Mimicking Classical Conditioning Based on a Single Flexible Memristor

A Self-Formed Ag Nanostructure Based Neuromorphic Device Performing Arithmetic Computation and Area Integration: Influence of Presynaptic Pulsing Scheme on Mathematical Precision

A material equivalent of a biosynapse is the key to neuromorphic architecture. Here we report a self-forming labyrinthine Ag nanostructure activated with a few pulses of 0.5 V, width and interval set at 50 ms, at current compliance (ICC) of 400 nA, serving as the active material for a highly stable device with programmable volatility. Both the conductance (G) and its retention time (tr) in the potentiated state are found to vary linearly with the pulse number for pulses of positive and negative polarities, with the nonlinearity factors being noticeably small, ∼0.03 for G during potentiation and ∼0.08 during depression. This was tested for over 200 days, and the results were highly reproducible. Relying on the high linearity, arithmetic operations involving counting of positive and negative integers were realized using pulses of both polarities, often by mixing them in the feeding sequence. The observed outcomes based on G and independently from tr are highly accurate, with deviations being typically less than ∼1.5% from the expected results. Notably, the way the pulse polarities are mixed is found to have an influence, with random sequences producing relatively larger deviations in integer estimation. However, deviations decreased with higher ICC values, which promoted stronger filament formation in the percolation networks. Besides, the G and tr values were found to vary with the pulse amplitude as well, which enabled the calculation of the area under a curve. Further, the device exhibited a simulation-based image classification accuracy of 94.95%, close to the ideal value (96.05%). Simulations utilizing the finite element method have showcased the uniqueness of the labyrinthine morphology, giving rise to field intensification along potential percolative paths.

Two-Dimensional Electrically Conductive Metal-Organic Framework Boosts Synaptic Plasticity for Dynamic Image Refresh, Classification, and Efferent Neuromuscular Systems

We present a two-dimensional (2D) electrically conductive metal-organic framework (EC-MOF)-based artificial synapse. The intrinsic electronic conductivity and subnanometer channels of the EC-MOF facilitate efficient ion diffusion, enable a high density of active redox centers, and significantly enhance capacitance within the artificial synapse. As a result, the synapse operates at an ultralow voltage of 10 mV and exhibits a remarkably low power consumption of approximately 1 fW, along with the longest retention time recorded for two-terminal electrolyte-type artificial synapses to date. The alignment of the quantum size of the subnanometer pores in the EC-MOF with various cations allows for versatile synaptic plasticity. This capability is applied to image refresh, classification, and efferent signal transmission for controlling artificial muscles, thereby offering a methodology for achieving tunable neuromorphic properties. These findings suggest the potential application of metal-organic frameworks in artificial nervous systems for future brain-inspired computation, peripheral interfaces, and neurorobotics.

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.

Recent advances in flexible memristors for advanced computing and sensing

Conventional computing systems based on von Neumann architecture face challenges such as high power consumption and limited data processing capability. Improving device performance via scaling guided by Moore's Law becomes increasingly difficult. Emerging memristors can provide a promising solution for achieving high-performance computing systems with low power consumption. In particular, the development of flexible memristors is an important topic for wearable electronics, which can lead to intelligent systems in daily life with high computing capacity and efficiency. Here, recent advances in flexible memristors are reviewed, from operating mechanisms and typical materials to representative applications. Potential directions and challenges for future study in this area are also discussed.

Three-Dimensional Integrated Synaptic Devices Based on a Silver-Cluster Conduction Mechanism with High Thermostability

During the operation of synaptic devices based on traditional conductive filament (CF) models, the formation and dissolution of CFs are usually uncertain. Moreover, when the device is operated for a long time, the CFs may dissolve due to both the Joule heat generated by the device itself and the thermal coupling between the devices. These problems seriously reduce the reliability and stability of the synaptic device. Here, an artificial synapse device based on polyimide-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites is presented. Research has shown that MoS2 QDs doped into the active layer can effectively induce the reduction of Ag ions into Ag atoms, leading to the formation of Ag clusters and thereby achieving control over the growth of the CFs. Therefore, the device is capable of stably realizing various basic synaptic functions. Moreover, the long-term potentiation/long-term depression (LTP/LTD) of this device shows good linearity. In addition, due to the change in the shape of the CFs, the highly integrated devices with a three-dimensional (3D) stacked structure can operate normally even in a high-temperature environment of 110 °C. Finally, the synaptic characteristics of the devices on learning and inference tests show that their recognition rates are approximately 90.75% (room temperature) and 90.63% (110 °C).

