Signal Filtering Enabled by Spike Voltage-Dependent Plasticity in Metalloporphyrin-Based Memristors

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.

Monolayer Vacancy-Induced MXene Memory for Write-Verify-Free Programming

The fundamental logic states of 1 and 0 in Complementary Metal-Oxide-Semiconductor (CMOS) are essential for modern high-speed non-volatile solid-state memories. However, the accumulated storage signal in conventional physical components often leads to data distortion after multiple write operations. This necessitates a write-verify operation to ensure proper values within the 0/1 threshold ranges. In this work, a non-gradual switching memory with two distinct stable resistance levels is introduced, enabled by the asymmetric vertical structure of monolayer vacancy-induced oxidized Ti3C2Tx MXene for efficient carrier trapping and releasing. This non-cumulative resistance effect allows non-volatile memories to attain valid 0/1 logic levels through direct reprogramming, eliminating the need for a write-verify operation. The device exhibits superior performance characteristics, including short write/erase times (100 ns), a large switching ratio (≈3 × 104), long cyclic endurance (>104 cycles), extended retention (>4 × 106 s), and highly resistive stability (>104 continuous write operations). These findings present promising avenues for next-generation resistive memories, offering faster programming speed, exceptional write performance, and streamlined algorithms.

Perovskite-Doped Modulated Color-Selective Photosynaptic Transistors for Target Object Recognition

The processing of multicolor noisy images in visual neuromorphic devices requires selective absorption at specific wavelengths; however, it is difficult to achieve this because the spectral absorption range of the device is affected by the type of material. Surprisingly, the absorption range of perovskite materials can be adjusted by doping. Herein, a CdCl2 co-doped CsPbBr3 nanocrystal-based photosensitive synaptic transistor (PST) is reported. By decreasing the doping concentration, the response of the PST to short-wavelength light is gradually enhanced, and even weak light of 40 μW·cm-2 can be detected. Benefiting from the excellent color selectivity of the PST device, the device array is applied to feature extraction of target blue items and removal of red and green noise, which results in the recognition accuracy of 95% for the noisy MNIST data set. This work provides new ideas for the application of novel transistors integrating sensors and storage computing.

Photoprogrammed Multifunctional Optoelectronic Synaptic Transistor Arrays Based on Photosensitive Polymer-Sorted Semiconducting Single-Walled Carbon Nanotubes for Image Recognition

The development of neuromorphic optoelectronic systems opens up the possibility of the next generation of artificial vision. In this work, the novel broadband (from 365 to 940 nm) and multilevel storage optoelectronic synaptic thin-film transistor (TFT) arrays are reported using the photosensitive conjugated polymer (poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-(bithiophene)], F8T2) sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) as channel materials. The broadband synaptic responses are inherited to absorption from both photosensitive F8T2 and sorted sc-SWCNTs, and the excellent optoelectronic synaptic behaviors with 200 linearly increasing conductance states and long retention time > 103 s are attributed to the superior charge trapping at the AlOx dielectric layer grown by atomic layer deposition. Furthermore, the synaptic TFTs can achieve IOn/IOff ratios up to 106 and optoelectronic synaptic plasticity with the low power consumption (59 aJ per single pulse), which can simulate not only basic biological synaptic functions but also optical write and electrical erase, multilevel storage, and image recognition. Further, a novel Spiking Neural Network algorithm based on hardware characteristics is designed for the recognition task of Caltech 101 dataset and multiple features of the images are successfully extracted with higher accuracy (97.92%) of the recognition task from the multi-frequency curves of the optoelectronic synaptic devices.

