Search Results (10)

Temporal (race) computing schemes rely on temporal memories, where information is represented with the timing of signal edges. Standard digital circuit techniques can be used to capture the relative timing characteristics of signal edges. However, the properties of emerging device technologies could be particularly exploited for more efficient circuit implementations. Specifically, the collective dynamics of networks of memristive devices could be leveraged to facilitate time-domain computations in emerging memristive memories. To this end, this work studies the star interconnect configuration of bipolar memristive devices. Through circuit simulations using a behavioral model of voltage-controlled bipolar memristive devices, we demonstrated the suitability of such circuits in two different contexts, namely sensing and “rank-order” coding. We particularly analyzed the conditions that the employed memristive devices should meet to guarantee the expected operation of the circuit and the possible effects of device variability in the storage and the reproduction of the information in arriving signal edges. The simulation results in LTSpice validate the correct operation and confirm the promising application prospects of such simple circuit structures, which, we show, natively exist in the crossbar geometry. Therefore, the star interconnect configuration could be considered for temporal computations inside resistive memory (ReRAM) arrays. Full article

BiFeO₃/La_0.7Sr_0.3MnO₃ (BFO/LSMO) epitaxial heterostructures were successfully synthesized by pulsed laser deposition on (001)-oriented SrTiO₃ single-crystal substrates with Au top electrodes. Stable bipolar resistive switching characteristics regulated by ferroelectric polarization reversal was observed in the Au/BFO/LSMO heterostructures. The conduction mechanism was revealed to follow the Schottky emission model, and the Schottky barriers in high-resistance and low-resistance states were estimated based on temperature-dependent current–voltage curves. Further, the observed memristive behavior was interpreted via the modulation effect on the depletion region width and the Schottky barrier height caused by ferroelectric polarization reversal, combining with the oxygen vacancies migration near the BFO/LSMO interface. Full article

This review article attempts to provide a comprehensive review of the recent progress in cerium oxide (CeO₂)-based resistive random-access memories (RRAMs). CeO₂ is considered the most promising candidate because of its multiple oxidation states (Ce³⁺ and Ce⁴⁺), remarkable resistive-switching (RS) uniformity in DC mode, gradual resistance transition, cycling endurance, long data-retention period, and utilization of the RS mechanism as a dielectric layer, thereby exhibiting potential for neuromorphic computing. In this context, a detailed study of the filamentary mechanisms and their types is required. Accordingly, extensive studies on unipolar, bipolar, and threshold memristive behaviors are reviewed in this work. Furthermore, electrode-based (both symmetric and asymmetric) engineering is focused for the memristor’s structures such as single-layer, bilayer (as an oxygen barrier layer), and doped switching-layer-based memristors have been proved to be unique CeO₂-based synaptic devices. Hence, neuromorphic applications comprising spike-based learning processes, potentiation and depression characteristics, potentiation motion and synaptic weight decay process, short-term plasticity, and long-term plasticity are intensively studied. More recently, because learning based on Pavlov’s dog experiment has been adopted as an advanced synoptic study, it is one of the primary topics of this review. Finally, CeO₂-based memristors are considered promising compared to previously reported memristors for advanced synaptic study in the future, particularly by utilizing high-dielectric-constant oxide memristors. Full article

The electrical characteristics and resistive switching properties of memristive devices have been studied in a wide temperature range. The insulator and electrode materials of these devices (silicon oxide and titanium nitride, respectively) are fully compatible with conventional complementary metal-oxide-semiconductor (CMOS) fabrication processes. Silicon oxide is also obtained through the low-temperature chemical vapor deposition method. It is revealed that the as-fabricated devices do not require electroforming but their resistance state cannot be stored before thermal treatment. After the thermal treatment, the devices exhibit bipolar-type resistive switching with synaptic behavior. The conduction mechanisms in the device stack are associated with the effect of traps in the insulator, which form filaments in the places where the electric field is concentrated. The filaments shortcut the capacitance of the stack to different degrees in the high-resistance state (HRS) and in the low-resistance state (LRS). As a result, the electron transport possesses an activation nature with relatively low values of activation energy in an HRS. On the contrary, Ohm’s law and tunneling are observed in an LRS. CMOS-compatible materials and low-temperature fabrication techniques enable the easy integration of the studied resistive-switching devices with traditional analog–digital circuits to implement new-generation hardware neuromorphic systems. Full article

This paper reports the fundamentals and the SPICE implementation of the Dynamic Memdiode Model (DMM) for the conduction characteristics of bipolar-type resistive switching (RS) devices. Following Prof. Chua’s memristive devices theory, the memdiode model comprises two equations, one for the electron transport based on a heuristic extension of the quantum point-contact model for filamentary conduction in thin dielectrics and a second equation for the internal memory state related to the reversible displacement of atomic species within the oxide film. The DMM represents a breakthrough with respect to the previous Quasi-static Memdiode Model (QMM) since it describes the memory state of the device as a balance equation incorporating both the snapback and snapforward effects, features of utmost importance for the accurate and realistic simulation of the RS phenomenon. The DMM allows simple setting of the initial memory condition as well as decoupled modeling of the set and reset transitions. The model equations are implemented in the LTSpice simulator using an equivalent circuital approach with behavioral components and sources. The practical details of the model implementation and its modes of use are also discussed. Full article

