Showing papers on "Memristor published in 2009"
Journal Article•
TL;DR: It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element, and the described SPICE model of the memristor is constructed as an open model, enabling additional modifications of non-linear boundary conditions.
Abstract: A mathematical model of the prototype of memristor, manufactured in 2008 in Hewlett-Packard Labs, is described in the paper. It is shown that the hitherto published approaches to the modeling of boundary conditions need not conform with the requirements for the behavior of a practical circuit element. The described SPICE model of the memristor is thus constructed as an open model, enabling additional modifications of non-linear boundary conditions. Its functionality is illustrated on computer simulations.
1,025 citations
18 Sep 2009
TL;DR: It is argued that capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system, are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend upon the history ofThe system, at least within certain time scales.
Abstract: We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system All these elements typically show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor We argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior
913 citations
TL;DR: The nature of the oxide electroforming as an electro-reduction and vacancy creation process caused by high electric fields and enhanced by electrical Joule heating is explained with direct experimental evidence.
Abstract: Metal and semiconductor oxides are ubiquitous electronic materials. Normally insulating, oxides can change behavior under high electric fields—through ‘electroforming’ or ‘breakdown’—critically affecting CMOS (complementary metal‐oxide‐semiconductor) logic, DRAM (dynamic random access memory) and flash memory, and tunnel barrier oxides. An initial irreversible electroforming process has been invariably required for obtaining metal oxide resistance switches, which may open urgently needed new avenues for advanced computer memory and logic circuits including ultra-dense non-volatile random access memory (NVRAM) and adaptive neuromorphic logic circuits. This electrical switching arises from the coupled motion of electrons and ions within the oxide material, as one of the first recognized examples of a memristor (memory‐resistor) device, the fourth fundamental passive circuit element originally predicted in 1971 by Chua. A lack of device repeatability has limited technological implementation of oxide switches, however. Here we explain the nature of the oxide electroforming as an electro-reduction and vacancy creation process caused by high electric fields and enhanced by electrical Joule heating with direct experimental evidence. Oxygen vacancies are created and drift towards the cathode, forming localized conducting channels in the oxide. Simultaneously, O 2− ions drift towards the anode where they evolve O2 gas, causing physical deformation of the junction. The problematic gas eruption and physical deformation are mitigated by shrinking to the nanoscale and controlling the electroforming voltage polarity. Better yet, electroforming problems can be largely eliminated by engineering the device structure to remove ‘bulk’ oxide effects in favor of interface-controlled electronic switching.
787 citations
TL;DR: In this paper, the properties of a single memristor, memristors in series and parallel, as well as ideal MC, MCL and MCL circuits are discussed.
Abstract: We present an introduction to and a tutorial on the properties of the recently discovered ideal circuit element, a memristor. By definition, a memristor M relates the charge q and the magnetic flux in a circuit and complements a resistor R, a capacitor C and an inductor L as an ingredient of ideal electrical circuits. The properties of these three elements and their circuits are a part of the standard curricula. The existence of the memristor as the fourth ideal circuit element was predicted in 1971 based on symmetry arguments, but was clearly experimentally demonstrated just last year. We present the properties of a single memristor, memristors in series and parallel, as well as ideal memristor–capacitor (MC), memristor–inductor (ML) and memristor–capacitor–inductor (MCL) circuits. We find that the memristor has hysteretic current–voltage characteristics. We show that the ideal MC (ML) circuit undergoes non-exponential charge (current) decay with two time scales and that by switching the polarity of the capacitor, an ideal MCL circuit can be tuned from overdamped to underdamped. We present simple models which show that these unusual properties are closely related to the memristor's internal dynamics. This tutorial complements the pedagogy of ideal circuit elements (R, C and L) and the properties of their circuits, and is aimed at undergraduate physics and electrical engineering students.
719 citations
01 Jan 2009
TL;DR: In this paper, the authors extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and in- ductors whose properties depend on the state and history of the system.
Abstract: We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and in- ductors whose properties depend on the state and history of the system. All these elements typically show pinched hyster- etic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor .W e argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior.
689 citations
TL;DR: In this article, a mathematical definition of a memristive device provides the framework for understanding the physical processes involved in bipolar switching and also yields formulas that can be used to compute and predict important electrical and dynamical properties of the device.
