Quasi-Zero-Dimensional Source/Drain Contact for Fermi-Level Unpinning in a Tungsten Diselenide (WSe2) Transistor: Approaching Schottky-Mott Limit.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) known for their exceptional electrical and optical properties have emerged as promising channel materials for next-generation electronics. However, as strong Fermi-level pinning (FLP) between the metal and the 2D TMDC material at the source/drain (S/D) contact decides the Schottky barrier height (SBH), the transistor polarity is fixed to a certain type, which remains a challenge for the 2D TMDC field-effect transistors (FETs). Here, a S/D contact structure with a quasi-zero-dimensional (quasi-0D) contact interface, in which the dimensionality reduction effect alleviates FLP, was developed to gain controllability over the polarity of the 2D TMDC FET. As a result, conventional metal contacts on the WSe2 FET showed n-type characteristics due to strong FLP (pinning factor of 0.06) near the conduction band, and the proposed quasi-0D contact enabled by the Ag conductive filament on the WSe2 FET exhibited p-type characteristics with a SBH very close to the Schottky-Mott rule (pinning factor of 0.95). Furthermore, modeling of Schottky barriers of conventional contacts, one-dimensional (1D) contacts, and quasi-0D contacts revealed that the SBH of the quasi-0D contact is relatively less subject to interface dipoles that induce FLP, owing to more rapid decaying of dipole energy. The proposed contact in this study provided a method that progressed beyond the alleviation of FLP to achieve controllable polarity. Moreover, reducing the contact dimensionality to quasi-0D will enable high compatibility with the further scaled-down nanoscale device contact structure.

From Solid to Fluid: Novel Approaches in Neuromorphic Engineering.

Neuromorphic engineering is rapidly developing as an approach to mimicking processes in brains using artificial memristors, devices that change conductivity in response to the electrical field (resistive switching effect). Memristor-based neuromorphic systems can overcome the existing problems of slow and energy-inefficient computing that conventional processors face. In the Introduction, the basic principles of memristor operation and its applications are given. The history of switching in sandwich structures and granular metals is reviewed in the Historical Overview. Particular attention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), which demonstrated the first evidence of resistive switching and predicted the filamentary mechanism of switching. Multi-dimensionality in neuromorphic systems: Despite the powerful computational abilities of traditional memristor arrays, they cannot repeat many organizational characteristics of biological neural networks, i.e., their multi-dimensionality. This part reviews the unconventional nanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential for use in reservoir computing due to the unique spiking change in conductance similar to firing in neurons. Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquid state broadens the possibilities for mimicking biological processes. In this section, ionic current memristors are reviewed and, the working principles of which bring us closer to the mechanisms of information transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineering linked to the application of nanofluids for the formation of reconfigurable nanoparticle networks with memristive properties is given in this section. The Conclusion t summarizes the bullet points of the Review and provides an outlook on the future of liquid-state neuromorphic systems.

Antiferroelectric Heterostructures Memristors with Unique Resistive Switching Mechanisms and Properties.

A novel antiferroelectric material, PbSnO3 (PSO), was introduced into a resistive random access memory (RRAM) to reveal its resistive switching (RS) properties. It exhibits outstanding electrical performance with a large memory window (>104), narrow switching voltage distribution (±2 V), and low power consumption. Using high-resolution transmission electron microscopy, we observed the antiferroelectric properties and remanent polarization of the PSO thin films. The in-plane shear strains in the monoclinic PSO layer are attributed to oxygen octahedral tilts, resulting in misfit dislocations and grain boundaries at the PSO/SRO interface. Furthermore, the incoherent grain boundaries between the orthorhombic and monoclinic phases are assumed to be the primary paths of Ag+ filaments. Therefore, the RS behavior is primarily dominated by antiferroelectric polarization and defect mechanisms for the PSO structures. The RS behavior of antiferroelectric heterostructures controlled by switching spontaneous polarization and strain, defects, and surface chemistry reactions can facilitate the development of new antiferroelectric device systems.

Eco-Friendly Biomemristive Nonvolatile Memory: Harnessing Organic Waste for Sustainable Technology.

