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Self-ordered polymeric Lewis acid–base adduct formation for a dual role of passivated CsPbBr3–Cs4PbBr6 perovskite grain morphology modulation and exciton dielectric confinement, thereby exhibiting high performances and stability in PeLEDs.

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors. The perovskite memristor exhibits 10000 conductance states with a modulation range of more than 4 orders of magnitude. The obtained tens of thousands of finely spaced conductance states with a near-ideal analog property provide a sufficiently large dynamic range and enough intermediate states, which were further applied as a reservoir to map the feature information on different sequential inputs in an analog way. The computing capability of the image classification task of a Fashion-MNIST data set with a high recognition accuracy of up to 90.1% shows that the excellent analog and short-term properties of our perovskite memristor allow the hardware implementation of neuromorphic computing with a reduced training cost.

Memory and synaptic devices based on three IDTT-based copolymers were investigated.

The most recent significant concerns of 2D materials for the synthesis, yield, and mechanism behind device-to-device variability, reliability, and durability benchmarking under memristor characteristics have been discussed.

The innovative exploration of the floating body effect in a 2D WSe2 dual-gate transistor for the potential implementation of single-transistor capacitor-less dynamic random access memory.

The promising roadmap for boosting the performance of TMD FETs, circuitry applications, perspective of the computing scheme and challenges of current integration technologies have been reviewed.

The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics. Here, a planar flexible floating-gate EDLT including an excellent linear/symmetric weight update, a large number (800) of conductance states, repetitive switching endurance (100 cycles), and low variation in weight update is reported. After 800 signal stimulations, it is found that the nonlinearity values of LTP are between 0.20 and 0.85, those of LTD fall between 0.66 and 1.55, the symmetricity values are between 120.7 and 639.8, and the dynamic range is between 150 and 352 nS. The study of 8 × 8 flexible floating-gate EDLT arrays shows that the average deviation and standard deviation between the experimental and theoretical values are 1.36 and 1.93, respectively, indicating that the conductance regulation in the array has a relatively small deviation. The different bending angles and the mechanical stability of the floating-gate EDLT are also studied, which exhibit the excellent bending properties. Furthermore, we studied the recognition of MNIST handwritten digit images by a three-layer perceptron artificial neural network with the experimental weight update, and the maximal recognition accuracy is up to 87.8%.

Two-dimensional (2D) metal oxides have aroused increasing attention in the field of electronics and optoelectronics due to their intriguing physical properties. In this review paper, an overview of recent advances on synthesis of 2D metal oxides and their electronic applications is presented. First, the tunable physical properties of 2D metal oxides that related to the structure (various oxidation-state forms, polymorphism, etc.), crystalline and defects (anisotropy, point defects, and grain boundary), and thickness (quantum confinement effect, interfacial effect, etc.) are discussed. Then, advanced synthesis method for 2D metal oxides besides mechanical exfoliation are introduced and classified into solution process, vapor phase deposition, and native oxidation on metal source. Later, the various roles of 2D metal oxides in widespread applications, i.e., transistors, inverters, photodetectors, piezotronics, memristors, and potential applications (solar cell, spintronics and superconducting devices) are discussed. Finally, the outlook of existing challenges and future opportunities in 2D metal oxides are proposed. This article is protected by copyright. All rights reserved.

Members of the 2D group VA semiconductors (phosphorene, arsenene, antimonene, and bismuthine) present a new class of 2D materials, which are recently gaining a lot of research interest. These materials possess layered morphology, tunable direct bandgap, high charge carrier mobility, high stability, unique in-plane anisotropy, and negative Poisson's ratio. They prepare the ground for novel and multifunctional applications in electronics, optoelectronics, and batteries. The most recent analytical and empirical developments in the fundamental characteristics, fabrication techniques, and potential implementation of 2D group VA materials in this review, along with presenting insights and concerns for the field's future are analyzed.

