Self-Compliance and High Performance Pt/HfOx/Ti RRAM Achieved through Annealing

Memristive Architectures Exploiting Self-Compliance Multilevel Implementation on 1 kb Crossbar Arrays for Online and Offline Learning Neuromorphic Applications

This paper suggests the practical implications of utilizing a high-density crossbar array with self-compliance (SC) at the conductive filament (CF) formation stage. By limiting the excessive growth of CF, SC functions enable the operation of a crossbar array without access transistors. An AlOx/TiOy, internal overshoot limitation structure, allows the SC to have resistive random-access memory. In addition, an overshoot-limited memristor crossbar array makes it possible to implement vector-matrix multiplication (VMM) capability in neuromorphic systems. Furthermore, AlOx/TiOy structure optimization was conducted to reduce overshoot and operation current, verifying uniform bipolar resistive switching behavior and analog switching properties. Additionally, extensive electric pulse stimuli are confirmed, evaluating long-term potentiation (LTP), long-term depression (LTD), and other forms of synaptic plasticity. We found that LTP and LTD characteristics for training an online learning neural network enable MNIST classification accuracies of 92.36%. The SC mode quantized multilevel in offline learning neural networks achieved 95.87%. Finally, the 32 × 32 crossbar array demonstrated spiking neural network-based VMM operations to classify the MNIST image. Consequently, weight programming errors make only a 1.2% point of accuracy drop to software-based neural networks.

Vacancy-Engineered Nickel Ferrite Forming-Free Low-Voltage Resistive Switches for Neuromorphic Circuits

Innovations in resistive switching devices constitute a core objective for the development of ultralow-power computing devices. Forming-free resistive switching is a type of resistive switching that eliminates the need for an initial high voltage for the formation of conductive filaments and offers promising opportunities to overcome the limitations of traditional resistive switching devices. Here, we demonstrate mixed charge state oxygen vacancy-engineered electroforming-free resistive switching in NiFe2O4 (NFO) thin films, fabricated as asymmetric Ti/NFO/Pt heterostructures, for the first time. Using pulsed laser deposition in a controlled oxygen atmosphere, we tune the oxygen vacancies together with the cationic valence state in the nickel ferrite phase, with the latter directly affecting the charge state of the oxygen vacancies. The structural integrity and chemical composition of the films are confirmed by X-ray diffraction and hard X-ray photoelectron spectroscopy, respectively. Electrical transport studies reveal that resistive switching characteristics in the films can be significantly altered by tuning the amount and charge state of the oxygen vacancy concentration during the deposition of the films. The resistive switching mechanism is seen to depend upon the migration of both singly and doubly charged oxygen vacancies formed as a result of changes in the nickel valence state and the consequent formation/rupture of conducting filaments in the switching layer. This is supported by the existence of an optimum oxygen vacancy concentration for efficient low-voltage resistive switching, below or above which the switching process is inhibited. Along with the filamentary switching mechanism, the Ti top electrode also enhances the resistive switching performance due to interfacial effects. Time-resolved measurements on the devices display both long- and short-term potentiation in the optimized vacancy-engineered NFO resistive switches, ideal for solid-state synapses achieved in a single system. Our work on correlated oxide forming-free resistive switches holds significant potential for CMOS-compatible low-power, nonvolatile resistive memory and neuromorphic circuits.

Simulation of the resistance switching performance and synaptic behavior of TiO2-based RRAM devices with CoFe2O4 insertion layers

