Investigation and Manipulation of Different Analog Behaviors of Memristor as Electronic Synapse for Neuromorphic Applications

Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles (GeO2 NP) embedded within a poly(methyl methacrylate) (PMMA) thin film. Besides exhibiting forming-free resistive switching and an uncommon "ON" state in pristine conditions, the hybrid (nanocomposite) devices demonstrate a unique form of mixed-mode switching. The observed stopping voltage-dependent switching enables state-of-the-art bifunctional synaptic behavior with short-term (volatile/temporal) and long-term (nonvolatile/nontemporal) modes that are switchable depending on the stopping voltage applied. The short-term memory mode device is demonstrated to further emulate important synaptic functions such as short-term potentiation (STP), short-term depression (STD), paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-voltage-dependent plasticity (SVDP), spike-duration-dependent plasticity (SDDP), and, more importantly, the "learning-forgetting-rehearsal" behavior. The long-term memory mode gives additional long-term potentiation (LTP) and long-term depression (LTD) characteristics for long-term plasticity applications. The work shows a unique coexistence of the two resistive switching modes, providing greater flexibility in device design for future adaptive and reconfigurable neuromorphic computing systems at the hardware level.

Optically Mediated Nonvolatile Resistive Memory Device Based on Metal-Organic Frameworks

Metal-organic frameworks (MOFs), characterized by tunable porosity, high surface area, and diverse chemical compositions, offer unique prospects for applications in optoelectronic devices. However, the prevailing research on thin-film devices utilizing MOFs has predominantly focused on aspects such as information storage and photosensitivity, often neglecting the integration of the advantages inherent in both photonics and electronics to enhance optical memory. This work demonstrates a light-mediated resistive memory device based on a highly oriented porphyrin-based MOFs film, in which the resistance state of the memristor is modulated by light, realizing the integration of the perception and storage of optical information. The memristor shows excellent performance with a wide light range of 405-785 nm and a persistent photoconductivity phenomenon up to 8.3 × 103 s. Further mechanistic studies have revealed that the resistive switching effect in the memristor is primarily associated with the reversible formation and annihilation of Ag conductive filaments.

A memristive circuit for self-organized network topology formation based on guided axon growth

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on.

α-Fe2O3-based artificial synaptic RRAM device for pattern recognition using artificial neural networks

We report on theα-Fe₂O₃-based artificial synaptic resistive random access memory device, which is a promising candidate for artificial neural networks (ANN) to recognize the images. The device consists of a structure Ag/α-Fe₂O₃/FTO and exhibits non-volatility with analog resistive switching characteristics. We successfully demonstrated synaptic learning rules such as long-term potentiation, long-term depression, and spike time-dependent plasticity. In addition, we also presented off-chip training to obtain good accuracy by backpropagation algorithm considering the synaptic weights obtained fromα-Fe₂O₃based artificial synaptic device. The proposedα-Fe₂O₃-based device was tested with the FMNIST and MNIST datasets and obtained a high pattern recognition accuracy of 88.06% and 97.6% test accuracy respectively. Such a high pattern recognition accuracy is attributed to the combination of the synaptic device performance as well as the novel weight mapping strategy used in the present work. Therefore, the ideal device characteristics and high ANN performance showed that the fabricated device can be useful for practical ANN implementation.

Compact SPICE Model of Memristor with Barrier Modulated Considering Short- and Long-Term Memory Characteristics by IGZO Oxygen Content

This paper introduces a compact SPICE model of a two-terminal memory with a Pd/Ti/IGZO/p⁺-Si structure. In this paper, short- and long-term components are systematically separated and applied in each model. Such separations are conducted by the applied bias and oxygen flow rate (OFR) during indium gallium zinc oxide (IGZO) deposition. The short- and long-term components in the potentiation and depression curves are modeled by considering the process (OFR of IGZO) and bias conditions. The compact SPICE model with the physical mechanism of SiO₂ modulation is introduced, which can be useful for optimizing the specification of memristor devices.

