Memristive Switching Research Articles

Stateful Three-Input Logic with Memristive Switches

Memristive switches are able to act as both storage and computing elements, which make them an excellent candidate for beyond-CMOS computing. In this paper, multi-input memristive switch logic is proposed, which enables the function X OR (Y NOR Z) to be performed in a single-step with three memristive switches. This ORNOR logic gate increases the capabilities of memristive switches, improving the overall system efficiency of a memristive switch-based computing architecture. Additionally, a computing system architecture and clocking scheme are proposed to further utilize memristive switching for computation. The system architecture is based on a design where multiple computational function blocks are interconnected and controlled by a master clock that synchronizes system data processing and transfer. The clocking steps to perform a full adder with the ORNOR gate are presented along with simulation results using a physics-based model. The full adder function block is integrated into the system architecture to realize a 64-bit full adder, which is also demonstrated through simulation.

A Boolean OR gate implemented with an optoelectronic switching memristor

Nonvolatile stateful logic computing in memristors has tremendous potential to realize the aggregation combined with information storage and processing in the same physical location for breaking the von Neumann bottleneck of traditional computing architecture. Here, we fabricate a monoclinic BiVO4 film with a bandgap of Eg ≈ 2.4 eV and a nanoporous morphology as the memristor storage medium. The device, consisting of a TiN/BiVO4/fluorine-doped tin oxide structure, demonstrated excellent electric- and light-control of resistive switching performance. A Boolean “OR” gate is shown to be operable with an electrical signal and light signal as inputs and the resistance as output. According to the I–V fitting results, the conduction mechanism of the memristor is inferred to be trapped-assisted tunneling model. The large photocurrent is due to trapped electrons in the defects which will be released to the conduction band. The nanoporous structure and suitable bandgap are also beneficial to light absorption and electron detrapping for enlarging photocurrent. This work lays the device foundation for electrical–optical controlling logic functions in memristor devices.

Nanoparticle Dynamics in Oxide‐Based Memristive Devices

In microelectronics, a device's functionality is shaped by its interfaces. While classical semiconductor research aims for the preparation of nearly ideal interfaces, the emerging paradigm is that new functionalities can arise from interfaces with less perfection. Memristive devices rely on the control of such less‐perfect interfaces. They are the building blocks for a new range of applications, including new memory and logic architectures and neuromorphic computing. Research on memristive systems and applications demands an interdisciplinary approach across disciplines, including solid‐state physics, electrochemistry, and biochemistry. Advanced metrology is the key for better understanding and finally a better control of such interfaces and novel device technologies. Herein, the authors highlight such recent advances in characterization and the understanding of electrochemical reactions on the (sub‐) nanoscale and the dynamics of the nanoparticles and clusters involved during operation of memristive devices acting as a model system. The authors focus particularly on in operando real‐time monitoring of the memristive switching effect by transmission electron microscopy and a novel plasmon‐enhanced spectroscopy method.

Optically Switchable Memristors: A Route to Hierarchical Control in Artificial Intelligent Systems

Modulation of resistive switching memory by light opens the route to new optoelectronic devices that can be switched optically and read electronically. Applications include integrated circuits with memory elements switchable by light and optically reconfigurable and tunable synaptic circuits for neuromorphic computing applications and brain-inspired artificial intelligent systems. A novel polarization and wavelength-specific optoelectronic memory device is presented1 that can be controlled purely optically, electronically or by both. Optical or electronic modulation switches the device between low and high conducting states whilst modulation by both facilitates fine-tuning of the resistive memory properties and switching characteristics of the device. In biological synapses, the connection strength (plasticity) between two neurons is controlled by the ionic flow through the synaptic cleft and it is widely believed that the adaptation of synaptic weights enables biological systems to learn and function. Similarly, the conductance of a memristor depends on the history of the total charge that has travelled through it. A key feature of neuronal learning is habituation, whereby repeated stimuli strengthens the synaptic plasticity whilst a lack of stimuli results in weakening. Learning in biological systems also involves spike-timing-dependent plasticity (STDP). In STDP, learning the synaptic efficacy governing potentiation and depression is determined by the temporal order of pre-synaptic and post-synaptic spikes. Using our optical memristor device, we demonstrate optical control of synaptic potentiation and depression, optical switching between short and long-term memory and optical modulation of the synaptic efficacy via spike timing dependent plasticity. The work opens the route to dynamic patterning of memristor networks both spatially and temporally by light, thus allowing the development of new optically reconfigurable neural networks and adaptive electronic circuits.2 , 3 References Jaafar, A. H. et al. Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems. Nanoscale 9, 17091 (2017).Kossifos, K. M., Antoniades, M. A., Georgiou, J., Jaafar, A. H. & Kemp, N. T. An Optically-Programmable Absorbing Metasurface. IEEE Int. Symp. Circuits Syst. (2018). doi:10.1109/ISCAS.2018.8351874Georgiou, J., Kossifos, K. M., Antoniades, M. A., Jaafar, A. H. & Kemp, N. T. Chua Mem-Components for Adaptive RF Metamaterials. IEEE Int. Symp. Circuits Syst. (2018). doi:10.1109/ISCAS.2018.8351852 Figure 1