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI3 solar cell directly stacking on the CsPbBr2I memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules. Furthermore, plasticity of 3 × 3 flexible crossbar array of self-powered memristors has been successfully modulated based on generalized BCM learning rule for optical-encoded pattern recognition. Finally, we implemented artificial striate cortex with binocularity and orientation selectivity based on two simulated 9 × 9 self-powered memristors networks. The emulation of striate cortex with binocular and orientation selectivity will facilitate the brisk edge and corner detection for machine vision in the future applications.

Although remarkable improvement has been achieved in stretchable strain sensors, challenges still exist in aspects including intelligent sensing, simultaneous data processing, and scalable fabrication techniques. In this work, a strain-sensitive device is presented by fabricating a CsPbBr3 quantum dots (QDs) floating-gate field-effect transistor (FET) sensing array on thin polyimide (PI) films. The FET exhibits an excellent on/off ratio (>103) and a large memory window (>2 V). With the introduction of CsPbBr3 QDs as the trapping layer, an additional UV response is obtained because of the photogenerated charge carriers that significantly enhance the source-drain current (IDS) of the device. At each electrical state, the IDS varies with the strains and the sensing range is from compressive +12.5% to tensile -10.8%. Excellent data retainability and mechanical durability demonstrate the high quality and reliability of the fabricated sensors. Furthermore, synapse functions including long-term potentiation (LTP), long-term depression (LTD), etc., are emulated at the device level. Linearity factor changes of LTP/LTD in different sensing scenarios demonstrate the reliability of the device and further confirm the different sensing mechanisms with/without UV illumination. Our results exhibit the potential of transistor-based devices for multifunctional intelligent sensing.

It is of great significance to develop multifunction-integrated photoelectric synaptic devices with the simple structure to imitate the functions of eyes. In this work, we reported the multimodal photoelectric neuromorphic thin film transistors (TFTs) with single gate and two input terminals using sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) mixed with lead-free perovskite (CsBi3I10) and lightly n-doped silicon as active layers and gate electrodes. The results showed that optical and electrical responses of SWCNT TFTs could increase 26 and 23 times after addition of CsBi3I10 under the same pulse sequence stimulation, respectively. Apart from emulating a series of typical synaptic functionalities, an artificial neural network based on SWCNT phototransistors was simulated to be trained for the recognition of handwritten digits in the Modified National Institute of Standards and Technology with the maximum accuracy of about 85.46%. Moreover, our synaptic devices can imitate traditional Pavlovian conditioning, and show NOR logic, reproducible flash memory functions under the optical and electrical programming stimulations. It is noted that the erasing voltage (3 V, 0.2 s) is one of the best value among the reported phototransistor memory. Furthermore, our synaptic devices can work well after more than 150 programming/erasing operation cycles and over 40 days testing.

Summary Memristive devices offer desirable voltage-regulated conductance switching and promise to address zettabyte storage challenges in the big-data era. Generally, most of these reported devices use amorphous solids, where the structural and compositional inhomogeneity is regarded as the origin of stochastic variability. Self-assembling peptide crystals with solution-processed fabrication, controllable morphologies, and structural stability are therefore a promising candidate to address the reliability issues. Here we report a planar diffusive memristor that possesses reliable switching characteristics based on a quasi-one-dimensional crystallized material: diphenylalanine (FF) microrod (MR). This element offers a preferential ion migration path, confining conductive filaments in a defined crystalline surface and therefore reducing programming stochasticity. Ion transport confinement along FF MR was observed via in situ electrostatic force microscopy technique. Additionally, FF MR memristor with high switching uniformity and reproducible relaxation dynamic provides an ideal hardware platform for reliable nociceptor emulation and hardware identification of four-bit decimal numbers.

The traditional von Neumann structure computers cannot meet the demands of high-speed big data processing; therefore, neuromorphic computing has received a lot of interest in recent years. Brain-inspired neuromorphic computing has the advantages of low power consumption, high speed and high accuracy. In human brains, the data transmission and processing are realized through synapses. Artificial synaptic devices can be adopted to mimic the biological synaptic functionalities. Nanowire (NW) is an important building block for nanoelectronics and optoelectronics, and many efforts have been made to promote the application of NW-based synaptic devices for neuromorphic computing. Here, we will introduce the current progress of NW-based synaptic memristors and synaptic transistors. The applications of NW-based synaptic devices for neuromorphic computing will be discussed. The challenges faced by NW-based synaptic devices will be proposed. We hope this perspective will be beneficial for the application of NW-based synaptic devices in neuromorphic systems.