Human somatosensory systems based on sensor-memory-integrated technology

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

Silver Nanowire Networks with Moisture-Enhanced Learning Ability

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

Tailoring Classical Conditioning Behavior in TiO2 Nanowires: ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learning capabilities presents fascinating applications in the next generation of artificial intelligence. However, research into synaptic devices exhibiting complex associative learning behaviors is still nascent. Here, an optoelectronic memristor based on Ag/TiO2 Nanowires: ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors. Effective implementation of synaptic behaviors, including long and short-term plasticity, and learning-forgetting-relearning behaviors, were achieved in the device through the application of light and electrical stimuli. Leveraging the optoelectronic co-modulated characteristics, a simulation of neuromorphic computing was conducted, resulting in a handwriting digit recognition accuracy of 88.9%. Furthermore, a 3 × 7 memristor array was constructed, confirming its application in artificial visual memory. Most importantly, complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli, respectively. After training through associative pairs, reflexes could be triggered solely using light stimuli. Comprehensively, under specific optoelectronic signal applications, the four features of classical conditioning, namely acquisition, extinction, recovery, and generalization, were elegantly emulated. This work provides an optoelectronic memristor with associative behavior capabilities, offering a pathway for advancing brain-machine interfaces, autonomous robots, and machine self-learning in the future.

Neuromorphic devices realised using self-forming hierarchical Al and Ag nanostructures: towards energy-efficient and wide ranging synaptic plasticity

Closely mimicking the hierarchical structural topology with emerging behavioral functionalities of biological neural networks in neuromorphic devices is considered of prime importance for the realization of energy-efficient intelligent systems. In this article, we report an artificial synaptic network (ASN) comprising of hierarchical structures of isolated Al and Ag micro-nano structures developed via the utilization of a desiccated crack pattern, anisotropic dewetting, and self-formation. The strategically designed ASN, despite having multiple synaptic junctions between electrodes, exhibits a threshold switching (Vth ∼ 1-2 V) with an ultra-low energy requirement of ∼1.3 fJ per synaptic event. Several configurations of the order of hierarchy in the device architecture are studied comprehensively to identify the importance of the individual metallic components in contributing to the threshold switching and energy-minimization. The emerging potentiation behavior of the conductance (G) profile under electrical stimulation and its permanence beyond are realized over a wide current compliance range of 0.25 to 300 μA, broadly classifying the short- and long-term potentiation grounded on the characteristics of filamentary structures. The scale-free correlation of potentiation in the device hosting metallic filaments of diverse shapes and strengths could provide an ideal platform for understanding and replicating the complex behavior of the brain for neuromorphic computing.

Highly Reliable Performance of Flexible Synaptic Devices Based on PVP-GO QD Nanocomposites Due to the Formation of Directional Filaments

The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the "learning experience". Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.

Controlled Synthesis of Lead-Free Double Perovskite Colloidal Nanocrystals for Nonvolatile Resistive Memory Devices

Although lead-free double perovskites such as Cs2AgBiBr6 have been widely explored, they still remain a daunting challenge for the controlled synthesis of lead-free double perovskite nanocrystals with highly tunable morphology and band structure. Here, we report the controlled synthesis of lead-free double perovskite colloidal nanocrystals including Cs2AgBiBr6 and Cs2AgInxBi1-xBr6 via a facile wet-chemical synthesis method for the fabrication of high-performance nonvolatile resistive memory devices. Cs2AgBiBr6 colloidal nanocrystals with well-defined cuboidal, hexagonal, and triangular morphologies are synthesized through a facile wet-chemical approach by tuning the reaction temperature from 150 to 190 °C. Further incorporating indium into Cs2AgBiBr6 to synthesize alloyed Cs2AgInxBi1-xBr6 nanocrystals not only can induce the indirect-to-direct bandgap transition with enhanced photoluminescence but also can improve its structural stability. After optimizing the active layers and device structure, the fabricated Ag/polymethylene acrylate@Cs2AgIn0.25Bi0.75Br6/ITO resistive memory device exhibits a low power consumption (the operating voltage is ∼0.17 V), excellent cycling stability (>10 000 cycles), and good synaptic property. Our study would enable the facile wet-chemical synthesis of lead-free double perovskite colloidal nanocrystals in a highly controllable manner for the development of high-performance resistive memory devices.

The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks

In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.

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.

Bipolar Resistive Switching in TiO2 Artificial Synapse Mimicking Pavlov's Associative Learning

Memristive devices are among the most emerging electronic elements to realize artificial synapses for neuromorphic computing (NC) applications and have potential to replace the traditional von-Neumann computing architecture in recent times. In this work, pulsed laser deposition-manufactured Ag/TiO₂/Pt memristor devices exhibiting digital and analog switching behavior are considered for NC. The TiO₂ memristor shows excellent performance of digital resistive switching with a memory window of order ∼10³. Furthermore, the analog resistive switching offers multiple conductance levels supporting the development of the bioinspired synapse. A possible mechanism for digital and analog switching behavior in our device is proposed. Remarkably, essential synaptic functions such as pair-pulse facilitation, long-term potentiation (LTP), and long-term depression (LTD) are successfully realized based on the change in conductance through analog memory characteristics. Based on the LTP-LTD, a neural network simulation for the pattern recognition task using the MNIST data set is investigated, which shows a high recognition accuracy of 95.98%. Furthermore, more complex synaptic behavior such as spike-time-dependent plasticity and Pavlovian classical conditioning is successfully emulated for associative learning of the biological brain. This work enriches the TiO₂-based resistive random-access memory, which provides information about the simultaneous existence of digital and analog behavior, thereby facilitating the further implementation of memristors in low-power NC.

