Spintronic Memristor Research Articles

Flame intensity sensor based on the resistive and memory properties of spintronic memristor

A flame intensity sensor based on the resistive and memory properties of spintronic memristor is proposed. The sensor circuit mainly consists of the infrared radiation conversion module, the spintronic memristor array module, the amplifier and current limiting resistor. In order to verify the designed sensor, experiments were conducted under no flame, constant and gradually increasing flame combustion temperature, and the effects of the number of parallel memristor arrays, current limiting resistors, measurement distance and radiation wavelength on the sensor were analyzed. The experiments results demonstrate that the designed flame intensity sensor based on the resistive and memory properties of spintronic memristor can quantitatively measure the flame intensity. When the temperature change rate of flame combustion is 1℃/s, the sensor measurement range is 0–3.1 mW/cm2, and the sensitivity is 4.516kΩ/(mW/cm2). The rate of change in the resistance of the spintronic memristor array may be affected by the changes in flame intensity, but this does not affect the measurement range of the sensor.

Chirality-Induced Memristor of Chiral Nanostructured Half-Metallic Fe3O4 Films.

Spintronic memristors, which combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, are expected to play a crucial role in advancing quantitative information processing. This field commonly relies on magnetic tunnel junctions, domain wall motion, and spin waves. Here, the discovery of chirality-induced memristor behavior in chiral nanostructured Fe3O4 films (CNFFs) is reported. These CNFFs are grown on fluorine tin oxide (FTO) substrates using enantiomeric glutamic acid (Glu) as symmetry-breaking agents and consist of arrays of oriented twisted nanofibers. At 100 K, the L-CNFF exhibits memristor behavior as a pinched hysteresis loop in the I-V curve, while the D-CNFF exhibits semiconductor behavior with constant electrical resistance. The intrinsic spin polarization of half-metallic Fe3O4 and the chirality-induced spin selectivity (CISS) are speculated to contribute to the memristor in one handedness of the chiral structure. These findings present a novel spinristor that combines the functions of a memristor and a spin-filter based on chiral structures, which may promote the development of spintronic devices.

A novel nuclear radiation cumulant sensor based on spintronic memristor

A designed scheme combining nuclear radiation cumulant sensor with spintronic memristor is proposed. The sensor circuit mainly consists of nuclear radiation converter, spintronic memristor array, current limiting resistor, amplifier. In the presence of nuclear radiation, the nuclear radiation converter will generate photocurrent and cause changes in current and charge in the circuit, which will cause changes in the resistance of the spintronic memristor array in the circuit. Therefore, according to the memory and resistance feature of the spintronic memristor, the nuclear radiation cumulant of the object irradiated for a period of time can be expressed by the spintronic memristor array resistance value. At the same time, we use the threshold of feature to eliminate the influence of dark current in the measurement and achieve the measurement of nuclear radiation cumulant. In order to verify the designed sensor, experiments were carried out under no nuclear radiation, constant and randomly varying radiation intensities, and the effects of memory resistor array intersection and amplifier gain on the sensor were analyzed. The experimental results show that the designed nuclear radiation cumulant sensor based on the spintronic memristor array can complete the measurement of nuclear radiation cumulant. When the nuclear radiation intensity is 1 mGy/h, the nuclear radiation measurement range of the sensor is 0–3.2 mGy, and the sensitivity is 1251.47 Ω/mGy. The change of nuclear radiation intensity will affect the change speed of the resistance value of the spintronic memristor, but it does not affect the measuring range and sensitivity of the sensor. • The nuclear radiation sensor can make continuous multiple measurements of radiation cumulant. • The spintronic memristor eliminates the influence of dark current on the circuit and avoids the measurement errors. • The nuclear radiation sensor has higher flexibility to adjust the sensor measurement range and sensitivity.