Review on the Basic Circuit Elements and Memristor Interpretation: Analysis, Technology and Applications

Circuit or electronic components are useful elements allowing the realization of different circuit functionalities. The resistor, capacitor and inductor represent the three commonly known basic passive circuit elements owing to their fundamental nature relating them to the four circuit variables, namely voltage, magnetic flux, current and electric charge. The memory resistor (or memristor) was claimed to be the fourth basic passive circuit element, complementing the resistor, capacitor and inductor. This paper presents a review on the four basic passive circuit elements. After a brief recall on the first three known basic passive circuit elements, a thorough description of the memristor follows. Memristor sparks interest in the scientific community due to its interesting features, for example nano-scalability, memory capability, conductance modulation, connection flexibility and compatibility with CMOS technology, etc. These features among many others are currently in high demand on an industrial scale. For this reason, thousands of memristor-based applications are reported. Hence, the paper presents an in-depth overview of the philosophical argumentations of memristor, technologies and applications.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

Graphene/SnS2 van der Waals Photodetector with High Photoresponsivity and High Photodetectivity for Broadband 365-2240 nm Detection

The fabrication of graphene/SnS₂ van der Waals photodetectors and their photoelectrical properties are systematically investigated. It was found that a dry transferred graphene/SnS₂ van der Waals heterostructure had a broadband sensing range from ultraviolet (365 nm) to near-infrared (2.24 μm) and respective improved responsivities and photodetectivities of 7.7 × 10³ A/W and 8.9 × 10¹³ jones at 470 nm and 2 A/W and 1.8 × 10¹⁰ jones at 1064 nm. Moreover, positive and negative photoconductance effects were observed when the photodetectors were illuminated by photon sources with energies greater and smaller than the bandgap of SnS₂, respectively. The photoresponsivity (R) versus incident power density (P) follows the empirical law R ∝ P_in^β, with β > -1 for positive photoconductance effects and β < -1 for negative photoconductance effects. On the basis of the Fowler-Nordheim tunneling model and a Poisson and drift-diffusion simulation, we show quantitatively that the barrier height and barrier width of the heterostructure photodetector could be controlled by a laser and an external electrical field through a photogating effect generated by carriers trapped at the interface, which could be used to tune the separation and transport of photogenerated carriers. Our results may be useful for the design of high performance van der Waals heterojunction photodetectors.

Bioresistive Random-Access Memory with Gold Nanoparticles that Generate the Coulomb Blocking Effect Can Realize Multilevel Data Storage and Synapse Simulation

Gold nanoparticles (Au NPs) have good biocompatibility and special quantum effects. In this Letter, we embedded Au NPs into silkworm hemolymph (SH) to improve the performance of the device and fabricated Al/SH:Au NPs/indium tin oxide (ITO)/glass resistive random access memory. The device exhibits a bipolar switching behavior with a retention time of 10⁴ s. Compared with the Al/SH/ITO device without Au NPs, the device has a higher ON/OFF current ratio (>10⁵) and a smaller V_reset. The improvement in device performance is attributed to the fact that Au NPs act as the electron-trapping center in the device; a Coulomb blockade occurs after electrons are trapped, thereby increasing the resistance of the device in the high-resistance state. Using optimized devices can realize multilevel data storage and can also simulate synaptic characteristics such as potentiation and depression. The device is expected to be applied to high-density, low-cost, degradable, and biocompatible storage devices and neuromorphic computing in the future.

Nonideal resistive and synaptic characteristics in Ag/ZnO/TiN device for neuromorphic system

Ideal resistive switching in resistive random-access memory (RRAM) should be ensured for synaptic devices in neuromorphic systems. We used an Ag/ZnO/TiN RRAM structure to investigate the effects of nonideal resistive switching, such as an unstable high-resistance state (HRS), negative set (N-set), and temporal disconnection, during the set process and the conductance saturation feature for synaptic applications. The device shows an I–V curve based on the positive set in the bipolar resistive switching mode. In 1000 endurance tests, we investigated the changes in the HRS, which displays large fluctuations compared with the stable low-resistance state, and the negative effect on the performance of the device resulting from such an instability. The impact of the N-set, which originates from the negative voltage on the top electrode, was studied through the process of intentional N-set through the repetition of 10 ON/OFF cycles. The Ag/ZnO/TiN device showed saturation characteristics in conductance modulation according to the magnitude of the applied pulse. Therefore, potentiation or depression was performed via consecutive pulses with diverse amplitudes. We also studied the spontaneous conductance decay in the saturation feature required to emulate short-term plasticity.