Organic material-based bioelectronic nonvolatile memory devices have recently received a lot of attention due to their environmental compatibility, simple fabrication recipe, preferred scalability, low cost, low power consumption, and numerous additional advantages. Resistive random-access memory (RRAM) devices work on the principle of resistive switching, which has the potential for applications in memory storage and neuromorphic computing. Here, natural organically grown orange peel was used to extract biocompatible pectin to design a resistive switching-based memory device of the structure Ag/Pectin/Indium tin oxide (ITO), and the behavior was studied between a temperature range of 10K and 300K. The microscopic characterization revealed the texture of the surface and thickness of the layers. The memristive current-voltage characteristics performed over 1000 consecutive cycles of repeated switching revealed sustainable bipolar resistive switching behavior with a high ON/OFF ratio. The underlying principle of Resistive Switching behavior is based on the formation of conductive filaments between the electrodes, which is explained in this work. Further, we have also designed a 2 × 2 crossbar array of RRAM devices to demonstrate various logic circuit operations useful for neuromorphic computing. The robust switching characteristics suggest possible uses of such devices for the design of ecofriendly bioelectronic memory applications and in-memory computing.

Optimization of Bilayer Resistive Random Access Memory Based on Ti/HfO2/ZrO2/Pt.

In this paper, the electrothermal coupling model of metal oxide resistive random access memory (RRAM) is analyzed by using a 2D axisymmetrical structure in COMSOL Multiphysics simulation software. The RRAM structure is a Ti/HfO2/ZrO2/Pt bilayer structure, and the SET and RESET processes of Ti/HfO2/ZrO2/Pt are verified and analyzed. It is found that the width and thickness of CF1 (the conductive filament of the HfO2 layer), CF2 (the conductive filament of the ZrO2 layer), and resistive dielectric layers affect the electrical performance of the device. Under the condition of the width ratio of conductive filament to transition layer (6:14) and the thickness ratio of HfO2 to ZrO2 (7.5:7.5), Ti/HfO2/ZrO2/Pt has stable high and low resistance states. On this basis, the comparison of three commonly used RRAM metal top electrode materials (Ti, Pt, and Al) shows that the resistance switching ratio of the Ti electrode is the highest at about 11.67. Finally, combining the optimal conductive filament size and the optimal top electrode material, the I-V hysteresis loop was obtained, and the switching ratio Roff/Ron = 10.46 was calculated. Therefore, in this paper, a perfect RRAM model is established, the resistance mechanism is explained and analyzed, and the optimal geometrical size and electrode material for the hysteresis characteristics of the Ti/HfO2/ZrO2/Pt structure are found.

Simultaneous monitoring of amoxicillin and paracetamol in synthetic biological fluids using a 3D printed disposable electrode with a lab-made conductive filament.

In this work, we are pleased to present for the first time a 3D-printed electrochemical device using a lab-made conductive filament based on graphite (Gr) and polylactic acid (PLA) polymer matrix for the simultaneous detection of amoxicillin (AMX) and paracetamol (PAR). The sensor was properly characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), and cyclic voltammetry (CV). Compared to the commercial glassy carbon electrode (GCE), the superior performance of the 3D-Gr/PLA electrode was verified with a 3.8-fold more favored charge transfer. A differential pulse voltammetry (DPV) method was proposed providing a linear working range of 4 to 12 µmol L-1 for both analytes and a limit of detection (LOD) of 0.80 and 0.51 µmol L-1 for AMX and PAR, respectively. Additionally, repeatability studies (n = 5, RSD < 5.7%) indicated excellent precision, and recovery percentages ranging from 89 to 109% when applied to synthetic human urine, saliva, and plasma samples, attested to the accuracy of the method. The studies also indicate that the sensor does not suffer significant interference from common substances (antibiotics and biomarkers) present in the biological fluids, which makes it a promising analytical tool considering its low-cost, ease of manufacturing, robustness, and electrochemical performance.

Novel disposable and portable 3D-printed electrochemical apparatus for fast and selective screening of 25E-NBOH in forensic samples.