Dual-Adaptive Heterojunction Synaptic Transistors for Efficient Machine Vision in Harsh Lighting Conditions

Photoadaptive synaptic devices enable in-sensor processing of complex illumination scenes, while second-order adaptive synaptic plasticity improves learning efficiency by modifying the learning rate in a given environment. The integration of above adaptations in one phototransistor device will provide opportunities for developing high-efficient machine vision system. Here, a dually adaptable organic heterojunction transistor as a working unit in the system, which facilitates precise contrast enhancement and improves convergence rate under harsh lighting conditions, is reported. The photoadaptive threshold sliding originates from the bidirectional photoconductivity caused by the light intensity-dependent photogating effect. Metaplasticity is successfully implemented owing to the combination of ambipolar behavior and charge trapping effect. By utilizing the transistor array in a machine vision system, the details and edges can be highlighted in the 0.4% low-contrast images, and a high recognition accuracy of 93.8% with a significantly promoted convergence rate by about 5 times are also achieved. These results open a strategy to fully implement metaplasticity in optoelectronic devices and suggest their vision processing applications in complex lighting scenes.

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Advanced Design of Soft Robots with Artificial Intelligence

A comprehensive review focused on the whole systems of the soft robotics with artificial intelligence, which can feel, think, react and interact with humans, is presented. The design strategies concerning about various aspects of the soft robotics, like component materials, device structures, prepared technologies, integrated method, and potential applications, are summarized. A broad outlook on the future considerations for the soft robots is proposed.

In recent years, breakthrough has been made in the field of artificial intelligence (AI), which has also revolutionized the industry of robotics. Soft robots featured with high-level safety, less weight, lower power consumption have always been one of the research hotspots. Recently, multifunctional sensors for perception of soft robotics have been rapidly developed, while more algorithms and models of machine learning with high accuracy have been optimized and proposed. Designs of soft robots with AI have also been advanced ranging from multimodal sensing, human–machine interaction to effective actuation in robotic systems. Nonetheless, comprehensive reviews concerning the new developments and strategies for the ingenious design of the soft robotic systems equipped with AI are rare. Here, the new development is systematically reviewed in the field of soft robots with AI. First, background and mechanisms of soft robotic systems are briefed, after which development focused on how to endow the soft robots with AI, including the aspects of feeling, thought and reaction, is illustrated. Next, applications of soft robots with AI are systematically summarized and discussed together with advanced strategies proposed for performance enhancement. Design thoughts for future intelligent soft robotics are pointed out. Finally, some perspectives are put forward.

Memristor-Based Neuromorphic Chips

In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips. Subsequently, a thorough discussion of the memristor array, which serves as the pivotal component of the neuromorphic chip, as well as an examination of the present mainstream neural networks, is delved. Furthermore, the design of the neuromorphic chip is categorized into three crucial sections, including synapse-neuron cores, networks on chip (NoC), and neural network design. Finally, the key performance metrics of the chip is highlighted, as well as the key metrics related to the memristor devices are employed to realize both the synaptic and neuronal components.

Recent Progress in Wearable Near-Sensor and In-Sensor Intelligent Perception Systems

As the Internet of Things (IoT) becomes more widespread, wearable smart systems will begin to be used in a variety of applications in people's daily lives, not only requiring the devices to have excellent flexibility and biocompatibility, but also taking into account redundant data and communication delays due to the use of a large number of sensors. Fortunately, the emerging paradigms of near-sensor and in-sensor computing, together with the proposal of flexible neuromorphic devices, provides a viable solution for the application of intelligent low-power wearable devices. Therefore, wearable smart systems based on new computing paradigms are of great research value. This review discusses the research status of a flexible five-sense sensing system based on near-sensor and in-sensor architectures, considering material design, structural design and circuit design. Furthermore, we summarize challenging problems that need to be solved and provide an outlook on the potential applications of intelligent wearable devices.

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

To tackle the current crisis of Moore's law, a sophisticated strategy entails the development of multistable memristors, bionic artificial synapses, logic circuits and brain-inspired neuromorphic computing. In comparison with conventional electronic systems, iontronic memristors offer greater potential for the manifestation of artificial intelligence and brain-machine interaction. Organic iontronic memristive materials (OIMs), which possess an organic backbone and exhibit stoichiometric ionic states, have emerged as pivotal contenders for the realization of high-performance bionic iontronic memristors. In this review, a comprehensive analysis of the progress and prospects of OIMs is presented, encompassing their inherent advantages, diverse types, synthesis methodologies, and wide-ranging applications in memristive devices. Predictably, the field of OIMs, as a rapidly developing research subject, presents an exciting opportunity for the development of highly efficient neuro-iontronic systems in areas such as in-sensor computing devices, artificial synapses, and human perception.