Oxygen vacancy engineering of TaOx-based resistive memories by Zr doping for improved variability and synaptic behavior

Resistive switching (RS) devices are promising forms of non-volatile memory. However, one of the biggest challenges for RS memory applications is the device-to-device (D2D) variability, which is related to the intrinsic stochastic formation and configuration of oxygen vacancy (V_O) conductive filaments (CFs). In order to reduce the D2D variability, control over the formation and configuration of oxygen vacancies is paramount. In this study, we report on the Zr doping of TaO_x-based RS devices prepared by pulsed-laser deposition as an efficient means of reducing the V_Oformation energy and increasing the confinement of CFs, thus reducing D2D variability. Our findings were supported by XPS, spectroscopic ellipsometry and electronic transport analysis. Zr-doped films showed increased V_Oconcentration and more localized V_Os, due to the interaction with Zr. DC and pulse mode electrical characterization showed that the D2D variability was decreased by a factor of seven, the resistance window was doubled, and a more gradual and monotonic long-term potentiation/depression in pulse switching was achieved in forming-free Zr:TaO_xdevices, thus displaying promising performance for artificial synapse applications.

Fabrication and investigation of quaternary Ag-In-Zn-S quantum dots-based memristors with ultralow power and multiple resistive switching behaviors

Quaternary Ag-In-Zn-S (AIZS) quantum dots (QDs) play critical roles in various applications since they have advantages of combining superior optical and electrical features, such as tunable fluorescence emission and high carrier mobilities. However, the application of semiconductor AIZS QDs in brain-inspired devices (e.g. memristor) has been rarely reported. In this work, the tunable volatile threshold switching (TS) and non-volatile memory switching (MS) behaviors have been obtained in a memristor composed of AIZS QDs by regulating the magnitude of compliance current. Additionally, the innovative Ag/AIZS structure devices without traditional oxide layer exhibit low operation voltage (∼0.25 V) and programming current (100 nA) under the TS mode. Moreover, the devices achieve reproducible bipolar resistive switching (RS) behaviors with large ON/OFF ratio of ∼10⁵, ultralow power consumption of ∼10^-10 W, and good device-to-device uniformity under the MS mode. Furthermore, the charge transport mechanisms of the high- and low-resistance states under the positive and negative bias have been analyzed with space-charge-limited-current and filament conduction models, respectively. This work not only validates the potential of AIZS QDs acting as dielectric layer in RS devices but also provides a new guideline for designing ultralow power and multiple RS characteristics devices.

Graphene oxide based synaptic memristor device for neuromorphic computing

Brain-inspired neuromorphic computing which consist neurons and synapses, with an ability to perform complex information processing has unfolded a new paradigm of computing to overcome the von Neumann bottleneck. Electronic synaptic memristor devices which can compete with the biological synapses are indeed significant for neuromorphic computing. In this work, we demonstrate our efforts to develop and realize the graphene oxide (GO) based memristor device as a synaptic device, which mimic as a biological synapse. Indeed, this device exhibits the essential synaptic learning behavior including analog memory characteristics, potentiation and depression. Furthermore, spike-timing-dependent-plasticity learning rule is mimicked by engineering the pre- and post-synaptic spikes. In addition, non-volatile properties such as endurance, retentivity, multilevel switching of the device are explored. These results suggest that Ag/GO/fluorine-doped tin oxide memristor device would indeed be a potential candidate for future neuromorphic computing applications.

Improving the Recognition Accuracy of Memristive Neural Networks via Homogenized Analog Type Conductance Quantization

Conductance quantization (QC) phenomena occurring in metal oxide based memristors demonstrate great potential for high-density data storage through multilevel switching, and analog synaptic weight update for effective training of the artificial neural networks. Continuous, linear and symmetrical modulation of the device conductance is a critical issue in QC behavior of memristors. In this contribution, we employ the scanning probe microscope (SPM) assisted electrode engineering strategy to control the ion migration process to construct single conductive filaments in Pt/HfO_x/Pt devices. Upon deliberate tuning and evolution of the filament, 32 half integer quantized conductance states in the 16 G₀ to 0.5 G₀ range with enhanced distribution uniformity was achieved. Simulation results revealed that the numbers of the available QC states and fluctuation of the conductance at each state play an important role in determining the overall performance of the neural networks. The 32-state QC behavior of the hafnium oxide device shows improved recognition accuracy approaching 90% for handwritten digits, based on analog type operation of the multilayer perception (MLP) neural network.