A memristor-based analogue reservoir computing system for real-time and power-efficient signal processing

Reservoir computing offers a powerful neuromorphic computing architecture for spatiotemporal signal processing. To boost the power efficiency of the hardware implementations of reservoir computing systems, analogue devices and components—including spintronic oscillators, photonic modules, nanowire networks and memristors—have been used to partially replace the elements of fully digital systems. However, the development of fully analogue reservoir computing systems remains limited. Here we report a fully analogue reservoir computing system that uses dynamic memristors for the reservoir layer and non-volatile memristors for the readout layer. The system can efficiently process spatiotemporal signals in real time with three orders of magnitude lower power consumption than digital hardware. We illustrate the capabilities of the system using temporal arrhythmia detection and spatiotemporal dynamic gesture recognition tasks, achieving accuracies of 96.6% and 97.9%, respectively. Our memristor-based fully analogue reservoir computing system could be of use in edge computing applications that require extremely low power and hardware cost.

Anisotropy and Current Control of Magnetization in SrRuO3/SrTiO3 Heterostructures for Spin-Memristors

Spintronics-based nonvolatile components in neuromorphic circuits offer the possibility of realizing novel functionalities at low power. Current-controlled electrical switching of magnetization is actively researched in this context. Complex oxide heterostructures with perpendicular magnetic anisotropy (PMA), consisting of SrRuO3 (SRO) grown on SrTiO3 (STO) are strong material contenders. Utilizing the crystal orientation, magnetic anisotropy in such simple heterostructures can be tuned to either exhibit a perfect or slightly tilted PMA. Here, we investigate current induced magnetization modulation in such tailored ferromagnetic layers with a material with strong spin-orbit coupling (Pt), exploiting the spin Hall effect. We find significant differences in the magnetic anisotropy between the SRO/STO heterostructures, as manifested in the first and second harmonic magnetoresistance measurements. Current-induced magnetization switching can be realized with spin-orbit torques, but for systems with perfect PMA this switching is probabilistic as a result of the high symmetry. Slight tilting of the PMA can break this symmetry and allow the realization of deterministic switching. Control over the magnetic anisotropy of our heterostructures therefore provides control over the manner of switching. Based on our findings, we propose a three-terminal spintronic memristor, with a magnetic tunnel junction design, that shows several resistive states controlled by electric charge. Non-volatile states can be written through SOT by applying an in-plane current, and read out as a tunnel current by applying a small out-of-plane current. Depending on the anisotropy of the SRO layer, the writing mechanism is either deterministic or probabilistic allowing for different functionalities to emerge. We envisage that the probabilistic MTJs could be used as synapses while the deterministic devices can emulate neurons.

Spintronic memristors for neuromorphic circuits based on the angular variation of tunnel magnetoresistance.

In this study, a new type of compact magnetic memristor is demonstrated. It is based on the variation of the conductivity of a nano-sized magnetic tunnel junction as a function of the angle between the in-plane reference layer magnetization and a free layer exhibiting an isotropic in-plane coercivity. The free layer magnetization is rotated by two spin transfer torque contributions: one originating from the in-plane magnetized reference layer and the other one from an additional perpendicular polarizer integrated in the stack. Thanks to a proper tuning of the relative influence of these two torques, the magnetization of the free layer can be rotated step by step clockwise or anticlockwise in a range of angle between 0° (parallel configuration) and 180° (anti-parallel configuration) by sending pulses of current through the stack, of one or opposite polarity. The amplitude of the rotation steps and therefore of the conductance variations depends on the pulse amplitude and duration. In this way, we achieve monotonous variations of the resistance with the voltage polarity through the application of pulses in the ns range. We also retrieve the analytical expression of critical current density which is found to be in good agreement with the experimental results. The thermal stability of the intermediate resistance levels and the role of Joule heating are also discussed.

Implementation of Unbalanced Ternary Logic Gates with the Combination of Spintronic Memristor and CMOS

A memristor is a nanoscale electronic element that displays a threshold property, non-volatility, and variable conductivity. Its composite circuits are promising for the implementation of intelligence computation, especially for logic operations. In this paper, a flexible logic circuit composed of a spintronic memristor and complementary metal-oxide-semiconductor (CMOS) switches is proposed for the implementation of the basic unbalanced ternary logic gates, including the NAND, NOR, AND, and OR gates. Meanwhile, due to the participation of the memristor and CMOS, the proposed circuit has advantages in terms of non-volatility and load capacity. Furthermore, the input and output of the proposed logic are both constant voltages without signal degradation. All these three merits make the proposed circuit capable of realizing the cascaded logic functions. In order to demonstrate the validity and effectiveness of the entire work, series circuit simulations were carried out. The experimental results indicated that the proposed logic circuit has the potential to realize almost all basic ternary logic gates, and even some more complicated cascaded logic functions with a compact circuit construction, high efficiency, and good robustness.