The advent of new psychoactive substances (NPS) has caused enormous difficulty for legal control since they are rapidly commercialized, and their chemical structures are routinely altered. In this aspect, derivatives phenethylamines, such as 25E-NBOH, have received great attention in the forensic scenario. Hence, we propose portable and cost-effective (U$ 5.00) 3D-printed devices for the electrochemical screening of 25E-NBOH for the first time. The cell and all electrodes were printed using acrylonitrile butadiene styrene filament (insulating material) and conductive filament (graphite embedded in a polylactic acid matrix), respectively, both by the fused deposition modeling (FDM) 3D printing technique. The electrochemical apparatus enables micro-volume analysis (50-2000 µL), especially important for low sample volumes. A mechanistic route for the electrochemical oxidation of 25E-NBOH is proposed based on cyclic voltammetric data, which showed two oxidation processes around +0.75 V and +1.00 V and a redox pair between +0.2 and -0.2 V (vs. graphite ink pseudo-reference). A fast and sensitive square-wave voltammetry method was developed, which exhibited a linear working range from 0.85 to 5.1 µmoL-1, detection limit of 0.2 µmol L-1, and good intra-electrode precision (n = 10, RSD <5.3 %). Inter-electrode measurements (n = 3, RSD <9.8 %) also attested that the electrode production process is reproducible. Interference tests in the presence of other drugs frequently found in blotting paper indicated high selectivity of the electrochemical method for screening of 25E-NBOH. Screening analysis of blotting paper confirmed the presence of 25E-NBOH in the seized samples. Moreover, a recovery percentage close to 100 % was found for a spiked saliva sample, suggesting the method's usefulness for quantitative purposes aimed at information on recent drug use.

Interface engineering in ZnO/CdO hybrid nanocomposites to enhanced resistive switching memory for neuromorphic computing.

Resistive random-access memory (RRAMs) has attracted significant interest for their potential applications in embedded storage and neuromorphic computing. Materials based on metal chalcogenides have emerged as promising candidates for the fulfilment of these requirements. Due to its ability to manipulate electronic states and control trap states through controlled compositional dynamics, metal chalcogenide RRAM has excellent non-volatile resistive memory properties. In the present we have synthesized ZnO-CdO hybrid nanocomposite by using hydrothermal method as an active layer. The Ag/C15ZO/Pt hybrid nanocomposite structure memristors showed electrical properties similar to biological synapses. The device exhibited remarkably stable resistive switching properties that have a low SET/RESET (0.41/-0.2) voltage, a high RON/OFF ratio of approximately 105, a high retention stability, excellent endurance reliability up to 104 cycles and multilevel device storage performance by controlling the compliance current. Furthermore, they exhibited an impressive performance in terms of emulating biological synaptic functions, which include long-term potentiation (LTP), long-term depression (LTD), and paired-pulse facilitation (PPF), via the continuous modulation of conductance. The hybrid nanocomposite memristors notably achieved an impressive recognition accuracy of up to 92.6 % for handwritten digit recognition under artificial neural network (ANN). This study shows that hybrid-nanocomposite memristor performance could lead to efficient future neuromorphic architectures.

Conductive Additive Manufactured Acrylonitrile Butadiene Styrene Filaments: Statistical Approach to Mechanical and Electrical Behaviors.

Additive manufacturing is a process in which digital three-dimensional (3D) design data are used to build a component in layers by accumulating materials. There are many materials used in additive manufacturing technology. The most basic features that distinguish these materials are their strength and electrical behavior. They can be strong or flexible, resistant to abrasion, depending on the application used. Recently, 3D printing filament and polymeric composite materials combined with carbon nanostructures with electrical conductivity have been used. In this study, acrylonitrile butadiene styrene (ABS), a carbon black-filled conductive material with high strength and hardness, was preferred. The aim in this study is to focus on the mechanical and electrical behavior of the material processed in filament form. Fabrication of samples was done using a fused deposition modeling-based printer that controls filament orientation. Different experimental studies were conducted: (1) mechanical tests to determine the maximum tensile strength values of the samples; and (2) electrical tests to analyze the electrical resistances of the samples. In the design of the first experiment, infill volume, layer height, infill type, and printing direction were determined as factors affecting strength. In the design of the second experiment, the length, nozzle temperature, and measurement temperature were determined as the factors affecting the electrical resistance. Statistical analysis of the measured data was performed to evaluate the overall result of the experiments. Finally, a prediction model of real-time tensile strength and resistance values was created using machine learning algorithms. These algorithms are Gaussian Process Regression and Support Vector Machine. The results confirmed the known linear dependence of electrical resistance on the length of the 3D-printed conductive ABS samples and showed how changing the fabrication settings affected the strength values.

Definition of a Localized Conducting Path via Suppressed Charge Injection in Oxide Memristors for Stable Practical Hardware Neural Networks.