Second-order associative memory circuit hardware implemented by the evolution from battery-like capacitance to resistive switching memory

Memristor-based Pavlov associative memory circuit presented today only realizes the simple condition reflex process. The secondary condition reflex endows the simple condition reflex process with more bionic, but it is only demonstrated in design and involves the large number of redundant circuits. A FeOx-based memristor exhibits an evolution process from battery-like capacitance (BLC) state to resistive switching (RS) memory as the I-V sweeping increase. The BLC is triggered by the active metal ion and hydroxide ion originated from water molecule splitting at different interfaces, while the RS memory behavior is dominated by the diffusion and migration of ion in the FeOx switching function layer. The evolution processes share the nearly same biophysical mechanism with the second-order conditioning. It enables a hardware-implemented second-order associative memory circuit to be feasible and simple. This work provides a novel path to realize the associative memory circuit with the second-order conditioning at hardware level.

High Performance Full-Inorganic Flexible Memristor with Combined Resistance-Switching

Flexible memristors hold great promise for flexible electronics applications but are still lacking of good electrical performance together with mechanical flexibility. Herein, we demonstrate a full-inorganic nanoscale flexible memristor by using free-standing ductile α-Ag2S films as both a flexible substrate and a functional electrolyte. The device accesses dense multiple-level nonvolatile states with a record high 106 ON/OFF ratio. This exceptional memristor performance is induced by sequential processes of Schottky barrier modification at the contact interface and filament formation inside the electrolyte. In addition, it is crucial to ensure that the cathode junction, where Ag+ is reduced to Ag, dominates the total resistance and takes the most of setting bias before the filament formation. Our study provides a comprehensive insight into the resistance-switching mechanism in conductive-bridging memristors and offers a new strategy toward high performance flexible memristors.

'Stateful' threshold switching for neuromorphic learning

Memristors have promising prospects in developing neuromorphic chips that parallel the brain-level power efficiency and brain-like computational functions. However, the limited available ON/OFF states and high switching voltage in conventional resistive switching (RS) constrain its practical and flexible implementations to emulate biological synaptic functions with low power consumption. We present 'stateful' threshold switching (TS) within the millivoltage range depending on the resistive states of RS, which originates from the charging/discharging parasitic elements of a memristive circuit. Fundamental neuromorphic learning can be facilely implemented based on a single memristor by utilizing four resistive states in 'stateful' TS. Besides the metaplasticity of synaptic learning-forgetting behaviors, multifunctional associative learning, involving acquisition, extinction, recovery, generalization and protective inhibition, was realized with nonpolar operation and power consumption of 5.71 pW. The featured 'stateful' TS with flexible tunability, enriched states, and ultralow operating voltage will provide new directions toward a massive storage unit and bio-inspired neuromorphic system.

Low energy consumption fiber-type memristor array with integrated sensing-memory

The increasing growth of electronic information science and technology has triggered the renaissance of the artificial sensory nervous system (SNS), which can emulate the response of organisms towards external stimuli with high efficiency. In traditional SNS, the sensor units and the memory units are separated, and therefore difficult to miniaturize and integrate. Here, we have incorporated the sensor unit and the memory unit into one system, taking advantage of the unique properties of the ion-gel system. Meanwhile, the weaving-type memory array presents paramount advantages of integration and miniaturization and conformal lamination to curved surfaces. It is worth noting that the electrical double layer (EDL) within the ion gel endow the device with a low operation voltage (<1 V) to achieve low energy consumption. Finally, according to the relationship of pressure stimuli and electrical behavior, the integrated responsiveness-storage external stimuli ability is achieved. Our work offers a new platform for designing cutting edge SNS.

Enhanced Memristive Performance of Double Perovskite Cs2 AgBiBr6-x Clx Devices by Chloride Doping

Mixed halide perovskites are promising memristive materials because of their excellent electronic-ionic properties. In this work, lead-free Cs₂ AgBiBr_6-x Cl_x (x=0, 0.2, 0.4, 0.6, 0.8, 1.0) double perovskite films were fabricated using a one-step solution spin-coating method in air. Moreover, the ITO/Cs₂ AgBiBr_6-x Cl_x /Al sandwich-like devices are fabricated to investigate the memristive behaviors. The present memristors exhibit nonvolatile and bipolar resistive switching behaviors without electroforming process. Interestingly, as the chloride content increases, the ON/OFF ratio of the device increases from 10³ to 10⁴ , the average SET voltage and the RESET voltage decrease from -0.40 V to -0.21 V and from 1.55 V to 1.34 V, respectively. In addition, resistance states of devices can be maintained after 100 switching cycles and 10⁴  s of reading. This study provides new possibility for the development of low-power and environmentally friendly memristors.