Abstract: Memristive devices are promising components for nanoelectronics with applications in nonvolatile memory and storage, defect-tolerant circuitry, and neuromorphic computing. Bipolar resistive switches based on metal oxides such as TiO2 have been identified as memristive devices primarily based on the “pinched hysteresis loop” that is observed in their current-voltage (i-v) characteristics. Here we show that the mathematical definition of a memristive device provides the framework for understanding the physical processes involved in bipolar switching and also yields formulas that can be used to compute and predict important electrical and dynamical properties of the device. We applied an electrical characterization and state-evolution procedure in order to capture the switching dynamics of a device and correlate the response with models for the drift diffusion of ionized dopants (vacancies) in the oxide film. The analysis revealed a notable property of nonlinear memristors: the energy required to switch a me...
688 citations
TL;DR: In this article, the authors extend the notion of memristive systems to capacitive and inductive elements, namely capacitors and inductors whose properties depend on the state and history of the system.
Abstract: We extend the notion of memristive systems to capacitive and inductive elements, namely capacitors and inductors whose properties depend on the state and history of the system. All these elements show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor. We argue that these devices are common at the nanoscale where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and they are likely to find applications in neuromorphic devices to simulate learning, adaptive and spontaneous behavior.
660 citations
Posted Content•
TL;DR: In this article, a memory-resistor (memristor for short) is used to emulate the synapse behavior of a neural network, which can remember its past dynamical history, store a continuous set of states, and be "plastic" according to the presynaptic and post-synaptic neuronal activity.
Abstract: When someone mentions the name of a known person we immediately recall her face and possibly many other traits. This is because we possess the so-called associative memory, that is the ability to correlate different memories to the same fact or event. Associative memory is such a fundamental and encompassing human ability (and not just human) that the network of neurons in our brain must perform it quite easily. The question is then whether electronic neural networks (electronic schemes that act somewhat similarly to human brains) can be built to perform this type of function. Although the field of neural networks has developed for many years, a key element, namely the synapses between adjacent neurons, has been lacking a satisfactory electronic representation. The reason for this is that a passive circuit element able to reproduce the synapse behaviour needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behaviour with electronic neural networks.
593 citations
TL;DR: In this article, an approach to use memristors (resistors with memory) in programmable analog circuits has been proposed, where low voltages are applied to the resistors during their operation as analog circuit elements and high voltages were used to program the memristor's states.
Abstract: We suggest an approach to use memristors (resistors with memory) in programmable analog circuits. Our idea consists in a circuit design in which low voltages are applied to memristors during their operation as analog circuit elements and high voltages are used to program the memristor's states. This way, as it was demonstrated in recent experiments, the state of memristors does not essentially change during analog mode operation. As an example of our approach, we have built several programmable analog circuits demonstrating memristor-based programming of threshold, gain and frequency.
389 citations
TL;DR: In this paper, the existence of spintronic memristor in nanoscale is demonstrated based upon spin-torque induced magnetization switching and magnetic-domain-wall motion.
Abstract: Existence of spintronic memristor in nanoscale is demonstrated based upon spin-torque-induced magnetization switching and magnetic-domain-wall motion. Our examples show that memristive effects are quite universal for spin-torque spintronic device at the time scale that explicitly involves the interactions between magnetization dynamics and electronic charge transport. We also proved that the spintronic device can be designed to explore and memorize the continuum state of current and voltage based on interactions of electron and spin transport.
365 citations
TL;DR: In this article, the authors demonstrate memristive response in a thin film of Vanadium Dioxide, which is driven by the insulator-to-metal phase transition typical of this oxide.
Abstract: Memristors are passive circuit elements which behave as resistors with memory. The recent experimental realization of a memristor has triggered interest in this concept and its possible applications. Here, we demonstrate memristive response in a thin film of Vanadium Dioxide. This behavior is driven by the insulator-to-metal phase transition typical of this oxide. We discuss several potential applications of our device, including high density information storage. Most importantly, our results demonstrate the potential for a new realization of memristive systems based on phase transition phenomena.
TL;DR: For the first time, memristor-based chaotic circuits have been derived from the canonical Chua’s circuit, and these circuits present opportunities for developing applications under the constraints of scalability and low power.