Linear and symmetric conductance response of magnetic domain wall type spin-memristor for analog neuromorphic computing

We developed a three-terminal spintronic memristor named spin-memristor for the artificial synapse of neuromorphic devices. Current-induced domain wall (DW) motion type magnetoresistance was used to generate variable analog conductance changes. Magnetic tunnel junction with a perpendicularly magnetized DW layer was fabricated on a silicon substrate. Due to the forward and backward DW motions, a linear and symmetric conductance response was achieved. The impact of memristive behavior on neural networks was evaluated using numerical simulation of hand-written digit recognition. This spin-memristor is identified as one of the promising candidates for artificial synapses in analog neuromorphic devices.

Design of Hopfield network for cryptographic application by spintronic memristors

Memory being one of the essential credential in today’s computer world seeks forward newer research interests in its types. Hopfield neural networks of artificial neural networks are one of its classes that can be modelled to form an associative memory. In this paper, we have shown the Hopfield neural network constructed with spintronic memristor bridges accounting to act as an associative memory unit. The memristors are nanoscaled, in terms of size, which possess synaptic behaviour in the artificial neuromorphic system. The associative behaviour is realised by the updation of synaptic weights of memristive Hopfield with single- and multiple-bit associativity which is simulated in MATLAB. The application of the hardware in the field of cryptography is also proposed.

A spintronic memristor crossbar array for fuzzy control with application in the water valves control system

Fuzzy control, an intelligent control method, is generally employed to deal with complex nonlinear controlled objects that cannot be expressed by accurate mathematical model. Memristor, whose unique advantages are automatic successive memory and nonvolatility, brought new opportunity for solving the key question of fuzzy control. With the design idea of software harden, this paper first constructed membership function in the fuzzy controller based on the unique feature of crossbar array of the spintronic memristor and elaborated the whole construction process. After that, this paper simulated the construction process with MATLAB simulation software, verifying its reasonability and feasibility. Furthermore, a typical fuzzy control water tank system was chosen to explore and discuss the flexibility of spintronic memristor crossbar array in the real-time control system, and the proposed control strategy and the typical fuzzy control strategy were compared. The results revealed that the proposed control strategy was able to attain the effectiveness of the typical fuzzy control system in the real-time control system. This sets light to future research on the implementation of memristor crossbar array in the real-time control system and also promotes the application of fuzzy controller design idea. The problems needed to be solved when implementing memristor crossbar array in the real-time control system were discussed in the final section.

A Spin–Orbit‐Torque Memristive Device

AbstractMemristors, demonstrated by solid‐state devices with continuously tunable resistance, have emerged as a new paradigm for self‐adaptive networks that require synapse‐like functions (artificial synapse, for example). Spin‐based memristors offer advantages over other types of memristors because of their significant endurance and high energy efficiency. Yet it remains a challenge to build dense and functional spintronic memristors with structures and materials that are compatible with existing ferromagnetic devices. Here, a memristive device based upon Ta/CoFeB/MgO heterostructures is demonstrated, which are commonly used in out‐of‐plane magnetized magnetic tunnel junctions (MTJ). To achieve the memristive function, a domain wall (DW) is driven back and forth in a continuous manner in the CoFeB layer by applying in‐plane positive or negative current pulses along the Ta layer, utilizing the spin–orbit torque (SOT) that the current exerts on the CoFeB magnetization. Hence, the magnetization and consequently the anomalous Hall effect (AHE) resistance are modulated in an analog manner, being controlled by the pulsed current characteristics including amplitude, duration, and repetition number. The quasi‐continuous AHE resistance variation is explained by the SOT‐induced DW creep motion. These results pave the way for developing SOT‐based energy‐efficient neuromorphic systems.