The electrothermal coupling model of Pt/CoFe2O4/TiO2/TiN devices was established to study their resistive switching characteristics and basic biological synaptic properties in our research. The processes of set and reset are simulated, and the distribution of the temperature, the electric field and the concentration of oxygen vacancies in the dielectric layer are obtained. The switching performance of the TiO2-based device is significantly improved after the CoFe2O4 layer is inserted, with the switching voltage, working current and power consumption being reduced, while the switching ratio is increased. By changing the thermal conductivity of the top electrode, the rupture position of the conductive filament can be controlled. The I-V characteristics of the Pt/CoFe2O4/TiO2/TiN device during the reset and set processes are fitted linearly in logarithmic coordinates, and the ohmic conduction mechanism or the space-limited charge conduction mechanism is mainly satisfied in the high and low resistance states. Finally, the application of dual-layer devices on biological synapses is studied, and the basic biological characteristics of enhancement, inhibition and paired pulse promotion are simulated successfully. In addition, the redox reaction induced by oxygen vacancy migration also promotes the formation and rupture of the conductive filament. Results of the study show this ferrite material as an insertion layer in a resistive random-access memory structure that offers potential for future information storage and bioneuromorphic computation devices.

Enhanced uranium extraction from seawater: from the viewpoint of kinetics and thermodynamics

Uranium extraction from seawater (UES) is recognized as one of the seven pivotal chemical separations with the potential to revolutionize global paradigms. The forthcoming decade is anticipated to witness a surge in UES, driven by escalating energy demands. The oceanic reservoirs, possessing uranium quantities approximately 1000-fold higher than terrestrial mines, present a more sustainable and environmentally benign alternative. Empirical evidence from historical research indicates that adsorption emerges as the most efficacious process for uranium recovery from seawater, considering operational feasibility, cost-effectiveness, and selectivity. Over the years, scientific exploration has led to the development of a plethora of adsorbents with superior adsorption capacity. It would be efficient to design materials with a deep understanding of the adsorption from the perspective of kinetics and thermodynamics. Here, we summarize recent advancements in UES technology and the contemporary challenges encountered in this domain. Furthermore, we present our perspectives on the future trajectory of UES and finally offer our insights into this subject.

Research Progress on the Application of Topological Phase Transition Materials in the Field of Memristor and Neuromorphic Computing

Topological phase transition materials have strong coupling between their charge, spin orbitals, and lattice structure, which makes them have good electrical and magnetic properties, leading to promising applications in the fields of memristive devices. The smaller Gibbs free energy difference between the topological phases, the stable oxygen vacancy ordered structure, and the reversible topological phase transition promote the memristive effect, which is more conducive to its application in information storage, information processing, information calculation, and other related fields. In particular, extracting the current resistance or conductance of the two-terminal memristor to convert to the weight of the synapse in the neural network can simulate the behavior of biological synapses in their structure and function. In addition, in order to improve the performance of memristors and better apply them to neuromorphic computing, methods such as ion doping, electrode selection, interface modulation, and preparation process control have been demonstrated in memristors based on topological phase transition materials. At present, it is considered an effective method to obtain a unique resistive switching behavior by improving the process of preparing functional layers, regulating the crystal phase of topological phase transition materials, and constructing interface barrier-dependent devices. In this review, we systematically expound the resistance switching mechanism, resistance switching performance regulation, and neuromorphic computing of topological phase transition memristors, and provide some suggestions for the challenges faced by the development of the next generation of non-volatile memory and brain-like neuromorphic devices based on topological phase transition materials.

Amorphous BN-Based Synaptic Device with High Performance in Neuromorphic Computing

The von Neumann architecture has faced challenges requiring high-fulfillment levels due to the performance gap between its processor and memory. Among the numerous resistive-switching random-access memories, the properties of hexagonal boron nitride (BN) have been extensively reported, but those of amorphous BN have been insufficiently explored for memory applications. Herein, we fabricated a Pt/BN/TiN device utilizing the resistive switching mechanism to achieve synaptic characteristics in a neuromorphic system. The switching mechanism is investigated based on the I-V curves. Utilizing these characteristics, we optimize the potentiation and depression to mimic the biological synapse. In artificial neural networks, high-recognition rates are achieved using linear conductance updates in a memristor device. The short-term memory characteristics are investigated in depression by controlling the conductance level and time interval.