Abstract: Ever since its physical fabrication in 2008, the memristor has been promising in the fields of nanoelectronics, computer logic and neuromorphic computers. Taking advantage of the circuit properties of the memristor, this paper proposes memristor-based chaotic circuits. For the first time, memristor-based chaotic circuits have been derived from the canonical Chua’s circuit. These circuits present opportunities for developing applications under the constraints of scalability and low power. They also provide a memristor-based framework for secure communications with chaos.
TL;DR: In this paper, the authors demonstrate memristive response in a thin film of vanadium dioxide, which is driven by the insulator-to-metal phase transition typical of this oxide and discuss details of this form of phase-change memristance and potential applications of their device.
Abstract: Memristors are passive circuit elements which behave as resistors with memory. The recent experimental realization of a memristor has triggered interest in this concept and its possible applications. Here, we demonstrate memristive response in a thin film of vanadium dioxide. This behavior is driven by the insulator-to-metal phase transition typical of this oxide. We discuss details of this form of phase-change memristance and potential applications of our device. Most importantly, our results demonstrate the potential for a realization of memristive systems based on phase-transition phenomena.
TL;DR: The digitally configured memristor crossbars were used to perform logic functions, to serve as a routing fabric for interconnecting the FETs and as the target for storing information.
Abstract: Memristor crossbars were fabricated at 40 nm half-pitch, using nanoimprint lithography on the same substrate with Si metal-oxide-semiconductor field effect transistor (MOS FET) arrays to form fully integrated hybrid memory resistor (memristor)/transistor circuits. The digitally configured memristor crossbars were used to perform logic functions, to serve as a routing fabric for interconnecting the FETs and as the target for storing information. As an illustrative demonstration, the compound Boolean logic operation (A AND B) OR (C AND D) was performed with kilohertz frequency inputs, using resistor-based logic in a memristor crossbar with FET inverter/amplifier outputs. By routing the output signal of a logic operation back onto a target memristor inside the array, the crossbar was conditionally configured by setting the state of a nonvolatile switch. Such conditional programming illuminates the way for a variety of self-programmed logic arrays, and for electronic synaptic computing.
02 Nov 2009
TL;DR: The fundamental electrical properties of memristor devices are characterized by encapsulating them into a set of compact closed-form expressions and derivations are investigated to investigate the design of read and write circuits and analyze data integrity and noise-tolerance issues.
Abstract: The search for new nonvolatile universal memories is propelled by the need for pushing power-efficient nanocomputing to the next higher level. As a potential contender for the next-generation memory technology of choice, the recently found "the missing fourth circuit element", memristor, has drawn a great deal of research interests. In this paper, we characterize the fundamental electrical properties of memristor devices by encapsulating them into a set of compact closed-form expressions. Our derivations provide valuable design insights and allow a deeper understanding of key design implications of memristor-based memories. In particular, we investigate the design of read and write circuits and analyze data integrity and noise-tolerance issues.
TL;DR: In this paper, a memory-resistor (memristor for short) is used to emulate the synapse behavior of a neural network, which can remember its past dynamical history, store a continuous set of states, and be "plastic" according to the presynaptic and post-synaptic neuronal activity.
Abstract: When someone mentions the name of a known person we immediately recall her face and possibly many other traits. This is because we possess the so-called associative memory - the ability to correlate different memories to the same fact or event. Associative memory is such a fundamental and encompassing human ability (and not just human) that the network of neurons in our brain must perform it quite easily. The question is then whether electronic neural networks - electronic schemes that act somewhat similarly to human brains - can be built to perform this type of function. Although the field of neural networks has developed for many years, a key element, namely the synapses between adjacent neurons, has been lacking a satisfactory electronic representation. The reason for this is that a passive circuit element able to reproduce the synapse behaviour needs to remember its past dynamical history, store a continuous set of states, and be "plastic" according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive off-the-shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behaviour with electronic neural networks.
TL;DR: A design study for a nano-scale crossbar memory system that uses memristors with symmetrical but highly nonlinear current-voltage characteristics as memory elements and simulation results show the feasibility of these writing and reading procedures.
