated with a drift-related halide migration within the perovskite bulk layer followed by an abrupt resistive switching associated with diffusion of mobile Ag⁺ conductive filamentary formation. Furthermore, we develop a dynamical model that explains the characteristic I-V curve that helps to untangle and quantify the switching regimes consistent with the experimental memristive response. This further insight into the two-step set process provides another degree of freedom in device design for versatile applications with varying levels of complexity.