Perovskite photodetectors and their application in artificial photonic synapses

Organic-inorganic hybrid perovskites exhibit superior optoelectrical properties and have been widely used in photodetectors. Perovskite photodetectors with excellent detectivity have great potential for developing artificial photonic synapses which can merge data transmission and storage. They are highly desired for next generation neuromorphic computing. The recent progress of perovskite photodetectors and their application in artificial photonic synapses are summarized in this review. Firstly, the key performance parameters of photodetectors are briefly introduced. Secondly, the recent research progress of photodetectors including photoconductors, photodiodes, and phototransistors is summarized. Finally, the applications of perovskite photodetectors in artificial photonic synapses in recent years are highlighted. All these demonstrate the great potential of perovskite photonic synapses for the development of artificial intelligence.

Bifunctional homologous alkali-metal artificial synapse with regenerative ability and mechanism imitation of voltage-gated ion channels

As a key component responsible for information processing in the brain, the development of a bionic synapse possessing digital and analog bifunctionality is vital for the hardware implementation of a neuro-system. Here, inspired by the key role of sodium and potassium in synaptic transmission, the alkali metal element lithium (Li) belonging to the same family is adopted in designing a bifunctional artificial synapse. The incorporation of Li endows the electronic devices with versatile synaptic functions. An artificial neural network based on experimental data exhibits a high performance approaching near-ideal accuracy. In addition, the regenerative ability allows synaptic functional recovery through low-frequency stimuli to be emulated, facilitating the prevention of permanent damage due to intensive neural activities and ensuring the long-term stability of the entire neural system. What is more striking for an Li-based bionic synapse is that it can not only emulate a biological synapse at a behavioral level but realize mechanism emulation based on artificial voltage-gated "ion channels". Concurrent digital and analog features lead to versatile synaptic functions in Li-doped artificial synapses, which operate in a mode similar to the human brain with its two hemispheres excelling at processing imaginative and analytical information, respectively.

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

A Multi-functional Memristive Pavlov Associative Memory Circuit Based on Neural Mechanisms

Pavlov conditioning is a typical associative memory, which involves associative learning between the gustatory and auditory cortex, known as Pavlov associative memory. Inspired by neural mechanisms and biological phenomena of Pavlov associative memory, this paper proposes a multi-functional memristive Pavlov associative memory circuit. In addition to learning and forgetting, whose rates change with the number of associative learning times, the circuit also achieves other innovative functions. First, consolidation learning, which refers to the continued learning process after acquiring associative memory, changes the rates of learning and forgetting. Secondly, the natural forgetting rate tends to zero when the associative memory has been acquired several times, which means the formation of long-term memory. Thirdly, the generalization and differentiation of associative memory caused by similar stimuli are realized through a simplified memristive feedforward neural network. Besides, this circuit implements the associative learning function of interval stimuli through a simpler structure, which refers to "the longer the stimuli interval, the slower the learning rate". The above functions are realized by the time interval module, variable rates module, and generalization and differentiation module. It has been shown that the proposed circuit has good robustness, and can reduce the influence of parasitic capacitance, memristive conductance drift, and input noise on circuit functions. Through further research, this circuit is expected to be used in robot platforms to realize human-like perception and associative cognitive functions.

Light-Emitting Memristors for Optoelectronic Artificial Efferent Nerve

The central nervous system sends a neural impulse through an efferent nerve system toward muscles to drive movement. In an electronically artificial neural system, the electronic neural devices and interconnections prevent achieving highly connected and long-distance artificial impulse transmission and exhibit a narrow bandwidth. Here we design and demonstrate light-emitting memristors (LEMs) for the realization of an optoelectronic artificial efferent nerve, in which the LEM combines the functions of a light receiver, a light emitter, and an optoelectronic synapse in a single device. The optical signal from the pre-LEM (presynaptic membrane) acts as the input signal for the post-LEM (postsynaptic membrane), leading to one-to-many transmission, dynamic adjustable transmission, and light-trained synaptic plasticity, thus removing the physical limitation in artificially electronic neural systems. Furthermore, we construct an optoelectronic artificial efferent nerve with LEMs to control manipulators intelligently. These results promote the construction of an artificial optoelectronic nerve for further development of sensorimotor functionalities.

Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Associative learning, a critical learning principle to improve an individual's adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

A Monochloro Copper Phthalocyanine Memristor with High-Temperature Resilience for Electronic Synapse Applications

Memristors are considered to be one of the most promising device concepts for neuromorphic computing, in particular thanks to their highly tunable resistive states. To realize neuromorphic computing architectures, the assembly of large memristive crossbar arrays is necessary, but is often accompanied by severe heat dispassion. Organic materials can be tailored with on-demand electronic properties in the context of neuromorphic applications. However, such materials are more susceptible to heat, and detrimental effects such as thermally induced degradation directly lead to failure of device operation. Here, an organic memristive synapse formed of monochloro copper phthalocyanine, which remains operational and capable of memristive switching at temperatures as high as 300 °C in ambient air without any encapsulation, is demonstrated. The change in the electrical conductance is found to be a result of ion movement, closely resembling what takes place in biological neurons. Furthermore, the high viability of this approach is showcased by demonstrating flexible memristors with stable switching behaviors after repeated mechanical bending as well as organic synapses capable of emulating a trainable and reconfigurable memristor array for image information processing. The results set a precedent for thermally resilient organic synapses to impact organic neuromorphic devices in progressing their practicality.