Abstract: We present a design study for a nano-scale crossbar memory system that uses memristors with symmetrical but highly nonlinear current-voltage characteristics as memory elements. The memory is non-volatile since the memristors retain their state when un-powered. In order to address the nano-wires that make up this nano-scale crossbar, we use two coded demultiplexers implemented using mixed-scale crossbars (in which CMOS-wires cross nano-wires and in which the crosspoint junctions have one-time configurable memristors). This memory system does not utilize the kind of devices (diodes or transistors) that are normally used to isolate the memory cell being written to and read from in conventional memories. Instead, special techniques are introduced to perform the writing and the reading operation reliably by taking advantage of the nonlinearity of the type of memristors used. After discussing both writing and reading strategies for our memory system in general, we focus on a 64 x 64 memory array and present simulation results that show the feasibility of these writing and reading procedures. Besides simulating the case where all device parameters assume exactly their nominal value, we also simulate the much more realistic case where the device parameters stray around their nominal value: we observe a degradation in margins, but writing and reading is still feasible. These simulation results are based on a device model for memristors derived from measurements of fabricated devices in nano-scale crossbars using Pt and Ti nano-wires and using oxygen-depleted TiO(2) as the switching material.
TL;DR: In this paper, a rewriteable low-power operation nonvolatile physically flexible memristor device is demonstrated, which is inexpensively fabricated at room temperature by spinning a TiO2 sol gel on a commercially available polymer sheet.
Abstract: A rewriteable low-power operation nonvolatile physically flexible memristor device is demonstrated. The active component of the device is inexpensively fabricated at room temperature by spinning a TiO2 sol gel on a commercially available polymer sheet. The device exhibits memory behavior consistent with a memristor, demonstrates an on/off ratio greater than 10 000 : 1, is nonvolatile for over 1.2 times 106 s, requires less than 10 V, and is still operational after being physically flexed more than 4000 times.
TL;DR: By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of Memristor cellular automaton and can perform more than one function at the same time, that is, it allows multitasking.
Abstract: In this paper, we design a cellular automaton and a discrete-time cellular neural network (DTCNN) using nonlinear passive memristors. They can perform a number of applications, such as logical operations, image processing operations, complex behaviors, higher brain functions, etc. By modifying the characteristics of nonlinear memristors, the memristor DTCNN can perform almost all functions of memristor cellular automaton. Furthermore, it can perform more than one function at the same time, that is, it allows multitasking.
TL;DR: In this article, a SPICE macromodel is described which simulates the electrical behavior of the recently discovered thin-film titanium dioxide (TiO 2 ) memristors.
Abstract: A SPICE macromodel is described which simulates the electrical behaviour of the recently discovered thin-film titanium dioxide (TiO 2 ) memristors. The macromodel allows for fast simulation of circuits that include memristors. The accuracy of the macromodel is demonstrated by comparison with the theoretical behaviour.
02 Oct 2009
TL;DR: It is shown that the memristor, designed in HP laboratories, can be modeled as a first-order memristive system with nonlinear dependence of the time derivative of the state variable on this variable and on the current flowing through.
Abstract: A methodology of SPICE modeling of general memristive, memcapacitative, and meminductive systems is proposed and their special cases such as memristors (MR), memcapacitors (MC), and meminductors (ML) are given. These elements are included alongside the conventional R, C, and L elements in a proposed constellation, which generalizes the hitherto approach when the memristor is regarded as a fourth missing passive element. It is shown that the memristor, designed in HP laboratories, can be modeled as a first-order memristive system with nonlinear dependence of the time derivative of the state variable on this variable and on the current flowing through. The outputs of SPICE analyses are consistent with the hitherto published results.
TL;DR: Assembly of nanoparticles that have sizes below 10 nm, exhibits at room temperature a voltage-current hysteresis with an abrupt and large bipolar resistance switching (R(OFF)/R(ON) approximately 20), and it is observed that such behavior is not restricted to magnetites.
Abstract: Recently a memristor (Chua, L. O. IEEE Trans. Circuit Theory 1971, 18, 507), the fourth fundamental passive circuit element, has been demonstrated as thin film device operations (Strukov, D. B.; Snider, G. S.; Stewart, D. R.; Williams, R. S. Nature (London) 2008, 453, 80; Yang, J. J.; Pickett. M. D.; Li, X.; Ohlberg, D. A. A.; Stewart, D. R.; Williams, R. S. Nat. Nanotechnol. 2008, 3, 429). A new addition to the memristor family can be nanoparticle assemblies consisting of an infinite number of monodispersed, crystalline magnetite (Fe3O4) particles. Assembly of nanoparticles that have sizes below 10 nm, exhibits at room temperature a voltage−current hysteresis with an abrupt and large bipolar resistance switching (ROFF/RON ≈ 20). Interestingly, observed behavior could be interpreted by adopting an extended memristor model that combines both a time-dependent resistance and a time-dependent capacitance. We also observed that such behavior is not restricted to magnetites; it is a general property of nanopart...