Oxide-based memristors have been demonstrated as suitable options for memory components in neuromorphic systems. In such devices, the resistive switching characteristics are caused by the formation of conductive filaments (CFs) comprising oxygen vacancies. Thus, the electrical performance is primarily governed by the CF structure. Despite various approaches for regulating the oxygen vacancy distributions in oxide memristors, controlling the CF structure without modifying the device configuration related to material compatibility is still a challenge. This study demonstrates an effective strategy for localizing CF distributions in memristors by suppressing charge injection during the formation of conducting paths. As the injected charge quantity is reduced in the electroforming process of the oxide memristor, the CF distributions become narrower, leading to more reproducible and stable resistive switching characteristics in the device. Based on these findings, a reliable hardware neural network comprising oxide memristors is constructed to recognize complex images. The developed memristor has been employed as a synaptic memory component in systems without degradation for a long time. This promising concept of oxide memristors acting as stable synaptic components holds great potential for developing practical neuromorphic systems and their expansion into artificial intelligent systems.

Calculation and study for the growth process and electrical characteristics of the conductive filament in nanoscale resistance memory under current-driven mode.

After investigating the behavior of ions during the growth of conductive filaments, we suggested a model for the growth process and electrical characteristics of the conductive filament under current-driven mode. In this model, the ionic displacement equation is derived by Arrhenius law, and a differential equation for the conductive filament growth has been established. We have also proved that the dielectric layer with the leakage current under current-driven mode can be equivalent to a parallel plate capacitor, which has a the equivalent dielectric constant. Consequently, the forming/set time of the device is gotten. At the same time, the kinetics process of ion motion is analyzed in detail, so that many microscopic parameters of the ion motion, such as the height of the potential barrier, the jump step, mobility and diffusion coefficient, can be obtained. Due to divalent and monovalent copper ions all participate in conduction, an equivalent copper ion Cuz+is used for replacing both Cu+and Cu2+, solving the computational complexity problem caused by multivalent metal ions. Finally, an equivalent circuit is proposed to calculate output voltage versus time characteristic. The calculation results of the model are consistent with experimental data.

Memristor-based spiking neural network with online reinforcement learning.

Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure. This paper presents an online reinforcement learning algorithm in which the change of connection weights is carried out after processing each environment state during interaction-with-environment data generation. Another novel feature of the algorithm is that it is applied to SNNs with memristor-based STDP-like learning rules. The plasticity functions are obtained from real memristors based on poly-p-xylylene and CoFeB-LiNbO3 nanocomposite, which were experimentally assembled and analyzed. The SNN is comprised of leaky integrate-and-fire neurons. Environmental states are encoded by the timings of input spikes, and the control action is decoded by the first spike. The proposed learning algorithm solves the Cart-Pole benchmark task successfully. This result could be the first step towards implementing a real-time agent learning procedure in a continuous-time environment that can be run on neuromorphic systems with memristive synapses.

3D-printed electrochemical cells with laser engraving: developing portable electroanalytical devices for forensic applications.

A new electrochemical device fabricated by the combination of 3D printing manufacturing and laser-generated graphene sensors is presented. Cell and electrodes were 3D printed by the fused deposition modeling (FDM) technique employing acrylonitrile butadiene styrene filament (insulating material that composes the cell) and conductive filament (lab-made filament based on graphite dispersed into polylactic acid matrix) to obtain reference and auxiliary electrodes. Infrared-laser engraved graphene, also reported as laser-induced graphene (LIG), was produced by laser conversion of a polyimide substrate, which was assembled in the 3D-printed electrochemical cell that enables the analysis of low volumes (50-2000 µL). XPS analysis revealed the formation of nitrogen-doped graphene multilayers that resulted in excellent electrochemical sensing properties toward the detection of atropine (ATR), a substance that was found in beverages to facilitate sexual assault and other criminal acts. Linear range between 5 and 35 µmol L-1, detection limit of 1 µmol L-1, and adequate precision (RSD = 4.7%, n = 10) were achieved using differential-pulse voltammetry. The method was successfully applied to beverage samples with recovery values ranging from 80 to 105%. Interference studies in the presence of species commonly found in beverages confirmed satisfactory selectivity for ATR sensing. The devices proposed are useful portable analytical tools for on-site applications in the forensic scenario.

Cost-effective protocol to produce 3D-printed electrochemical devices using a 3D pen and lab-made filaments to ciprofloxacin sensing.