An accurate model of domain-wall-based spintronic memristor

Spintronic memristors are promising devices that can be used in various applications such as memory chips and neuromorphic systems. The spintronic memristor combines the non-volatility advantage of resistive memristors, and the good scalability, and radiation hardness of spin-transfer torque magnetic devices. In addition, spintronic memristors can benefit from the maturity of integrating magnetic devices on top of CMOS devices. Current models of spintronic memristor only provide a similar version of the linear ion drift model of resistive memristors, which offers a simplified model, but with low accuracy and without enough linking to the device's physical parameters. In this paper, an accurate model of domain-wall- based spintronic memristor based on Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation is proposed. The proposed model provides a more accurate dynamical behavior by using the LLGS equation, and better relation to the device's physical parameters. It also uses the required equations that cover different types and geometries of spintronic memristors. The effect of the thermal fluctuations on device's parameters is also included into the model. The model uses the theory of domain-wall motion to explain the behavior of the device. Furthermore, a Verilog-A model is developed in order be compatible with IC CAD tools.

Chain of magnetic tunnel junctions as a spintronic memristor

In the context of neuromorphic computation, spintronic memristors are investigated for their use as synaptic weights. In this paper, we propose and experimentally demonstrate a resistive synaptic device based on ten magnetic tunnel junctions (MTJs) connected in a serial configuration. Our device exhibits multiple resistance levels that support its use as a synaptic element. It allows for two operating knobs: external magnetic field and voltage pulses (Spin-Transfer Torque). Moreover, it can be operated in different ways. When varying continuously the amplitude of the voltage pulse and/or the magnetic field, eleven resistance states can be reached. In contrast, if the initial state of the chain is reset between every step, a very large number of levels are reached. Ideally, a total of 2N resistance levels could be accessible. This coincides well with the desired analog-like behavior in ideal memristors. Since this device consists of a scalable number of N MTJs, and MTJ technology is continuously optimized and improved, the proposed memristor shows promise as a scalable synapse solution for neuromorphic hardware implementations.

Spintronic memristor synapse and its RWC learning algorithm

As one of the most widely used memristor models, the spintronic memristor has become a promising candidate for the electronic synapse. Non-volatility, nanoscale geometries, binary data and multi-level information storage make the circuit simpler and consume less electricity. In this study, a new spintronic memristor synaptic circuit is proposed which can realise positive, zero and negative weights successfully. Furthermore, the circuits of the presented synaptic-based neuron and compact neural network are designed and an improved random weight change (RWC) algorithm is proposed. Compared with the traditional RWC algorithm, it has faster training speed and less training error. In addition, the neural network is applied to data prediction, the result of which is closer to real data. Finally, the correctness and effectiveness of the proposed network are verified via a series of computer simulations.

An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array

Spintronic memristors have received significant attention as a potential building block for control systems, and particularly, it can be laid out in a high-density grid known as a crossbar array. Meanwhile, radial basis function (RBF) neural network control algorithm can effectively improve the control performance against large uncertain systems. But choosing reasonable parameters for RBF neural network to save convergence time and reduce the amplitude of the initial step is very important and difficult. Therefore, based on the study of characteristics of RBF neural network and spintronic memristors, this paper proposes a RBF neural network control algorithm based on spintronic memristor crossbar array; that is to say, the RBF neural network is divided into two parts of hardware and software. Hardware completes the updated parameters’ storage and extraction by spintronic memristor crossbar array, and software accomplishes the parameters updating process from the traditional RBF neural network. With a multiple-input multiple-output data sample training as the research object, simulation results show that the typical RBF neural network control algorithm needs 24 steps to fulfill the convergence requirements and the amplitude of the initial step is close to 2e−4. However, the proposed algorithm only needs 18 steps and the amplitude of the initial step is just about 8.6e−9. The comparison of these results indicates evidently the effectiveness of proposed algorithm.