Memristor-Based Artificial Chips

Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO3:MgO Film Memristor

Neuromorphic computing based on memristors capable of in-memory computing is promising to break the energy and efficiency bottleneck of well-known von Neumann architectures. However, unstable and nonlinear conductance updates compromise the recognition accuracy and block the integration of neural network hardware. To this end, we present a highly stable memristor with self-assembled vertically aligned nanocomposite (VAN) SrTiO3:MgO films that achieve excellent resistive switching with low set/reset voltage variability (4.7%/-5.6%) and highly linear conductivity variation (nonlinearity = 0.34) by spatially limiting the conductive channels at the vertical interfaces. Various synaptic behaviors are simulated by continuously modulating the conductance. Especially, convolutional image processing using diverse crossbar kernels is demonstrated, and the artificial neural network achieves an overwhelming recognition accuracy of up to 97.50% for handwritten digits. Even under the perturbation of Poisson noise (λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%, respectively. Importantly, the logic memory function is proven experimentally based on the nonvolatile properties. This work provides a material system and design idea to achieve high-performance neuromorphic computing and logic operation.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Rationally Designing High-Performance Versatile Organic Memristors through Molecule-Mediated Ion Movements

Organic memory has attracted tremendous attention for next-generation electronic elements for the molecules' striking ease of structural design. However, due to them being hardly controllable and their low ion transport, it is always essential and challenge to effectively control their random migration, pathway, and duration. There are very few effective strategies, and specific platforms with a view to molecules with specific coordination-groups-regulating ions have been rarely reported. In this work, as a generalized rational design strategy, the well-known tetracyanoquinodimethane (TCNQ) is introduced with multiple coordination groups and small plane structure into a stable polymers framework to modulate Ag migration and then achieve high-performance devices with ideal productivity, low operation voltage and power, stable switching cycles, and state retention. Raman mapping demonstrates that the migrated Ag can specially coordinate with the embedded TCNQ molecules. Notably, the TCNQ molecule distribution can be modulated inside the polymer framework and regulate the memristive behaviors through controlling the formed Ag conductive filaments (CFs) as demonstrated by Raman mapping, in situ conductive atomic force microscopy (C-AFM), X-ray diffraction (XRD) and depth-profiling X-ray photoelectron spectroscopy (XPS). Thus the controllable molecule-mediated Ag movements show its potential in rationally designing high-performance devices and versatile functions and is enlightening in constructing memristors with molecule-mediated ion movements.

New Chemical Dopant and Counterion Mechanism for Organic Electrochemical Transistors and Organic Mixed Ionic-Electronic Conductors

Organic mixed ionic-electronic conductors (OMIECs) have varied performance requirements across a diverse application space. Chemically doping the OMIEC can be a simple, low-cost approach for adapting performance metrics. However, complex challenges, such as identifying new dopant materials and elucidating design rules, inhibit its realization. Here, these challenges are approached by introducing a new n-dopant, tetrabutylammonium hydroxide (TBA-OH), and identifying a new design consideration underpinning its success. TBA-OH behaves as both a chemical n-dopant and morphology additive in donor acceptor co-polymer naphthodithiophene diimide-based polymer, which serves as an electron transporting material in organic electrochemical transistors (OECTs). The combined effects enhance OECT transconductance, charge carrier mobility, and volumetric capacitance, representative of the key metrics underpinning all OMIEC applications. Additionally, when the TBA+ counterion adopts an "edge-on" location relative to the polymer backbone, Coulombic interaction between the counterion and polaron is reduced, and polaron delocalization increases. This is the first time such mechanisms are identified in doped-OECTs and doped-OMIECs. The work herein therefore takes the first steps toward developing the design guidelines needed to realize chemical doping as a generic strategy for tailoring performance metrics in OECTs and OMIECs.