Nonvolatile memory is distinguished for the application in many electronic products due to its excellent charge storage ability. Nevertheless, as the device dimensions are scaled down, floating gate memory encounters various challenges: the increasing leakage current leading to a serious reliability issue and the decreasing of charge density. Hence, metal nanoparticle‐based floating gate memory has been proposed and become a promising candidate for nonvolatile memories due to its outstanding operation speed, excellent scalability, and favorable reliability. This review briefly introduces the classification of memory devices. The operation mechanisms, fabrication and characterization of metal nanoparticle‐based floating gate memory are discussed based on research activities reported in recent years.

This article provides a review of current development and challenges in memristor modeling. We review the mechanisms of memristive devices based on various classifications and survey the progress of memristive models and simulations. Except for classical models in theory, different modeling architectures are compared, including first-principle, modular dynamics and the finite element tools like COMSOL and MATLAB. Challenges and strategies for memristors with non-ideal mechanisms, including large parameter variations, modeling algorithms and simulation roadblocks are also discussed.

Cellulose nanocrystals (CNCs) are the most commonly used natural polymers for biomaterial synthesis. However, their low dispersibility, conductivity, and poor compatibility with the hydrophobic matrix hinder their potential applications. Therefore, we grafted sulfate half-ester and carboxylic functional groups onto CNC surfaces (S-CNC and C-CNC) to overcome these shortcomings. The effect of the dopants, surfactant ratios, and properties of CNCs on the thermal stability, conductivity, and surface morphology of polyaniline (PANI)-doped CNC nanocomposites were investigated through emulsion and in situ polymerization. The higher electrical conductivity and well-dispersed morphology of SCNC–PANI30 (1.1 × 10−2 S cm−1) but lower thermal stability than that of CCNC–PANI30 (T0: 189 °C) nanocomposites are highly related to dispersibility of S-CNCs. However, after 4-dodecylbenzenesulfonic acid (DBSA) was added, the conductivity and thermal stability of SCNC/PANI increased up to 2.5 × 10−1 S cm−1 and 192 °C with almost no particle aggregation because of the increase in charge dispersion. The proposed biodegradable, renewable, and surface-modified S-CNC and C-CNC can be used in high-thermal-stability applications such as food packaging, optical films, reinforcement fillers, flexible semiconductors, and electromagnetic materials.

Recently, mainstream silicon (Si)-based materials and complementary metal oxide semiconductor (CMOS) technology have been used in developing extremely tiny sized (of a few nanometers) devices. However, with the reduction of transistor characteristic dimensions, many new challenges such as the short channel effect and high heat dissipation problems have emerged. Two-dimensional transition metal dichalcogenides (2D TMDs) are deemed the most promising semiconductor materials to conquer the challenge of the short channel effect owing to their excellent properties, including high mobility and atomic thickness. Nevertheless, Fermi-level pinning (FLP) occurs when TMDs are in direct contact with metal electrodes, which causes an uncontrollable Schottky barrier and a high contact resistance, limiting the device performance. In this review, we summarize the recent progress on how to circumvent FLP between 2D TMDs semiconductors and metals. Firstly, the related concepts, aiming to get an in-depth understanding of FLP are introduced. Secondly, we discuss the factors contributing to FLP in detail and the strategies of Fermi-level depinning according to these factors. Finally, we present a summary and outlook, which will provide a guideline for suppressing FLP in the process of fabricating high-performance 2D TMD devices.