A novel conductive filament based on graphite (Gr) dispersed in polylactic acid polymer matrix (PLA) is described to produce 3D-electrochemical devices (Gr/PLA). This conductive filament was used to additively manufacture electrochemical sensors using the 3D pen. Thermogravimetric analysis confirmed that Gr was successfully incorporated into PLA, achieving a composite material (40:60% w/w, Gr and PLA, respectively), while Raman and scanning electron microscopy revealed the presence of defects and a high porosity on the electrode surface, which contributes to improved electrochemical performance. The 3D-printed Gr/PLA electrode provided a more favorable charge transfer (335 Ω) than the conventional glassy carbon (1277 Ω) and 3D-printed Proto-pasta® (3750 Ω) electrodes. As a proof of concept, the ciprofloxacin antibiotic, a species of multiple interest, was selected as a model molecule. Thus, a square wave voltammetry (SWV) method was proposed in the potential range + 0.9 to + 1.3 V (vs Ag|AgCl|KCl(sat)), which provided a wide linear working range (2 to 32 µmol L-1), 1.79 µmol L-1 limit of detection (LOD), suitable precision (RSD < 7.9%), and recovery values from 94 to 109% when applied to pharmaceutical and milk samples. Additionally, the sensor is free from the interference of other antibiotics routinely employed in veterinary practices. This device is disposable, cost-effective, feasibly produced in financially limited laboratories, and consequently promising for evaluation of other antibiotic species in routine applications.

Tuned Transport Path of Perovskite MAPbI3 -based Memristor Structure.

In this study, the features of resistive random access memory (RRAM) employing a straightforward Cr/MAPbI3 /FTO three-layer structure have been examined and clarified. The device displays various resistance switching (RS) behavior at various sweep voltages between 0.5 and 5 V. The RS effect has a conversion in the direction of the SET and RESET processes during sweeping for a number of cycles at a specific voltage. The directional change of the RS processes corresponds to the dominant transition between the generation/recombination of iodide ion and vacancy in the MAPbI3 perovskite layer and the electrochemical metallization of the Cr electrode under the influence of an electric field, which results in the conductive filament (CF) formation/rupture. At each stage, these processes are controlled by specific charge conduction mechanisms, including Ohmic conduction, space-charge-limited conduction (SCLC), and variable-range hopping (VRH). By identifying the biased voltage and the quantity of voltage sweep cycles, one can take a new approach to control or modulate the pathways for effective charge transport. This new approach is made possible by an understanding of the RS characteristics and the corresponding mechanisms causing the variation of RS behavior in the structure.

Fully 3D-Printed Dry EEG Electrodes.

Electroencephalography (EEG) is used to detect brain activity by recording electrical signals across various points on the scalp. Recent technological advancement has allowed brain signals to be monitored continuously through the long-term usage of EEG wearables. However, current EEG electrodes are not able to cater to different anatomical features, lifestyles, and personal preferences, suggesting the need for customisable electrodes. Despite previous efforts to create customisable EEG electrodes through 3D printing, additional processing after printing is often needed to achieve the required electrical properties. Although fabricating EEG electrodes entirely through 3D printing with a conductive material would eliminate the need for further processing, fully 3D-printed EEG electrodes have not been seen in previous studies. In this study, we investigate the feasibility of using a low-cost setup and a conductive filament, Multi3D Electrifi, to 3D print EEG electrodes. Our results show that the contact impedance between the printed electrodes and an artificial phantom scalp is under 550 Ω, with phase change of smaller than -30∘, for all design configurations for frequencies ranging from 20 Hz to 10 kHz. In addition, the difference in contact impedance between electrodes with different numbers of pins is under 200 Ω for all test frequencies. Through a preliminary functional test that monitored the alpha signals (7-13 Hz) of a participant in eye-open and eye-closed states, we show that alpha activity can be identified using the printed electrodes. This work demonstrates that fully 3D-printed electrodes have the capability of acquiring relatively high-quality EEG signals.

Process Parameters and Geometry Effects on Piezoresistivity in Additively Manufactured Polymer Sensors.