Modeling and Simulation of Hafnium Oxide RRAM Based on Oxygen Vacancy Conduction

The resistive memory has become one of the most promising new memory types because of its excellent performance, and HfO2 resistive material has attracted extensive attention. The conduction mechanism based on oxygen vacancy is widely recognized in the research of new nonvolatile memory. An RRAM electrothermal coupling model based on the oxygen vacancy conduction mechanism was constructed using COMSOL. The resistance process of the device is simulated by solving the coefficient partial differential equation, and the distribution of oxygen vacancy concentration, temperature, electric field, electric potential and other parameters in the dielectric layer at different voltages are obtained. The effects of temperature, dielectric layer thickness, top electrode thermal conductivity and conductive wire size on the resistance characteristics of the device are studied. It has guiding significance to further study the RRAM mechanism.

Bipolar and Complementary Resistive Switching Characteristics and Neuromorphic System Simulation in a Pt/ZnO/TiN Synaptic Device

In this work, a ZnO-based resistive switching memory device is characterized by using simplified electrical conduction models. The conventional bipolar resistive switching and complementary resistive switching modes are accomplished by tuning the bias voltage condition. The material and chemical information of the device stack including the interfacial layer of TiON is well confirmed by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analysis. The device exhibits uniform gradual bipolar resistive switching (BRS) with good endurance and self-compliance characteristics. Moreover, complementary resistive switching (CRS) is achieved by applying the compliance current at negative bias and increasing the voltage at positive bias. The synaptic behaviors such as long-term potentiation and long-term depression are emulated by applying consecutive pulse input to the device. The CRS mode has a higher array size in the cross-point array structure than the BRS mode due to more nonlinear I–V characteristics in the CRS mode. However, we reveal that the BRS mode shows a better pattern recognition rate than the CRS mode due to more uniform conductance update.

Self-Rectifying Resistive Switching and Short-Term Memory Characteristics in Pt/HfO2/TaOx/TiN Artificial Synaptic Device

Here, we propose a Pt/HfO₂/TaO_x/TiN artificial synaptic device that is an excellent candidate for artificial synapses. First, XPS analysis is conducted to provide the dielectric (HfO₂/TaO_x/TiN) information deposited by DC sputtering and atomic layer deposition (ALD). The self-rectifying resistive switching characteristics are achieved by the asymmetric device stack, which is an advantage of the current suppression in the crossbar array structure. The results show that the programmed data are lost over time and that the decay rate, which is verified from the retention test, can be adjusted by controlling the compliance current (CC). Based on these properties, we emulate bio-synaptic characteristics, such as short-term plasticity (STP), long-term plasticity (LTP), and paired-pulse facilitation (PPF), in the self-rectifying I–V characteristics of the Pt/HfO₂/TaO_x/TiN bilayer memristor device. The PPF characteristics are mimicked by replacing the bio-stimulation with the interval time of paired pulse inputs. The typical potentiation and depression are also implemented by optimizing the set and reset pulse. Finally, we demonstrate the natural depression by varying the interval time between pulse inputs.

Synthesis and Memristor Effect of a Forming-Free ZnO Nanocrystalline Films

We experimentally investigated the effect of post-growth annealing on the morphological, structural, and electrophysical parameters of nanocrystalline ZnO films fabricated by pulsed laser deposition. The influence of post-growth annealing modes on the electroforming voltage and the resistive switching effect in ZnO nanocrystalline films is investigated. We demonstrated that nanocrystalline zinc oxide films, fabricated at certain regimes, show the electroforming-free resistive switching. It was shown, that the forming-free nanocrystalline ZnO film demonstrated a resistive switching effect and switched at a voltage 1.9 ± 0.2 V from 62.42 ± 6.47 (RHRS) to 0.83 ± 0.06 kΩ (RLRS). The influence of ZnO surface morphology on the resistive switching effect is experimentally investigated. It was shown, that the ZnO nanocrystalline film exhibits a stable resistive switching effect, which is weakly dependent on its nanoscale structure. The influence of technological parameters on the resistive switching effect in a forming-free ZnO nanocrystalline film is investigated. The results can be used for fabrication of new-generation micro- and nanoelectronics elements, including random resistive memory (ReRAM) elements for neuromorphic structures based on forming-free ZnO nanocrystalline films.