In this study, we propose high-performance chitosan-based flexible memristors with embedded single-walled carbon nanotubes (SWCNTs) for neuromorphic electronics. These flexible transparent memristors were applied to a polyethylene naphthalate (PEN) substrate using low-temperature solution processing. The chitosan-based flexible memristors have a bipolar resistive switching (BRS) behavior due to the cation-based electrochemical reaction between a polymeric chitosan electrolyte and mobile ions. The effect of SWCNT addition on the BRS characteristics was analyzed. It was observed that the embedded SWCNTs absorb more metal ions and trigger the conductive filament in the chitosan electrolyte, resulting in a more stable and wider BRS window compared to the device with no SWCNTs. The memory window of the chitosan nanocomposite memristors with SWCNTs was 14.98, which was approximately double that of devices without SWCNTs (6.39). Furthermore, the proposed SWCNT-embedded chitosan-based memristors had memristive properties, such as short-term and long-term plasticity via paired-pulse facilitation and spike-timing-dependent plasticity, respectively. In addition, the conductivity modulation was evaluated with 300 synaptic pulses. These findings suggest that memristors featuring SWCNT-embedded chitosan are a promising building block for future artificial synaptic electronics applications. Full article

This study evaluated the memristive switching characteristics of a biomaterial solid polymer electrolyte (SPE) chitosan-based memristor and confirmed its artificial synaptic behavior with analog switching. Despite the potential advantages of organic memristors for high-end electronics, the unstable multilevel states and poor reliability of organic devices must be overcome. The fabricated Ti/SPE-chitosan/Pt-structured memristor has stable bipolar resistive switching (BRS) behavior due to a cation-based electrochemical reaction between a polymeric electrolyte and metal ions and exhibits excellent endurance in 5 × 10² DC cycles. In addition, we achieved multilevel per cell (MLC) BRS I-V characteristics by adjusting the set compliance current (I_cc) for analog switching. The multilevel states demonstrated uniform resistance distributions and nonvolatile retention characteristics over 10⁴ s. These stable MLC properties are explained by the laterally intensified conductive filaments in SPE-chitosan, based on the linear relationship between operating voltage margin (ΔV_switching) and I_cc. In addition, the multilevel resistance dependence on I_cc suggests the capability of continuous analog resistance switching. Chitosan-based SPE artificial synapses ensure the emulation of short- and long-term plasticity of biological synapses, including excitatory postsynaptic current, inhibitory postsynaptic current, paired-pulse facilitation, and paired-pulse depression. Furthermore, the gradual conductance modulations upon repeated stimulation by 10⁴ electric pulses were evaluated in high stability. Full article

Energy-efficient computing paradigms beyond conventional von-Neumann architecture, such as neuromorphic computing, require novel devices that enable information storage at nanoscale in an analogue way and in-memory computing. Memristive devices with long-/short-term synaptic plasticity are expected to provide a more capable neuromorphic system compared to traditional Si-based complementary metal-oxide-semiconductor circuits. Here, compositionally graded oxide films of Al-doped Mg_xZn_1−xO (g-Al:MgZnO) are studied to fabricate a memristive device, in which the composition of the film changes continuously through the film thickness. Compositional grading in the films should give rise to asymmetry of Schottky barrier heights at the film-electrode interfaces. The g-Al:MgZnO films are grown by using aerosol-assisted chemical vapor deposition. The current-voltage (I-V) and capacitance-voltage (C-V) characteristics of the films show self-rectifying memristive behaviors which are dependent on maximum applied voltage and repeated application of electrical pulses. Endurance and retention performance tests of the device show stable bipolar resistance switching (BRS) with a short-term memory effect. The short-term memory effects are ascribed to the thermally activated release of the trapped electrons near/at the g-Al:MgZnO film-electrode interface of the device. The volatile resistive switching can be used as a potential selector device in a crossbar memory array and a short-term synapse in neuromorphic computing. Full article

Solution-processable nonvolatile memory devices, consisted of graphene oxide (GO) embedded into an insulating polymer polymethyl methacrylate (PMMA), were manufactured. By varying the GO content in PMMA nanocomposite films, the memristic conductance behavior of the Ni/PMMA:GO/Indium tin oxide (ITO) sandwiched structure can be tuned in a controllable manner. An investigation was made on the memristic performance mechanism regarding GO charge-trap memory; these blends were further characterized by transmission electron microscope (TEM), scanning electron microscope (SEM), Fourier transform infrared spectra (FTIR), Raman spectra, thermogravimetric analysis, X-ray diffraction (XRD), ultraviolet-visible spectroscopy, and fluorescence spectra in particular. Dependent on the GO content, the resistive switching was originated from the charges trapped in GO, for which bipolar tunable memristic behaviors were observed. PMMA:GO composites possess an ideal capability for large area device applications with the benefits of superior electronic properties and easy chemical modification. Full article

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al₂O₃/TaO_x/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (<1%) can be obtained with random initial states. However, the device shows non-linear conductance change characteristics, and a nearly linear conductance change behavior is obtained by optimizing the training scheme. Based on these results, the device is a promising emulator for biology synapses, which could be of great benefit to memristor-based neuromorphic computing. Full article