The current work experimentally determined how the initial resistance and gauge factor in additively manufactured piezoresistive sensors are affected by the material, design, and process parameters. This was achieved through the tensile testing of sensors manufactured with different infill angles, layer heights, and sensor thicknesses using two conductive polymer composites. Linear regression models were then used to analyze which of the input parameters had significant effects on the sensor properties and which interaction effects existed. The findings demonstrated that the initial resistance in both materials was strongly dependent on the sensor geometry, decreasing as the cross-sectional area was increased. The resistance was also significantly influenced by the layer height and the infill angle, with the best variants achieving a resistance that was, on average, 22.3% to 66.5% lower than less-favorable combinations, depending on the material. The gauge factor was most significantly affected by the infill angle and, depending on the material, by the layer height. Of particular interest was the finding that increasing in the infill angle resulted in an increase in the sensitivity that outweighed the associated increase in the initial resistance, thereby improving the gauge factor by 30.7% to 114.6%, depending on the material.

Stable and reliable IGZO resistive switching device with HfAlOxinterfacial layer.

The performance stability of the resistive switching (RS) is vital for a resistive random-access memory device. Here, by inserting a thin HfAlOxlayer between the InGaZnO (IGZO) layer and the bottom Pt electrode, the RS performance in amorphous IGZO memory device is significantly improved. Comparing with a typical metal-insulator-metal structure, the device with HfAlOxlayer exhibits lower switching voltages, faster switching speeds, lower switching energy and lower power consumption. As well, the uniformity of switching voltage and resistance state is also improved. Furthermore, the device with HfAlOxlayer exhibits long retention time (>104s at 85 °C) , high on/off ratio and more than 103cycles of endurance at atmospheric environment. Those substantial improvements in IGZO memory device are attributed to the interface effects with a HfAlOxinsertion layer. With such layer, the formation and rupture locations of Ag conductive filaments are better regulated and confined, thus an improved performance stability.

Lab-made 3D-printed electrochemical sensors for tetracycline determination.

Antibiotics such as tetracycline (TC) are widely prescribed to treat humans or dairy animals. Therefore, it is important to establish affordable devices in laboratories with minimal infrastructure. 3D printing has proven to be a powerful and cost-effective tool that revolutionizes many applications in electrochemical sensing. In this work, we employ a conductive filament based on graphite (Gr) and polylactic acid (PLA) (40:60; w/w; synthesized in our lab) to manufacture 3D-printed electrodes. This electrode was used "as printed" and coupled to batch injection analysis with amperometric detection (BIA-AD) for TC sensing. Preliminary studies by cyclic voltammetry and differential pulse voltammetry revealed a mass transport governed by adsorption of the species and consequent fouling of the redox products on the 3D printed surface. Thus, a simple strategy (solution stirring and application of successive potentials, +0.95 V followed by +1.2 V) was associated with the BIA-AD system to solve this effect. The proposed electrode showed analytical performance comparable to costly conventional electrodes with linear response ranging from 0.5 to 50 µmol L-1 and a detection limit of 0.19 µmol L-1. Additionally, the developed method was applied to pharmaceutical, tap water, and milk samples, which required minimal sample preparation (simple dilution). Recovery values of 92-117% were obtained for tap water and milk samples, while the content found of TC in the capsule was close to the value reported by the manufacturer. These results indicate the feasibility of the method for routine analysis involving environmental, pharmaceutical, and food samples.

Deterministic Conductive Filament Formation and Evolution for Improved Switching Uniformity in Embedded Metal-Oxide-Based Memristors─A Phase-Field Study.

The extreme device-to-device variation of switching performance is one of the major obstacles preventing the applications of metal-oxide-based memristors in large-scale memory storage and resistive neural networks. Recent experimental works have reported that embedding metal nano-islands (NIs) in metal oxides can effectively improve the uniformity of the memristors, but the underlying role of the NIs is not fully understood. Here, to address this specific problem, we develop a physical model to understand the origin of the variability and how the embedded NIs can improve the performance and uniformity of memristors. We find that due to the dimension confinement effect, embedding metal NIs can modulate the electric field distribution and lead to a more deterministic formation of the conductive filament (CF) from their vicinity, in contrast to the random growth of CFs without embedded NIs. This deterministic CF formation, via vacancy nucleation, further reduces the forming, reset, and set voltages and enhances the uniformity of the operation voltages and current ON/OFF ratios. We further demonstrate that modifying the shapes of the metal NIs can modulate the field strengths/distributions around the NIs and that choosing the NI metal composition and shape that chemically facilitate vacancy formations can further optimize the CF morphology, reduce the operation voltages, and improve the switching performance. Our work thus provides a fundamental understanding of how embedded metal NIs improve the resistive switching performance in oxide-based memristors and could potentially guide the selection of embedded NIs to realize a more uniform memristor that also operates at a higher efficiency than present materials.