Resistive random access memories (RRAMs) based on the electrochemical metallization mechanism (ECM) have potential applications in high-density data storage and efficient neuromorphic computing. However, the high variability of ECM devices still hinders their application in artificial intelligence owing to the random formation of conductive filaments (CFs). Here, we demonstrate 2D covalent organic framework (COF) RRAM with electroforming-free resistive switching behavior, low spatial/temporal variations, and excellent retention capability up to 105 s. The one-dimensional channels of the oriented COF-5 film can not only confine the shape of filaments but also modulate the transition direction of Ag ions. Moreover, alcohol vapors could activate the device to achieve gas-mediated multilevel resistive switching since COF materials can absorb small molecules through host guest interactions to vary the conductivity. An alcohol gas recognition system constructed by integrating the COF RRAM as a sensor and filter part with the k-nearest neighbors (KNN) algorithm as a classifier was demonstrated with a recognition accuracy of 87.2%. Furthermore, the effect of alcohol inhibition stimulation in the human nervous system is successfully emulated by the COF RRAM.

The saturation of Moore's law and the finality of Dennard scaling highlight the need for new data-storage approaches employing different physical mechanisms. Due to the low operation voltage, multibit storage and cost-effective manufacturability, memristive devices are considered to be one of the most attractive alternatives to conventional silicon-based memory. Typically, memristive devices can be switched to a low resistance state or a high resistance state by electrical SET/RESET operations, respectively. Recent years have witnessed a surge of research interest in optoelectronic memories, where tunable switching characteristics, coexistence of volatile and non-volatile behaviours, multilevel programming capability and programming/erasing steps can be achieved optically. In this work we introduce an electro-photoactive memristive device based on natural melanin with favorable electrical and optical properties. The device shows reproducible bipolar resistive switching with a low SET voltage (0.2 V) and an ON/OFF ratio over 104. The SET voltage can be easily tuned from 0.2 V to 1.2 V by varying the wavelength or the intensity of the optical input. Due to this phototunable switching characteristic, light-driven switchable NAND and NOR operations have been realized on a melanin-based circuit.

With the miniaturization of silicon-based electronic components, power consumption is becoming a fundamental issue for micro-nano electronic circuits. The main reason for this is that the scaling of the supply voltage in the ultra-large-scale integrated circuit cannot keep up with the shrinking of the characteristic size of conventional transistors due to the physical limit termed "Boltzmann Tyranny", in which a gate voltage of at least 60 mV is required to modulate the drain current by one order of magnitude. Accordingly, to solve this problem, several new transistor architectures have been designed to reduce the subthreshold swing (SS) to lower than the fundamental limitation, thus lowering the supply voltage and reducing the power consumption. In this review, we first analytically formulate the SS, summarize the methods for reducing the SS, and propose four new transistor concepts, including tunnelling field-effect transistor, negative capacitance field-effect transistor, impact ionization field-effect transistor, and cold source field-effect transistor. Then, we review their physical mechanisms and optimization methods and consider the potential and drawbacks of these four new transistors. Finally, we discuss the challenges encountered in the investigation of these steep-slope transistors and present the future outlook.

Resistive random access memory (RRAM) based on hybrid organic–inorganic halide perovskite (HOIP) materials has recently gained significant interest due to its low activation energy of ion migration. HOIP RRAM has been reported to exhibit outstanding performance, but they still suffer from issues of high operating voltage and poor uniformity. Here, we provide an efficient method for improving the resistive switching characteristics of HOIP RRAM by integrating an Au tip in the bottom electrode to regulate the growth of conductive filaments (CFs). The Ag/polymethylmethacrylate/CH3NH3PbI3/Au tip/indium tin oxide RRAM exhibits a lower set voltage of −0.11 V, a larger ON/OFF ratio of 108, excellent durability (up to 4 × 103 times), low power consumption, and good non-volatility. Moreover, the time-dependent resistive switching characteristics were investigated by a statistical analysis of the data on the turn-on time based on the Weibull distribution. Tests using transmission electron microscopy, electron energy depletion spectroscopy, and energy dispersive spectroscopy have demonstrated that the formation and rupture of CFs occur mainly in the CH3NH3PbI3 layer and that the CFs should be composed of Ag atoms and I vacancies. Our research offers a strategy for enhancing the stability of perovskite devices and promoting the application of low-power, high-density non-volatile memory devices.