Switching Transients in Memristors

Transient performance of nanowire memristors realized using different material systems is discussed. The approach is validated by comparing simulated results with experimental data obtained for a ZnO nanowire memristor. The ZnO nanowire memristors demonstrate bipolar resistive switching with an ROFF/RON ratio of 684 that is 3 times higher than the previous best report. The transient switching model, derived from the physical mechanisms for memristor switching, show a material dependent switching delay greatly influenced by the mobility of the oxygen vacancies. Measured switching delay of 372 µs for ZnO memristor show excellent agreement with the simulated data. The switching delays for other material systems, namely – TiOx, TaOx, HfOx, and ZrOx are calculated to be 2.5 s, 5.5 ns, 11.8 ps, and 7.15 ps, respectively, for identical device geometry (length 2 µm and diameter 300 nm). Upon scaling devices down to 50 nm, the delay is observed to decrease by 3-4 orders of magnitude. ZrOx based memristors showed the shortest switching delay owing to a mobility as high as 370 cm2/V-s. Experimental data for ZnO memristors suggest rise and fall times shorter than 7 µs and 10 ns, respectively. Ultralow switching power of 261 µW and 155 µW are achieved for SET and RESET switching, respectively. Measured switching energy less than 83 nJ and slew rates greater than 0.02 V/µs are attained.

Miniature Multilevel Optical Memristive Switch Using Phase Change Material

Optical memristive switches can be used as optical latching switches in which the switching state is changed only by applying an electrical Write/Erase pulse and maintained without an external power supply. We demonstrate an optical memristive switch based on a silicon multimode interferometer structure covered with nanoscale-sized Ge2Sb2Te5 (GST) material on top. The phase change of GST is triggered by resistive heating of the silicon layer beneath GST with an electrical pulse. Experimental results reveal that the optical transmissivity can be tuned in a controllable and repeatable manner. Partial crystallization of GST is obtained by controlling the width and amplitude of the electrical pulses. Crucially, we demonstrate that both Erase and Write operations, to and from any intermediate level, are possible with accurate control of the electrical pulses. Our work marks a significant step forward toward realizing photonic memristive switches without static power consumption, which are highly demanded in integrated photonics.

Low-Voltage Oscillatory Neurons for Memristor-Based Neuromorphic Systems.

Neuromorphic systems consisting of artificial neurons and synapses can process complex information with high efficiency to overcome the bottleneck of von Neumann architecture. Artificial neurons are essentially required to possess functions such as leaky integrate‐and‐fire and output spike. However, previous reported artificial neurons typically have high operation voltage and large leakage current, leading to significant power consumption, which is contrary to the energy‐efficient biological model. Here, an oscillatory neuron based on Ag filamentary threshold switching memristor (TS) that has a low operation voltage (<0.6 V) with ultralow power consumption (<1.8 µW) is presented. It can trigger neuronal functions, including leaky integrate‐and‐fire and threshold‐driven spiking output, with high endurance (>108 cycles). Being connected to an external resistor or a resistive switching memristor (RS) as synaptic weight, the TS clearly demonstrates self‐oscillation behavior once the input pulse voltage exceeds the threshold voltage. Meanwhile, the oscillation frequency is proportional to the input pulse voltage and the conductance of RS synapse, which can be used to integrate the weighted sum current. As an energy‐efficient memristor‐based spiking neural network, this combination of TS oscillatory neuron with RS synapse is further evaluated for image recognition achieving an accuracy of 79.2 ± 2.4% for CIFAR‐10 subset.