Toward a Reliable Synaptic Simulation Using Al-Doped HfO2 RRAM

The potential in a synaptic simulation for neuromorphic computation has revived the research interest of resistive random access memory (RRAM). However, novel applications require reliable multilevel resistive switching (RS), which still represents a challenge. We demonstrate in this work the achievement of reliable HfO₂-based RRAM devices for synaptic simulation by performing the Al doping and the postdeposition annealing (PDA). Transmission electron microscopy and operando hard X-ray photoelectron spectroscopy results reveal the positive impact of Al doping on the formation of oxygen vacancies. Detailed I-V characterizations demonstrate that the 16.5% Al doping concentration leads to better RS properties of the device. In comparison with the other reported results based on HfO₂ RRAM, our devices with 16.5% Al-doping and PDA at 450 °C show better reliable multilevel RS (∼20 levels) performance and an increased on/off ratio. The 16.5% Al:HfO₂ sample with PDA at 450 °C shows good potentiation/depression characteristics with low pulse width (10 μs) along with a good On/Off ratio (>1000), good data retention at room temperature, and high temperature and good program/erase endurance characteristics with a pulse width of 50 ns. The synapse features including potentiation, depression, and spike time-dependent plasticity were successfully achieved using optimized Al-HfO₂ RRAM devices. Our results demonstrate the beneficial effects of Al doping and PDA on the enhancement of the performances of RRAM devices for the synaptic simulation in neuromorphic computing applications.

Self-Doping Memristors with Equivalently Synaptic Ion Dynamics for Neuromorphic Computing

The accumulation and extrusion of Ca²⁺ ions in the pre- and post-synaptic terminals play crucial roles in initiating short- and long-term plasticity (STP and LTP) in biological synapses, respectively. Mimicking these synaptic behaviors by electronic devices represents a vital step toward realization of neuromorphic computing. However, the majority of reported synaptic devices usually focus on the emulation of qualitatively synaptic behaviors; devices that can truly emulate the physical behavior of the synaptic Ca²⁺ ion dynamics in STP and LTP are rarely reported. In this work, Ag/Ag:Ta₂O₅/Pt self-doping memristors were developed to equivalently emulate the Ca²⁺ ion dynamics of biological synapses. With conductive filaments from double sources, these memristors produced unique double-switching behavior under voltage sweeps and demonstrated several essential synaptic behaviors under pulse stimuli, including STP, LTP, STP to LTP transition, and spike-rate-dependent plasticity. Experimental results and nanoparticle dynamic simulations both showed that Ag atoms from double sources could mimic Ca²⁺ dynamics in the pre- and post-synaptic terminals under stimuli. A perceptron network with an STP to LTP transition layer based on the self-doping memristors was also introduced and evaluated; simulations showed that this network could solve noisy figure recognition tasks efficiently. All of these results indicate that the self-doping memristors are promising components for future hardware creation of neuromorphic systems and emulate the characteristics of the brain.

Vertical MoS2 Double-Layer Memristor with Electrochemical Metallization as an Atomic-Scale Synapse with Switching Thresholds Approaching 100 mV

Atomically thin two-dimensional (2D) materials-such as transition metal dichalcogenide (TMD) monolayers and hexagonal boron nitride (hBN)-and their van der Waals layered preparations have been actively researched to build electronic devices such as field-effect transistors, junction diodes, tunneling devices, and, more recently, memristors. Two-dimensional material memristors built in lateral form, with horizontal placement of electrodes and the 2D material layers, have provided an intriguing window into the motions of ions along the atomically thin layers. On the other hand, 2D material memristors built in vertical form with top and bottom electrodes sandwiching 2D material layers may provide opportunities to explore the extreme of the memristive performance with the atomic-scale interelectrode distance. In particular, they may help push the switching voltages to a lower limit, which is an important pursuit in memristor research in general, given their roles in neuromorphic computing. In fact, recently Akinwande et al. performed a pioneering work to demonstrate a vertical memristor that sandwiches a single MoS₂ monolayer between two inert Au electrodes, but it could neither attain switching voltages below 1 V nor control the switching polarity, obtaining both unipolar and bipolar switching devices. Here, we report a vertical memristor that sandwiches two MoS₂ monolayers between an active Cu top electrode and an inert Au bottom electrode. Cu ions diffuse through the MoS₂ double layers to form atomic-scale filaments. The atomic-scale thickness, combined with the electrochemical metallization, lowers switching voltages down to 0.1-0.2 V, on par with the state of the art. Furthermore, our memristor achieves consistent bipolar and analogue switching, and thus exhibits the synapse-like learning behavior such as the spike-timing dependent plasticity (STDP), the very first STDP demonstration among all 2D-material-based vertical memristors. The demonstrated STDP with low switching voltages is promising not only for low-power neuromorphic computing, but also from the point of view that the voltage range approaches the biological action potentials, opening up a possibility for direct interfacing with mammalian neuronal networks.