Neuromorphic computing inspired by the functions of human brain has attracted much attention in the research area of artificial intelligence. Herein, we report an optoelectronic synapse based on a polyvinylpyrrolidone (PVPy)-upconverting nanoparticle (UCNP) hybrid floating gate transistor. The UCNP-based synaptic transistor exhibits a remarkable near-infrared (NIR)-induced de-trapping behaviour as well as a long retention time. Furthermore, our device is employed to successfully emulate various synaptic functions including postsynaptic current with different duration times and pulse amplitudes, paired-pulse facilitation/depression, transition from short-term plasticity to long-term plasticity and learning-forgetting-relearning processes. Overall, this work may open up a new opportunity for NIR optoelectronic synapses.

The floating body effect in Meta-Stable-Dip RAM (MSDRAM) has been broadly employed in implementing single-transistor capacitor-less (1T0C) dynamic random access memory (DRAM) cells to break through the limitation of finite size reduction of peripheral capacitors. However, the majority of them were broadly demonstrated in conventional CMOS technology, while emerging semiconductor systems are rarely explored. Here, we creatively explore exfoliated multilayer tungsten diselenide (WSe

As the power density of the electronics is on increasing, improving the heat dissipation performance of electronic packaging materials will play a positive role in promoting the performance of modern electronics. In this work, the three-dimensional carbon fiber (CF)-MXenes foam, in which the vertically aligned CF constructed the heat transport paths, were prepared by simple freeze-drying method. As a result, the thermal conductivity (TC) of CF-M/epoxy composites was improved to an ultra-high level (9.68 W/mK) at 30.2 wt% hybrid fillers, increasing by 4509% enhancement compared with that of neat epoxy. In addition, the thermal properties of composites, such as glass transition temperature (Tg), coefficient of thermal expansion (CTE) were investigated. All the results indicated that the CF-MXenes/epoxy composite was supposed to be a promising heat dissipation material and will be used in the field of electronic packaging.

The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin-film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Spatiotemporal learning and inference in parallel mode is the key advantage for neuromorphic computing, which mimics the human brain architecture. Here, a memtransistor was fabricated with a self-assembled layer of 3-aminopropyltriethoxysilane (APTES) to mimic the synaptic behavior. The aggregates due to the hydrogen bonding between APTES molecules during the self-assembled process could form a defect in the interface. In the memtransistor, the holes could be captured effectively through not only the gate electrode, but also the drain electrode, resulting in controlled carrier concentration in the channel. An obvious hysteresis window was found by applying the operation voltage to the drain electrode. To study the competitive and the cooperative behavior, a multi-terminal memtransistor was prepared, and these synaptic behaviors could be successfully imitated. Finally, an artificial neural network based on the weight updating method was simulated to discriminate the numbers, and the total accuracy reached 96.42%. The results demonstrated that the APTES-based memtransistor had potential applications in synaptic electronics.

Antimonene, which is similar to two-dimensional (2D) phosphorene, has recently gained considerable attention because of its thickness-dependent energy band structure. However, unlike phosphorene, undoped antimonene has an indirect bandgap only at the monolayer limit. In this work, an electrochemical sodium doping strategy was proposed to tune the energy band structure of antimonene. First-principles calculations indicated that a direct bandgap of 0.88 eV formed in 5.55% Na-doped antimonene, while undoped antimonene had an indirect bandgap of 2.38 eV. Optical and electrical measurements provided clear evidence for such a reconstruction of the energy band. We experimentally demonstrated p-type conduction in antimonene quantum dots (QDs)-based field-effect transistors. Furthermore, the induced direct bandgap enabled electric-field control of the surface-enhanced Raman scattering on plasmonic-free antimonene QDs. This allowed for detection of rhodamine 6G with a detection limit down to the sub-femtomolar level. Our study highlights the potential of doped antimonene as an emerging 2D semiconductor.