Independent Memcapacitive Switching Triggered by Bromide Ion Migration for Quaternary Information Storage.

Memcapacitors are emerging as an attractive candidate for high-density information storage due to their multilevel and adjustable capacitances and long-term retention without a power supply. However, knowledge of their memcapacitive mechanism remains unclear and accounts for the limited implementation of memcapacitors for multilevel memory technologies. Here, repeatable and reproducible quaternary memories fabricated from hybrid perovskite (CH3 NH3 SnBr3 ) memcapacitors are reported. The device can be modulated to at least four capacitive states ranging from 0 to 169 pF with retention for 104 s. Impressively, an effective device yield approaching 100% for quaternary memory switching is achieved by a batch of devices; each state has a sufficiently narrow distribution that can be distinguished from the others and is superior to most multilevel memories that have a low device yield as well as an overlapping distribution of states. The memcapacitive switching stems from the modulated p-i-n junction capacitance triggered by Br- migration, as demonstrated by in situ element mapping, X-ray photoelectron spectra, and frequency-dependent capacitance measurements; this mechanism is different from the widely reported memristive switching involving filamentary conduction. The results provide a new way to produce high-density information storage through memcapacitors.

Memristive resistive switch based on spontaneous barrier creation in metal-chalcogenide junctions

Electrically induced resistive switching resulting from ionic transport and electrochemical redox reactions is promising for future generation non-volatile memory devices and artificial neural computing. The key ingredient for the highly efficient neural computing in this context is a memristor, which is a special type of a resistive two-terminal element whose electrical properties depend on not only the state of the element but also how the state has been achieved in its history. Memristor characteristics are demonstrated in a bilayer junction of Al and a Bi–Cu–S alloy utilizing electrically reversible generation of an insulating interface. The high resistance due to the interface layer drops abruptly by orders of magnitude when the barrier is annihilated electrochemically under a bias. The barrier is made to be robust by applying a reverse bias, giving rise to a controllable memory effect on the switching phenomenon. The switching mechanism based on the manipulation of a barrier, which is complementary to conventional bridging conductive filaments, will open the way for new functionalities as device elements.

Bipolar Cu/HfO2/p++ Si Memristors by Sol-Gel Spin Coating Method and Their Application to Environmental Sensing

In this paper, the memristive switching behavior of Cu/ HfO2/p++ Si devices fabricated by an organic-polymer-assisted sol-gel spin-coating method, coupled with post-annealing and shadow-mask metal sputtering steps, is examined. HfO2 layers of about 190 nm and 80 nm, are established using cost-effective spin-coating method, at deposition speeds of 2000 and 4000 rotations per minute (RPM), respectively. For two types of devices, the memristive characteristics (Von, Ion, and Vreset) and device-to-device electrical repeatability are primarily discussed in correlation with the oxide layer uniformity and thickness. The devices presented in this work exhibit an electroforming free and bipolar memory-resistive switching behavior that is typical of an Electrochemical Metallization (ECM) I-V fingerprint. The sample devices deposited at 4000 RPM generally show less variation in electrical performance parameters compared to those prepared at halved spin-coating speed. Typically, the samples prepared at 4000 RPM (n = 8) display a mean switching voltage Von of 3.0 V (±0.3) and mean reset voltage Vreset of −1.1 V (±0.5) over 50 consecutive sweep cycles. These devices exhibit a large Roff/Ron window (up to 104), and sufficient electrical endurance and retention properties to be further examined for radiation sensing. As they exhibit less statistical uncertainty compared to the samples fabricated at 2000 RPM, the devices prepared at 4000 RPM are tested for the detection of soft gamma rays (emitted from low-activity Cs-137 and Am-241 radioactive sources), by assessing the variation in the on-state resistance value upon exposure. The analysis of the probability distributions of the logarithmic Ron values measured over repeated ON-OFF cycles, before, during and after exposing the devices to radiation, demonstrate a statistical difference. These results pave the way for the fabrication and development of cost-effective soft-gamma ray detectors.

Design of Ultra-Compact Optical Memristive Switches with GST as the Active Material.