Stretchable and conformable synapse memristors for wearable and implantable electronics

Stretchable and conformable synapse memristors that can emulate the behaviour of the biological neural system and well adhere onto the curved surfaces simultaneously are desirable for the development of imperceptible wearable and implantable neuromorphic computing systems. Previous synapse memristors have been mainly limited to rigid substrates. Herein, a stretchable and conformable memristor with fundamental synaptic functions including potentiation/depression characteristics, long/short-term plasticity (STP and LTP), "learning-forgetting-relearning" behaviour, and spike-rate-dependent and spike-amplitude-dependent plasticity is demonstrated based on highly elastic Ag nanoparticle-doped thermoplastic polyurethanes (TPU : Ag NPs) and polydimethylsiloxane (PDMS). The memristor can be well operated even at 60% strain and can be well conformed onto the curved surfaces. The formed conductive filament (CF) obtained from the movement of Ag nanoparticle clusters under the locally enhanced electric field gives rise to resistance switching of our memristor. These results indicate a feasible strategy to realize stretchable and conformable synaptic devices for the development of new-generation artificial intelligence computers.

Highly Anisotropic GeSe Nanosheets for Phototransistors with Ultrahigh Photoresponsivity

2D GeSe possesses black phosphorous-analog-layered structure and shows excellent environmental stability, as well as highly anisotropic in-plane properties. Additionally, its high absorption efficiency in the visible range and high charge carrier mobility render it promising for applications in optoelectronics. However, most reported GeSe-based photodetectors show frustrating performance especially in photoresponsivity. Herein, a 2D GeSe-based phototransistor with an ultrahigh photoresponsivity is demonstrated. Its optimized photoresponsivity can be up to ≈1.6 × 105 A W-1. This high responsivity can be attributed to the highly efficient light absorption and the enhanced photoconductive gain due to the existence of trap states. The exfoliated GeSe nanosheet is confirmed to be along the [001] (armchair direction) and [010] (zigzag direction) using transmission electron microscopy and anisotropic Raman characterizations. The angle-dependent electric and photoresponsive performance is systematically explored. Notably, the GeSe-based phototransistor shows strong polarization-dependent photoresponse with a peak/valley ratio of 1.3. Furthermore, the charge carrier mobility along the armchair direction is measured to be 1.85 times larger than that along the zigzag direction.

Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing

This paper presents aligned carbon nanotube (CNT) synaptic transistors for large-scale neuromorphic computing systems. The synaptic behavior of these devices is achieved via charge-trapping effects, commonly observed in carbon-based nanoelectronics. In this work, charge trapping in the high- k dielectric layer of top-gated CNT field-effect transistors (FETs) enables the gradual analog programmability of the CNT channel conductance with a large dynamic range ( i. e., large on/off ratio). Aligned CNT synaptic devices present significant improvements over conventional memristor technologies ( e. g., RRAM), which suffer from abrupt transitions in the conductance modulation and/or a small dynamic range. Here, we demonstrate exceptional uniformity of aligned CNT FET synaptic behavior, as well as significant robustness and nonvolatility via pulsed experiments, establishing their suitability for neural network implementations. Additionally, this technology is based on a wafer-level technique for constructing highly aligned arrays of CNTs with high semiconducting purity and is fully CMOS compatible, ensuring the practicality of large-scale CNT+CMOS neuromorphic systems. We also demonstrate fine-tunability of the aligned CNT synaptic behavior and discuss its application to adaptive online learning schemes and to homeostatic regulation of artificial neuron firing rates. We simulate the implementation of unsupervised learning for pattern recognition using a spike-timing-dependent-plasticity scheme, indicate system-level performance (as indicated by the recognition accuracy), and demonstrate improvements in the learning rate resulting from tuning the synaptic characteristics of aligned CNT devices.