Vertical 0D-Perovskite/2D-MoS2 van der Waals Heterojunction Phototransistor for Emulating Photoelectric-Synergistically Classical Pavlovian Conditioning and Neural Coding Dynamics

Optoelectronic-neuromorphic transistors are vital for next-generation nanoscale brain-like computational systems. However, the hardware implementation of optoelectronic-neuromorphic devices, which are based on conventional transistor architecture, faces serious challenges with respect to the synchronous processing of photoelectric information. This is because mono-semiconductor material cannot absorb adequate light to ensure efficient light-matter interactions. In this work, a novel neuromorphic-photoelectric device of vertical van der Waals heterojunction phototransistors based on a colloidal 0D-CsPbBr₃ -quantum-dots/2D-MoS₂ heterojunction channel is proposed using a polymer ion gel electrolyte as the gate dielectric. A highly efficient photocarrier transport interface is established by introducing colloidal perovskite quantum dots with excellent light absorption capabilities on the 2D-layered MoS₂ semiconductor with strong carrier transport abilities. The device exhibits not only high photoresponsivity but also fundamental synaptic characteristics, such as excitatory postsynaptic current, paired-pulse facilitation, dynamic temporal filter, and light-tunable synaptic plasticity. More importantly, efficiency-adjustable photoelectronic Pavlovian conditioning and photoelectronic hybrid neuronal coding behaviors can be successfully implemented using the optical and electrical synergy approach. The results suggest that the proposed device has potential for applications associated with next-generation brain-like photoelectronic human-computer interactions and cognitive systems.

NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks

Artificial Intelligence (AI) at the edge has become a hot subject of the recent technology-minded publications. The challenges related to IoT nodes gave rise to research on efficient hardware-based accelerators. In this context, analog memristor devices are crucial elements to efficiently perform the multiply-and-add (MAD) operations found in many AI algorithms. This is due to the ability of memristor devices to perform in-memory-computing (IMC) in a way that mimics the synapses in human brain. Here, we present a novel planar analog memristor, namely NeuroMem, that includes a partially reduced Graphene Oxide (prGO) thin film. The analog and non-volatile resistance switching of NeuroMem enable tuning it to any value within the R_ON and R_OFF range. These two features make NeuroMem a potential candidate for emerging IMC applications such as inference engine for AI systems. Moreover, the prGO thin film of the memristor is patterned on a flexible substrate of Cyclic Olefin Copolymer (COC) using standard microfabrication techniques. This provides new opportunities for simple, flexible, and cost-effective fabrication of solution-based Graphene-based memristors. In addition to providing detailed electrical characterization of the device, a crossbar of the technology has been fabricated to demonstrate its ability to implement IMC for MAD operations targeting fully connected layer of Artificial Neural Network. This work is the first to report on the great potential of this technology for AI inference application especially for edge devices.

Viewpoint: Pavlovian Materials-Functional Biomimetics Inspired by Classical Conditioning

Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli-responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as "Pavlovian materials" are described, whose stimuli-responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus-triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli-responsive and shape-memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever-more intelligent functions are expected.

Binary Electronic Synapses for Integrating Digital and Neuromorphic Computation in a Single Physical Platform

As a promising advanced computation technology, the integration of digital computation with neuromorphic computation into a single physical platform holds the advantage of a precise, deterministic, fast data process as well as the advantage of a flexible, paralleled, fault-tolerant data process. Even though two-terminal memristive devices have been respectively proved as leading electronic elements for digital computation and neuromorphic computation, it is difficult to steadily maintain both sudden-state-change and gradual-state-change in a single device due to the entirely different operating mechanisms. In this work, we developed a digital-analog compatible memristive device, namely, binary electronic synapse, through realizing controllable cation drift in a memristive layer. The devices feature nonvolatile binary memory as well as artificial neuromorphic plasticity with high operation endurance. With strong nonlinearity in switching dynamics, binary switching, neuromorphic plasticity, two-dimension information store, and trainable memory can be implemented by a single device.

Resistive switching of silicon-silver thin film devices in flexible substrates

Novel applications for memory devices demand nanoscale flexible structures. In particular, resistive switching (RS) devices are promising candidates for wearable and implantable technologies. Here, the Pt/Si/Ag/TiW metal-insulator-metal structure was fabricated and characterized on top of flexible substrates using a straightforward microfabrication process. We also showed that these substrates are compatible with sputtering deposition. RS was successfully achieved using both commercial cellulose cleanroom paper and bacterial cellulose, and polymer (PET) substrates. The bipolar switching behavior was observed for both flat and bent (under a radius of 3.5 mm) configurations. The observed phenomenon was explained by the formation/rupture of metallic Ag filaments in the otherwise insulating Si host layer.