In the following study, we propose optical memristive switches consisting of a silicon waveguide integrated with phase-change material Ge2Sb2Te5 (GST). Thanks to its high refractive index contrast between the crystalline and amorphous states, a miniature-size GST material can offer a high switching extinction ratio. We optimize the device design by using finite-difference-time-domain (FDTD) simulations. A device with a length of 4.7 μm including silicon waveguide tapers exhibits a high extinction ratio of 33.1 dB and a low insertion loss of 0.48 dB around the 1550 nm wavelength. The operation bandwidth of the device is around 60 nm.

Wavelength dependent light tunable resistive switching graphene oxide nonvolatile memory devices

This paper reports on the first optically tunable graphene oxide memristor device. Modulation of resistive switching memory by light opens the route to new optoelectronic devices that can be switched optically and read electronically. Applications include integrated circuits with memory elements switchable by light and optically reconfigurable and tunable synaptic circuits for neuromorphic computing and brain-inspired, artificial intelligence systems. In this report, planar and vertical structured optical resistive switching memristors based on graphene oxide are reported. The device is switchable by either optical or electronic means, or by a combination of both. In addition the devices exhibit a unique wavelength dependence that produces reversible and irreversible properties depending on whether the irradiation is long or short wavelength light, respectively. For long wavelength light, the reversible photoconductance effect permits short-term dynamic modulation of the resistive switching properties of the light, which has application as short-term memory in neuromorphic computing. In contrast, short wavelength light induces both the reversible photoconductance effect and an irreversible change in the memristance due to reduction of the graphene oxide. This has important application in the fabrication of cloned neural networks with factory defined weights, enabling the fast replication of artificial intelligent chips with pre-trained information.

Direct observation of delithiation as the origin of analog memristance in LixNbO2

The discovery of analog LixNbO2 memristors revealed a promising new memristive mechanism wherein the diffusion of Li+ rather than O2− ions enables precise control of the resistive states. However, directly correlating lithium concentration with changes to the electronic structure in active layers remains a challenge and is required to truly understand the underlying physics. Chemically delithiated single crystals of LiNbO2 present a model system for correlating lithium variation with spectroscopic signatures from operando soft x-ray spectroscopy studies of device active layers. Using electronic structure modeling of the x-ray spectroscopy of LixNbO2 single crystals, we demonstrate that the intrinsic memristive behavior in LixNbO2 active layers results from field-induced degenerate p-type doping. We show that electrical operation of LixNbO2-based memristors is viable even at marginal Li deficiency and that the analog memristive switching occurs well before the system is fully metallic. This study serves as a benchmark for material synthesis and characterization of future LixNbO2-based memristor devices and suggests that valence change switching is a scalable alternative that circumvents the electroforming typically required for filamentary-based memristors.

On the resistive switching mechanism of parylene-based memristive devices

Abstract Parylene is a widely used polymer possessing such advantages as low cost and safety for the human body. Recently, several studies have been conducted showing that parylene can be used as a dielectric layer of memristors — new circuit design elements that are promising for the implementation of hardware neural networks. However, the mechanism of resistive switching of parylene-based memristors remains unclear. In this paper, we report the result of a comprehensive study of this mechanism for Metal/Parylene/ITO sandwich memristive devices. The obtained results clearly show that the origin of resistive switching in the devices is the formation of conductive metal bridges (filaments) from the top electrode (Cu, Ag or Al) to the bottom one (ITO). And furthermore, conductance quantization effect with both integer and half-integer multiples of the quantum of conductance G0 = 2e2/h has been observed in the samples, which also confirms the chosen switching model, and can be useful in the development of multilevel data memory cells.

Memristive switching in ionic liquid–based two-terminal discrete devices

In the present work, we have developed discrete and two-terminal memristive devices using 1-butyl-3-methylimidazolium bromide [Bmim][Br] ionic liquid (IL). We have varied the mole fractions (x) of IL from 0.0001 to 1 and investigated its memristive properties. The bipolar resistive switching and frequency-dependent limiting linear characteristics are clearly observed in developed IL memristive devices. Furthermore, analog memory property indicates that the IL memristive device is a potential candidate to develop electronic synapse devices for neuromorphic computing application. It is observed that the 0.010-mol fraction-based memristive device shows good resistive switching, good memory window (ratio of HRS/LRS) (~ 36), and uniform endurance. In order to cross-check our approach, we have developed 1-ethyl-3-methylimidazolium bromide [Emim][Br] IL devices (x = 0.0001 to 1) and studied its memristive properties. Interestingly, [Emim][Br] IL devices also show the memristive-like properties similar to [Bmim][Br] IL memristive devices. The results of both IL-based devices indicate that the two-terminal structure with IL as an active element could be a possible solution to develop two-terminal discrete memristive devices.