Spontaneous Threshold Lowering Neuron using Second-Order Diffusive Memristor for Self-Adaptive Spatial Attention

Intrinsic plasticity of neurons, such as spontaneous threshold lowering (STL) to modulate neuronal excitability, is key to spatial attention of biological neural systems. In-memory computing with emerging memristors is expected to solve the memory bottleneck of the von Neumann architecture commonly used in conventional digital computers and is deemed a promising solution to this bioinspired computing paradigm. Nonetheless, conventional memristors are incapable of implementing the STL plasticity of neurons due to their first-order dynamics. Here, a second-order memristor is experimentally demonstrated using yttria-stabilized zirconia with Ag doping (YSZ:Ag) that exhibits STL functionality. The physical origin of the second-order dynamics, i.e., the size evolution of Ag nanoclusters, is uncovered through transmission electron microscopy (TEM), which is leveraged to model the STL neuron. STL-based spatial attention in a spiking convolutional neural network (SCNN) is demonstrated, improving the accuracy of a multiobject detection task from 70% (20%) to 90% (80%) for the object within (outside) the area receiving attention. This second-order memristor with intrinsic STL dynamics paves the way for future machine intelligence, enabling high-efficiency, compact footprint, and hardware-encoded plasticity.

Polymeric Memristor Based Artificial Synapses with Ultra-Wide Operating Temperature

Neuromorphic electronics, being inspired by how the brain works, hold great promise to the successful implementation of smart artificial systems. Among several neuromorphic hardware issues, a robust device functionality under extreme temperature is of particular importance for practical applications. Given that the organic memristors for artificial synapse applications are demonstrated under room temperature, achieving a robust device performance at extremely low or high temperature is still utterly challenging. In this work, the temperature issue is addressed by tuning the functionality of the solution-based organic polymeric memristor. The optimized memristor demonstrates a reliable performance under both the cryogenic and high-temperature environments. The unencapsulated organic polymeric memristor shows a robust memristive response under test temperature ranging from 77 to 573 K. Utilizing X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary-ion mass spectrometry (ToF-SIMS) depth profiling, the device working mechanism is unveiled by comparing the compositional profiles of the fresh and written organic polymeric memristors. A reversible ion migration induced by an applied voltage contributes to the characteristic switching behavior of the memristor. Herein, both the robust memristive response achieved at extreme temperatures and the verified device working mechanism will remarkably accelerate the development of memristors in neuromorphic systems.

Emerging photoelectric devices for neuromorphic vision applications: principles, developments, and outlooks

The traditional von Neumann architecture is gradually failing to meet the urgent need for highly parallel computing, high-efficiency, and ultra-low power consumption for the current explosion of data. Brain-inspired neuromorphic computing can break the inherent limitations of traditional computers. Neuromorphic devices are the key hardware units of neuromorphic chips to implement the intelligent computing. In recent years, the development of optogenetics and photosensitive materials has provided new avenues for the research of neuromorphic devices. The emerging optoelectronic neuromorphic devices have received a lot of attentions because they have shown great potential in the field of visual bionics. In this paper, we summarize the latest visual bionic applications of optoelectronic synaptic memristors and transistors based on different photosensitive materials. The basic principle of bio-vision formation is first introduced. Then the device structures and operating mechanisms of optoelectronic memristors and transistors are discussed. Most importantly, the recent progresses of optoelectronic synaptic devices based on various photosensitive materials in the fields of visual perception are described. Finally, the problems and challenges of optoelectronic neuromorphic devices are summarized, and the future development of visual bionics is also proposed.