Self-Powered Tactile Sensor with Learning and Memory

Fabrication of human-like intelligent tactile sensors is an intriguing challenge for developing human-machine interfaces. As inspired by somatosensory signal generation and neuroplasticity-based signal processing, intelligent neuromorphic tactile sensors with learning and memory based on the principle of a triboelectric nanogenerator are demonstrated. The tactile sensors can actively produce signals with various amplitudes on the basis of the history of pressure stimulations because of their capacity to mimic neuromorphic functions of synaptic potentiation and memory. The time over which these tactile sensors can retain the memorized information is alterable, enabling cascaded devices to have a multilevel forgetting process and to memorize a rich amount of information. Furthermore, smart fingers by using the tactile sensors are constructed to record a rich amount of information related to the fingers' current actions and previous actions. This intelligent active tactile sensor can be used as a functional element for artificial intelligence.

Associative Learning by Classical Conditioning in Liquid Crystal Network Actuators

Responsive and shape-memory materials allow stimuli-driven switching between fixed states. However, their behavior remains unchanged under repeated stimuli exposure, i.e., their properties do not evolve. By contrast, biological materials allow learning in response to past experiences. Classical conditioning is an elementary form of associative learning, which inspires us to explore simplified routes even for inanimate materials to respond to new, initially neutral stimuli. Here, we demonstrate that soft actuators composed of thermoresponsive liquid crystal networks "learn" to respond to light upon a conditioning process where light is associated with heating. We apply the concept to soft microrobotics, demonstrating a locomotive system that "learns to walk" under periodic light stimulus, and gripping devices able to "recognize" irradiation colors. We anticipate that actuators that algorithmically emulate elementary aspects of associative learning and whose sensitivity to new stimuli can be conditioned depending on past experiences may provide new routes toward adaptive, autonomous soft microrobotics.

Highly flexible and stable resistive switching devices based on WS2 nanosheets:poly(methylmethacrylate) nanocomposites

This paper reports data for the electrical characteristics and the operating mechanisms of flexible resistive switching devices based on WS₂ nanosheets (NSs) dispersed in a poly(methyl methacrylate) (PMMA) layer. The ON/OFF ratio of the memristive device based on an Al/WS₂ NSs:PMMA/indium tin oxides (ITO) structure was approximately 5.9 × 10⁴. The memristive device based on the WS₂ NSs also exhibited the bipolar switching characteristics with low power consumption and great performance in the bent state with radii of the curvatures of 20 and 10 mm. Especially, the results obtained after bending the device were similar to those observed before bending. The device showed nearly the same ON/OFF ratio for a retention time of 1 × 10⁴ sec, and the number of endurance cycles was greater than 1 × 10². The set voltage and the reset voltage probability distributions for the setting and the resetting processes indicated bipolar switching characteristics. The operating and the carrier transport mechanisms of the Al/WS₂ NSs:PMMA/ITO device could be explained based on the current-voltage results with the aid of an energy band diagram.

Facile Synthesis of Ti3C2Tx-Poly(vinylpyrrolidone) Nanocomposites for Nonvolatile Memory Devices with Low Switching Voltage

MXenes, an emerging class of two-dimensional (2D) transition-metal carbide materials, have received increasing attention for their interesting physiochemical properties. For not only MXenes but also other 2D materials, delamination is a requisite step for the exploitation of their unique properties. In this work, a facile method for exfoliating Ti₃C₂T_x MXene to nanosheets of small size with the aid of poly(vinylpyrrolidone) (PVP) is designed, which has never been reported to our knowledge. Since both hydrophobic methylene groups and hydrophilic amide groups are provided with PVP, this method is applicable in a wide range of solvents, such as ethanol, water, and chloroform. Considering the charge detrapping and trapping behavior of 2D transition-metal materials in PVP dielectric, a memory device with the configuration of reduced graphene oxide (rGO)/Ti₃C₂T_x-PVP/Au is directly fabricated with these well-dispersed Ti₃C₂T_x-PVP composites by the solution process technique. Interestingly, the resultant device exhibits a typical bistable electrical switching, ultralow switching voltage (∼0.9 V), and a nonvolatile rewritable memory effect with the function of flash. This work might pave the way of using MXenes for future data storage, which is an indispensable field nowadays.

Programmable responsive hydrogels inspired by classical conditioning algorithm

Living systems have inspired research on non-biological dynamic materials and systems chemistry to mimic specific complex biological functions. Upon pursuing ever more complex life-inspired non-biological systems, mimicking even the most elementary aspects of learning is a grand challenge. We demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning, i.e., conditioning, which is among the simplest forms of learning. Algorithmically, associative learning minimally requires responsivity to two different stimuli and a memory element. Herein, nanoparticles form the memory element, where a photoacid-driven pH-change leads to their chain-like assembly with a modified spectral behaviour. On associating selected light irradiation with heating, the gel starts to melt upon the irradiation, originally a neutral stimulus. A logic diagram describes such an evolution of the material response. Coupled chemical reactions drive the system out-of-equilibrium, allowing forgetting and memory recovery. The findings encourage to search non-biological materials towards associative and dynamic properties.