Spintronic Memristor as Interface Between DNA and Solid State Devices

Recently biomolecular computing platforms have been widely investigated with great potentials in both biomedical research and practices, such as using molecular structures of DNA to present the data bits and to operate the logic. Emerging CMOS/molecular hybrid (CMOL) circuitry demonstrates many overwhelming advantages compared with pure biomolecular circuitry, including the design flexibility and compatibility with the traditional CMOS process. In this work, spintronic devices are utilized to detect the spatial information of DNA by translating the magnetic signals associated with the DNA pieces to electrical signals. The physical mechanisms and sensing performances of various magnetic sensor structures are discussed and evaluated, including giant magneto-resistance (GMR) spin valve sensors, tunnel magneto-resistance (TMR) sensors and our newly proposed spintronic GMR/TMR memristor sensors. A on-chip readout scheme is also proposed with a telecommunication method-frequency division multiplexing (FDM) technique to efficiently transmit useful information and filtrate the noise which could achieve high SNR up to 70 dB. The new approach can be adopted as not only the interface between the DNA structure and CMOS circuitry, but also a promising sensing mechanism in the DNA hybridization detection technique.

Process Variation Aware Design of Multi-Valued Spintronic Memristor-Based Memory Arrays

The missing fourth passive element, predicted by L. Chua and denoted by memristor, has recently been in the research focus since its titanium dioxide thin film realization is reported by HP. Following that, the spintronic memristor, which is based on the magnetic tunneling junction, is presented as an alternative to the thin film memristor. The nano-scale geometry size of the memristor makes it hard to control its dimensions due to the process variation resulting from the fabrication process. This process variation results in yield degradation in the spintronic memristor-based memory arrays. This yield degradation is more significant when the spintronic memristor is utilized as a multi-valued memory elements. In this paper, the impact of the process variation on the spintronic memristor-based memory yield is discussed for the 1-bit, 2-bit, and ${n}$ -bit memory element. Moreover, two approaches are introduced to enhance the memory yield.

A Spintronic Memristor-Based Neural Network With Radial Basis Function for Robotic Manipulator Control Implementation

A radial basis function (RBF) neural network control algorithm can effectively improve the robotic manipulators’ performance against a large amount of uncertainty. The adaptive law can be derived by using the Lyapunov method so that the stability of robotic manipulator control system and the weight self-adaptive convergence of RBF neural networks will be guaranteed. Meanwhile, system fluctuations and even overshot phenomenon under every start-up process, which are caused by the system’s convergence from the given nonoptimal initial weight value to the optimal weight value, can be avoided by using memristors to remember the optimal weight after the system’s first operation. According to the above analysis, this correspondence paper designs a kind of RBF neural network control algorithm based on spintronic memristors, and then analyzes its theoretical derivation process and core design idea. Finally, the system simulation model, which uses a two-link robotic manipulator as control object, is built to prove the algorithm’s validity and feasibility. Simulation results show that the proposed algorithm can satisfy the effect of presupposition.

A spintronic memristor bridge synapse circuit and the application in memrisitive cellular automata

Spintronic memristor is a new nonlinear circuit element which has property of memory and similar synapse, and the memristive effect can be realized by the spin-torque-induced magnetization switching or the magnetic domain wall motion. In this paper, a simple and compact memristor bridge synapse circuit which is able to perform signed synaptic weighting was proposed. The synaptic model have the good matching features in the system and is verified by matlab simulation experiment on the cellular automata, so this memristor synapse circuit is expected to be applied to neuromorphic system such as cellular neural network.

Overview of emerging memristor families from resistive memristor to spintronic memristor

AbstractMemristor is a fundamental circuit element in addition to resistor, capacitor, and inductor. As it can remember its resistance state even encountering a power off, memristor has recently received widespread applications from non-volatile memory to neural networks. The current memristor family mainly comprises resistive memristor, polymeric memristor, ferroelectric memristor, manganite memristor, resonant-tunneling diode memristor, and spintronic memristor in terms of the materials the device is made of. In order to help researcher better understand the physical principles of the memristor, and thus to provide a promising prospect for memristor devices, this paper presents an overview of memristor materials properties, switching mechanisms, and potential applications. The performance comparison among different memristor members is also given.