Full imitation of synaptic metaplasticity based on memristor devices

Neuromorphic engineering is a promising technology for developing new computing systems owing to the low-power operation and the massive parallelism similarity to the human brain. Optimal function of neuronal networks requires interplay between rapid forms of Hebbian plasticity and homeostatic mechanisms that adjust the threshold for plasticity, termed metaplasticity. Metaplasticity has important implications in synapses and is barely addressed in neuromorphic devices. An understanding of metaplasticity might yield new insights into how the modification of synapses is regulated and how information is stored by synapses in the brain. Here, we propose a method to imitate the metaplasticity inhibition of long-term potentiation (MILTP) for the first time based on memristors. In addition, the metaplasticity facilitation of long-term potentiation (MFLTP) and the metaplasticity facilitation of long-term depression (MFLTD) are also achieved. Moreover, the mechanisms of metaplasticity in memristors are discussed. Additionally, the proposed method to mimic the metaplasticity is verified by three different memristor devices including oxide-based resistive memory (OxRAM), interface switching random access memory, and conductive bridging random access memory (CBRAM). This is a further step toward developing fully bio-realistic artificial synapses using memristors. The findings in this study will deepen our understanding of metaplasticity, as well as provide new insight into bio-realistic neuromorphic engineering.

Nonvolatile Bio-Memristor Based on Silkworm Hemolymph Proteins

This paper reports the first successful fabrication of an ITO/silkworm hemolymph/Al bio-memristor using silkworm hemolymph as the active layer. Experiments demonstrate that the silkworm hemolymph bio-memristor is a nonvolatile rewritable bipolar memory device with a current switching ratio exceeding 10³. The state of the bio-memristor can be retained for more than 10⁴ seconds and remains stable for at least 500 cycles. Tests of 1/f noise have shown that the resistance switching characteristics of the silkworm hemolymph bio-memristor are related to the formation and breaking of conductive filaments, which result from the migration of oxygen ions and the oxidation and reduction of metal cations in the silkworm hemolymph film. The naturally non-toxic silkworm hemolymph offers advantages for human health, environmental protection, and biocompatibility. The proposed nonvolatile rewritable bio-memristor based on silkworm hemolymph possesses great application potential.

Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Memristive devices, having a huge potential as artificial synapses for low-power neural networks, have received tremendous attention recently. Despite great achievements in demonstration of plasticity and learning functions, little progress has been made in the repeatable analog resistance states of memristive devices, which is, however, crucial for achieving controllable synaptic behavior. The controllable behavior of synapse is highly desired in building neural networks as it helps reduce training epochs and diminish error probability. Fundamentally, the poor repeatability of analog resistance states is closely associated with the random formation of conductive filaments, which consists of oxygen vacancies. In this work, graphene quantum dots (GQDs) are introduced into memristive devices. By virtue of the abundant oxygen anions released from GQDs, the GQDs can serve as nano oxygen-reservoirs and enhance the localization of filament formation. As a result, analog resistance states with highly tight distribution are achieved with nearly 85% reduction in variations. In addition the insertion of GQDs can alter the energy band alignment and boost the tunneling current, which leads to significant reduction in both switching voltages and their distribution variations. This work may pave the way for achieving artificial neural networks with accurate and efficient learning capability.

Charge Transport in MoS2/WSe2 van der Waals Heterostructure with Tunable Inversion Layer

Despite numerous studies on two-dimensional van der Waals heterostructures, a full understanding of the charge transport and photoinduced current mechanisms in these structures, in particular, associated with charge depletion/inversion layers at the interface remains elusive. Here, we investigate transport properties of a prototype multilayer MoS₂/WSe₂ heterojunction via a tunable charge inversion/depletion layer. A charge inversion layer was constructed at the surface of WSe₂ due to its relatively low doping concentration compared to that of MoS₂, which can be tuned by the back-gate bias. The depletion region was limited within a few nanometers in the MoS₂ side, while charges are fully depleted on the whole WSe₂ side, which are determined by Raman spectroscopy and transport measurements. Charge transport through the heterojunction was influenced by the presence of the inversion layer and involves two regimes of tunneling and recombination. Furthermore, photocurrent measurements clearly revealed recombination and space-charge-limited behaviors, similar to those of the heterostructures built from organic semiconductors. This contributes to research of various other types of heterostructures and can be further applied for electronic and optoelectronic devices.

Optimization of chitosan gated electric double layer transistors by combining nanoparticle incorporation and acid doping

Electric double layer transistors gated by bio-polyelectrolyte chitosan could be optimized by combining nanoparticle incorporation and acid doping.