Living systems inspired research on systems chemistry to mimic specific complex biological functions, but mimicking even the most elementary aspects of learning is a grand challenge. Here the authors demonstrate a programmable hydrogel-based model system, whose behaviour is inspired by associative learning.

Flexible organic synaptic device based on poly (methyl methacrylate):CdSe/CdZnS quantum-dot nanocomposites

A synaptic device that functionally mimics a biological synapse is a promising candidate for use as an electronic element in a neuromorphic system. In this study, flexible electronic synaptic devices based on poly (methyl methacrylate) (PMMA):CdSe/CdZnS core-shell quantum-dot (QD) nanocomposites are demonstrated. The current-voltage characteristics for the synaptic devices under consecutive voltage sweeps show clockwise hysteresis, which is a critical feature of an artificial synaptic device. The effect of the CdSe/CdZnS QD concentration on the device performance is studied. The flexible electronic synaptic devices under bending show the similar and stable electrical performances. The memory retention measurements show that the e-synapse exhibits long-term potentiation and depression. The carrier transport mechanisms are analyzed, and thermionic emission and space-charge-limited-current conduction are found to be dominant.

Flexible, transparent patterned electrodes based on graphene oxide/silver nanowire nanocomposites fabricated utilizing an accelerated ultraviolet/ozone process to control silver nanowire degradation

We developed flexible, transparent patterned electrodes, which were fabricated utilizing accelerated ultraviolet/ozone (UV/O₃)-treated graphene oxide (GO)/silver nanowire (Ag-NW) nanocomposites via a simple, low-cost pattern process to investigate the feasibility of promising applications in flexible/wearable electronic and optoelectronic devices. The UV/O₃ process of the GO/Ag-NW electrode was accelerated by the pre-heat treatment, and the degradation interruption of Ag NWs was removed by the GO treatment. After the deposition of the GO-treated Ag NW electrodes, the sheet resistance of the thermally annealed GO-treated Ag-NW electrodes was significantly increased by using the UV/O₃ treatment, resulting in a deterioration of the GO-treated Ag NWs in areas exposed to the UV/O₃ treatment. The degradation of the Ag NWs caused by the UV/O₃ treatment was confirmed by using the sheet resistances, scanning electron microscopy images, X-ray photoelectron microscopy spectra, and transmittance spectra. While the sheet resistance of the low-density Ag-NW electrode was considerably increased due to the pre-thermal treatment at 90 °C for 10 min, that of the high-density Ag-NW electrode did not vary significantly even after a UV/O₃ treatment for a long time. The degradation interference phenomenon caused by the UV/O₃ treatment in the high-density Ag NWs could be removed by using a GO treatment, which resulted in the formation of a Ag-NW electrode pattern suitable for promising applications in flexible organic light-emitting devices. The GO treatment decreased the sheet resistance of the Ag-NW electrode and enabled the pattern to be formed by using the UV/O₃ treatment. The selective degradation of Ag NWs due to UV/O₃ treatment decreased the transparency of the Ag-NW electrode by about 8% and significantly increased its sheet resistance more than 100 times.

One-Step Fabrication of Bio-Compatible Coordination Complex Film on Diverse Substrates for Ternary Flexible Memory

Recently, resistance random access memories (RRAMs) have been studied extensively, because the demand for information storage is increasing. However, it remains challenging to obtain a flexible device because the active materials involved need to be nontoxic, nonpolluting, distortion-tolerable, and biodegradable as well adhesive to diverse flexible substrates. In this paper, tannic acid (TA) and an iron ion (Fe^III ) coordination complex were employed as the active layer in a sandwich-like (Al/active layer/substrate) device to achieve memory performance. A nontoxic, biocompatible TA-Fe^III coordination complex was synthesized by a one-step self-assembly solution method. The retention time of the TA-Fe^III memory performance was up to 15 000 s, the yield up to 53 %. Furthermore, the TA-Fe^III coordination complex can form a high-quality film and shows stable ternary memory behavior on various flexible substrates, such as polyethylene terephthalate (PET), polyimide (PI), printer paper, and leaf. The device can be degraded by immersing it in vinegar solution. Our work will broaden the application of organic coordination complexes in flexible memory devices with diverse substrates.

A Photoelectric-Stimulated MoS2 Transistor for Neuromorphic Engineering

The von Neumann bottleneck has spawned the rapid expansion of neuromorphic engineering and brain-like networks. Synapses serve as bridges for information transmission and connection in the biological nervous system. The direct implementation of neural networks may depend on novel materials and devices that mimic natural neuronal and synaptic behavior. By exploiting the interfacial effects between MoS₂ and AlOx, we demonstrate that an h-BN-encapsulated MoS₂ artificial synapse transistor can mimic the basic synaptic behaviors, including EPSC, PPF, LTP, and LTD. Efficient optoelectronic spikes enable simulation of synaptic gain, frequency, and weight plasticity. The Pavlov classical conditioning experiment was successfully simulated by electrical tuning, showing associated learning behavior. In addition, h-BN encapsulation effectively improves the environmental time stability of our devices. Our h-BN-encapsulated MoS₂ artificial synapse provides a new paradigm for hardware implementation of neuromorphic engineering.