With the rapid application of artificial intelligence in daily life and work, the traditional von Neumann architecture device faces the limitation of scalability and high energy consumption. These limitations can be overcome by in-memory computing based on analog resistance switch devices, but the resistive switching behavior depends on the formation and rupture of filaments with spatial and temporal variation. According to the filamentary switching mechanisms, conductive filaments play an irreplaceable role in the resistive switching process, and the stochastic filaments are the main cause of nonuniform performances and variation. Therefore, an efficient way to solve these problems is by controlling the filaments. In recent years, researchers have made many efforts to control the filaments, resulting in numerous feasible methods being invented. Herein, departing from the filamentary mechanisms, the strategies of filament control are discussed from the aspects of electrode optimization, switching layer optimization and channel design. Meanwhile, the challenges of promotion in device performance and application in neuromorphic computing and outlook for future research directions are also discussed.

Inexpensive and flexible organic resistive memory can be easily fabricated, scaled and stacked. Owing to these advantages, organic resistive memory is considered as a very promising technology capable of substituting current inorganic semiconductor-based memory technology. It has attracted a lot of attention from the scientific and engineering communities. In this mini-review, the recent state-of-the-art developments related to organic small molecules for resistive random-access memory (RRAM) have been emphasized. We first describe the general characteristics of RRAM devices, their typical structures, fabrication, classification, and switching mechanisms. Next, we discuss the usage of RRAM devices in next-generation computing. In particular, we discuss the use of organic small molecules and their composites in RRAM with various functionalities. Finally, the current challenges and developing trends of performance improvement for organic small molecule-based RRAM are discussed.

Memristor devices have been of great interest over the past decade for the implementation of brain-inspired memory and computing owing to their inherent multistage memory, exquisite structure and higher integration. Herein, a CsPbBr3 QD-based protocol was constructed to exhibit analog memristive characteristics. The field-driven charge trapping/detrapping process was accelerated in the highlighted asymmetric electrode configuration to enable persistent dual direction current modulation, which serves as a basis for synaptic weight variation in the human brain. Significantly, the synaptic functions of long-term potentiation (LTP), long-term depression (LTD) and spike-timing-dependent plasticity (STDP) have been emulated on the device level. Furthermore, light signal-facilitated paired-pulse facilitation (PPF) behavior was validated with in situ atomic force microscopy (AFM) based on electrical techniques. These results may pave a new way to produce memristive devices with advanced implications for future neuromorphic computing.

Memristors that possess massive memory capacity and outstanding processing efficiency concurrently are vitally important to surmount the limitation of Moore's law and traditional Von Neumann bottleneck. Specifically, memristors are capable of integrating various functionalities such as information storage and processing into just one unit to obtain high electrical performance and thereby satisfy the contemporary needs. Here, a multifunctional polymer memristor with combined data storage and processing abilities on the basis of novel solution-processed poly(9,9-dioctyl-9H-fluorene)-alt-1,3-bis(2-ethylhexyl)-(5,7-di(thiophen-2-yl)-4H,8H-benzo[1,2-c:4,5-c′]dithiophene-4,8-dione)-alt-((2,4-pentanedionato)bis(2-(thiophen-2-yl)-pyridine)iridium) (PFTBDD-IrTPy) is demonstrated for the first time. As confirmed by field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX) as well as density functional theory (DFT) calculations, the memristive behavior of the multifunctional device is attributed to the synergetic electrochemical metallization and charge transfer effect. Associated with these cation regulating and charge transfer characteristics, our polymer memristor is capable of achieving diverse functionalities comprising multilevel data storage, biological synaptic emulation, Boolean logic function as well as basic arithmetic operations and commutative law of addition, subtraction, multiplication and division. These experimental results will accelerate the development of metal complex polymer based memristors for various electronic applications.

The switching variability caused by intrinsic stochasticity of the ionic/atomic motions during the conductive filaments (CFs) formation process largely limits the applications of diffusive memristors (DMs), including artificial neurons, neuromorphic computing and artificial sensory systems. In this study, a DM device with improved device uniformity based on well-crystallized two-dimensional (2D) h-BN, which can restrict the CFs formation from three to two dimensions due to the high migration barrier of Ag