Synaptic and Fast Switching Memristance in Porous Silicon-Based Structures.

Memristors are two terminal electronic components whose conductance depends on the amount of charge that has flown across them over time. This dependence can be gradual, such as in synaptic memristors, or abrupt, as in resistive switching memristors. Either of these memory effects are very promising for the development of a whole new generation of electronic devices. For the successful implementation of practical memristors, however, the development of low cost industry compatible memristive materials is required. Here the memristive properties of differently processed porous silicon structures are presented, which are suitable for different applications. Electrical characterization and SPICE simulations show that laser-carbonized porous silicon shows a strong synaptic memristive behavior influenced by defect diffusion, while wet-oxidized porous silicon has strong resistance switching properties, with switching ratios over 8000. Results show that practical memristors of either type can be achieved with porous silicon whose memristive properties can be adjusted by the proper material processing. Thus, porous silicon may play an important role for the successful realization of practical memristorics with cost-effective materials and processes.

MemSens: a new detection method for heavy metals based on silver nanoparticle assisted memristive switching principle

This work reports a new heavy metal sensing and detection methodology based on memristive switching effect. The hysteresis loop and limiting linear characteristic of a memristive device are used to validate the hypothesis of detection of heavy metals by putting them in contact with silver nanoparticles (Ag NPs). The sensing of various heavy metal ions using the proposed methodology was done and the results showed a response towards all tested metal ions. In particular, the selectivity of the Ag NPs towards Cd2+ ions was more than other metal ions. The current against concentration graph for Ag NPs + Cd2+ ions system was obtained which gives the possibility of determination of Cd2+ ion concentration in an unknown sample. The concentration of the Cd2+ ions was varied to test the lower limit of detection of the system and found that the sensor system was able to detect the Cd2+ up to very low concentration. Therefore, the conceptually new heavy metal detection approach is a significant new advancement in heavy metal detection and preparation of portable devices for that purpose.

Multilevel Bipolar Memristor Model Considering Deviations of Switching Parameters in the Verilog-A Language

We describe a bipolar memristor in the Verilog-A language. The proposed model concepts take into account the following parameter deviations in the memristor switching between conduction states: the variation of the conduction parameters in the highly resistive and low-resistance state, the switching threshold variations, and the variation of the number of switching cycles.

Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing

An electronic synapse (e-synapse) based on memristive switching is a promising electronic element that emulates a biological synapse to realize neuromorphic computing. However, the complex resistive switching process it relies on hampers the reproducibility of its performance. Thus, achievement of a reproducible electronic synapse with a high rate of finished products has become a significant challenge in the development of an artificial intelligent circuit. Here, we demonstrate an ultrathin e-synapse having high yield (>95%), minimal performance variation, and extremely low power consumption based on an Al2O3/graphene quantum dots/Al2O3 sandwich structure that was fabricated using atomic layer deposition. The e-synapse showed both high device-to-device and cycle-to-cycle reproducibility with high stability, endurance, and switching uniformity, because the essential synaptic behaviors could be observed. This implementation of an e-synapse with an Al2O3/graphene quantum dots/Al2O3 structure should intensify motivation for engineering e-synapses for neuromorphic computing.

RF Single-Pole Double-Throw Switch Based on Two-Port Memistor

The paper introduces a novel memristive two-port element for an efficient construction of Radio Frequency (RF) single-pole double-throw (SPDT) switches, the so-called Two-Port Memistor (TPM). In comparison with the classical Widrow memistor whose resistance depends on the charge passing through the auxiliary terminal, the resistance of the TPM is governed by the controlling signal applied to the programming gate. It is demonstrated that the SPDT switch can be constructed via two TPMs such that the RF chokes providing DC references and capacitors for AC couplings are no longer necessary. Since the TPM-based switches provide elegant and much simpler circuit solutions than the currently developed memristive switches, this paper could stimulate the research towards manufacturing the TPM as a solid-state device.