Pseudo-transistors for emerging neuromorphic electronics

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

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

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

Anisotropic charge trapping in phototransistors unlocks ultrasensitive polarimetry for bionic navigation

Being able to probe the polarization states of light is crucial for applications from medical diagnostics and intelligent recognition to information encryption and bio-inspired navigation. Current state-of-the-art polarimeters based on anisotropic semiconductors enable direct linear dichroism photodetection without the need for bulky and complex external optics. However, their polarization sensitivity is restricted by the inherent optical anisotropy, leading to low dichroic ratios of typically smaller than ten. Here, we unveil an effective and general strategy to achieve more than 2,000-fold enhanced polarization sensitivity by exploiting an anisotropic charge trapping effect in organic phototransistors. The polarization-dependent trapping of photogenerated charge carriers provides an anisotropic photo-induced gate bias for current amplification, which has resulted in a record-high dichroic ratio of >10⁴, reaching over the extinction ratios of commercial polarizers. These findings further enable the demonstration of an on-chip polarizer-free bionic celestial compass for skylight-based polarization navigation. Our results offer a fundamental design principle and an effective route for the development of next-generation highly polarization-sensitive optoelectronics.

Tunable Resistive Switching in 2D MXene Ti3C2 Nanosheets for Non-Volatile Memory and Neuromorphic Computing

An artificial synapse is essential for neuromorphic computing which has been expected to overcome the bottleneck of the traditional von-Neumann system. Memristors can work as an artificial synapse owing to their tunable non-volatile resistance states which offer the capabilities of information storage, processing, and computing. In this work, memristors based on two-dimensional (2D) MXene Ti₃C₂ nanosheets sandwiched by Pt electrodes are investigated in terms of resistive switching (RS) characteristics, synaptic functions, and neuromorphic computing. Digital and analog RS behaviors are found to coexist depending on the magnitude of operation voltage. Digital RS behaviors with two resistance states possessing a large switching ratio exceeding 10³ can be achieved under a high operation voltage. Analog RS behaviors with a series of resistance states exhibiting a gradual change can be observed at a relatively low operation voltage. Furthermore, artificial synapses can be implemented based on the memristors with the basic synaptic functions, such as long-term plasticity of long-term potentiation and depression and short-term plasticity of the paired-pulse facilitation and depression. Moreover, the "learning-forgetting" experience is successfully emulated based on the artificial synapses. Also, more importantly, the artificial synapses can construct an artificial neural network to implement image recognition. The coexistence of digital and analog RS behaviors in the 2D Ti₃C₂ nanosheets suggests the potential applications in non-volatile memory and neuromorphic computing, which is expected to facilitate simplifying the manufacturing complexity for complex neutral systems where analog and digital switching is essential for information storage and processing.

Gridization-Driven Mesoscale Self-Assembly of Conjugated Nanopolymers into Luminescence-Anisotropic Photonic Crystals

Organic semiconducting emitters integrated with butterfly-mimetic photonic crystals (PhCs) are fascinating for dramatic advantages over light outcoupling efficiency and multifunctional strain sensors, as well as the key step toward electrically pumped lasers. Herein, an unprecedentedly direct mesoscale self-assembly into 1D PhCs is reported through a covalently gridization-driven approach of wide-bandgap conjugated polymers. A simple solvent-casting procedure allows for in situ self-assembly of the state-of-the-art conjugated nanopolymer, poly{[4-(octyloxy)-9,9-diphenylfluoren-2,7-diyl]grid}-co-{[5-(octyloxy)-9,9-diphenylfluoren-2,7-diyl]grid} (PODPFG), into well-defined multilayer architectures with an excellent toughness (30-40 J m^-3 ). This ordered meso-architecture shows a typical Bragg-Snell diffraction behavior to testify the PhC nature, along with a high effective refractive index (1.80-1.88) and optical transmittance (85-87%). The PhC films also exhibit an angle-dependent blue/green photoluminescence switching, an electroluminescence efficiency enhancement by 150-250%, and an amplified spontaneous emission enhancement with ultralow waveguide loss coefficient (2.60 cm^-1 ). Gridization of organic semiconductors offers promising opportunities for cross-scale morphology-directed molecular design in multifunctional organic mechatronics and intelligences.