Flexible memristive devices based on polyimide:mica nanosheet nanocomposites with an embedded PEDOT:PSS layer

Flexible memristive devices with a structure of Al/polyimide:mica/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate/indium-tin-oxide/polyethylene glycol naphthalate showed electrical bistability characteristics. The maximum current margin of the devices with mica nanosheets was much larger than that of the devices without mica nanosheets. For these devices, the current vs. time curves showed nonvolatile characteristics with a retention time of more than 1 × 10⁴ s, and the current vs. number-of-cycles curves demonstrated an endurance for high resistance state/low resistance state switchings of 1 × 10² cycles. As to the operation performance, the “reset” voltage was distributed between 2.5 and 3 V, and the “set” voltage was distributed between −0.7 and −0.5 V, indicative of high uniformity. The electrical characteristics of the devices after full bendings with various radii of curvature were similar to those before bending, which was indicative of devices having ultra-flexibility. The carrier transport and the operation mechanisms of the devices were explained based on the current vs. voltage curves and the energy band diagrams.

Highly-stable write-once-read-many-times switching behaviors of 1D-1R memristive devices based on graphene quantum dot nanocomposites

One diode and one resistor (1D–1R) memristive devices based on inorganic Schottky diodes and poly(methylsilsesquioxane) (PMSSQ):graphene quantum dot (GQD) hybrid nanocomposites were fabricated to achieve stable memory characteristics. Current-voltage (I-V) curves for the Al/PMSSQ:GQDs/Al/p-Si/Al devices at room temperature exhibited write-once, read-many-times memory (WORM) characteristics with an ON/OFF ratio of as large as 10⁴ resulting from the formation of a 1D–1R structure. I-V characteristics of the WORM 1D–1R device demonstrated that the memory and the diode behaviors of the 1D–1R device functioned simultaneously. The retention time of the WORM 1D–1R devices could be maintained at a value larger than 10⁴ s under ambient conditions. The operating mechanisms of the devices were analyzed on the basis of the I–V results and with the aid of the energy band diagram.

Capacitive neural network with neuro-transistors

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with “leaky integrate-and-fire” dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.

Though memristors can potentially emulate neuron and synapse functionality, useful signal energy is lost to Joule heating. Here, the authors demonstrate neuro-transistors with a pseudo-memcapacitive gate that actively process signals via energy-efficient capacitively-coupled neural networks.

Flexible Neuromorphic Architectures Based on Self-Supported Multiterminal Organic Transistors

Because of the fast expansion of artificial intelligence, development and applications of neuromorphic systems attract extensive interest. In this paper, a highly interconnected neuromorphic architecture (HINA) based on flexible self-supported multiterminal organic transistors is proposed. Au electrodes, poly(3-hexylthiophene) active channels, and ion-conducting membranes were combined to fabricate organic neuromorphic devices. Especially, freestanding ion-conducting membranes were used as gate dielectrics as well as support substrates. Basic neuromorphic behavior and four forms of spike-timing-dependent plasticity were emulated. The fabricated neuromorphic device showed excellent electrical stability and mechanical flexibility after 1000 bends. Most importantly, the device structure is interconnected in a way similar to the neural architecture of the human brain and realizes not only the structure of the multigate but also characteristics of the global gate. Dynamic processes of memorizing and forgetting were incorporated into the global gate matrix simulation. Pavlov's learning rule was also simulated by taking advantage of the multigate array. Realization of HINAs would open a new path for flexible and sophisticated neural networks.

Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS₂ ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.

Flexible Memristive Devices Based on InP/ZnSe/ZnS Core-Multishell Quantum Dot Nanocomposites

The effects of the ZnS shell layer on the memory performances of flexible memristive devices based on quantum dots (QDs) with an InP/ZnSe/ZnS core-multishell structure embedded in a poly(methylmethacrylate) layer were investigated. The on/off ratios of the devices based on QDs with an InP/ZnSe core-shell structure and with an InP/ZnSe/ZnS core-multishell structure were approximately 4.2 × 10² and 8.5 × 10³, respectively, indicative of enhanced charge storage capability in the latter. After bending, the memory characteristics of the memristive devices based on QDs with the InP/ZnSe/ZnS structure were similar to those before bending. In addition, those devices maintained the same on/off ratios for retention time of 1 × 10⁴ s, and the number of endurance cycles was above 1 × 10². The reset voltages ranged from -2.3 to -3.1 V, and the set voltages ranged from 1.3 to 2.1 V, indicative of reliable electrical characteristics. Furthermore, the possible operating mechanisms of the devices are presented on the basis of the electron trapping and release mode.