TL;DR: Using a memristor emulator, the suggested circuits have been built and their operation has been demonstrated, showing a useful and interesting connection between the three memory elements.
Abstract: We suggest electronic circuits with memristors (resistors with memory) that operate as memcapacitors (capacitors with memory) and meminductors (inductors with memory). Using a memristor emulator, the suggested circuits have been built and their operation has been demonstrated, showing a useful and interesting connection between the three memory elements.
TL;DR: In this article, the performance of an organic memristor was investigated in the context of adaptive signal handling networks, where an optimization of a conducting polymer (polyaniline) in the active channel and better environmental control of fabrication methods led to a large increase in the absolute values of the conductivity in the partially oxydized state of polyanilines and of the on-off conductivity ratio.
Abstract: The combination of memory and signal handling characteristics of a memristor makes it a promising candidate for adaptive bioinspired information processing systems. This poses stringent requirements on the basic device, such as stability and reproducibility over a large number of training/learning cycles, and a large anisotropy in the fundamental control material parameter, in our case the electrical conductivity. In this work we report results on the improved performance of electrochemically controlled polymeric memristors, where optimization of a conducting polymer (polyaniline) in the active channel and better environmental control of fabrication methods led to a large increase both in the absolute values of the conductivity in the partially oxydized state of polyaniline and of the on-off conductivity ratio. These improvements are crucial for the application of the organic memristor to adaptive complex signal handling networks.
02 Oct 2009
TL;DR: This paper proposes and analyzes Spike-Timing-Dependent-Plasticity (STDP) rule for memristor crossbar based spiking neuromorphic networks and can modify the state of nanodevices with regards to pre- and postsynaptic spike timings.
Abstract: Memristor nanodevices have good properties for use as synapses to add dynamic learning to neuromorphic networks implemented in crossbar-based CMOS/Nano hybrids. In this paper, we propose and analyze Spike-Timing-Dependent-Plasticity (STDP) rule for memristor crossbar based spiking neuromorphic networks. The learning method is implemented by using CMOS based neurons which generate two-part spikes similar to biological Action Potentials (APs) and send them to both forward and backward directions along their axon and dendrites, simultaneously. The local learning method can modify the state of nanodevices with regards to pre- and postsynaptic spike timings.
TL;DR: Grazing-incidence X-ray fluorescence measurements were applied for a time-resolved study of an organic memristor conductivity variation mechanism and showed that the variation of the fluorescence intensity of Rb ions is directly connected to the ionic charge transferred between the conducting polymer and the solid electrolyte, which made up the device.
Abstract: Grazing-incidence X-ray fluorescence measurements were applied for a time-resolved study of an organic memristor conductivity variation mechanism. A comparison of these results with electrical measurements has allowed us to conclude that the variation of the fluorescence intensity of Rb ions is directly connected to the ionic charge transferred between the conducting polymer and the solid electrolyte, which made up the device. In addition, the conductivity of the memristor was shown to be a function of the transferred ionic charge.
30 Oct 2009
TL;DR: This paper presents a point-by-point comparison between DRAM and this new RRAM, based on both existent and expected near-term memristor devices, and considers the case of a die-stacked 3D memory that is integrated onto a logic die and evaluates which memory is best suited for the job.
Abstract: The first memristor, originally theorized by Dr. Leon Chua in 1971, was identiffed by a team at HP Labs in 2008. This new fundamental circuit element is unique in that its resistance changes as current passes through it, giving the device a memory of the past system state. The immediately obvious application of such a device is in a non-volatile memory, wherein high- and low-resistance states are used to store binary values. A memory array of memristors forms what is called a resistive RAM or RRAM. In this paper, we survey the memristors that have been produced by a number of different research teams and present a point-by-point comparison between DRAM and this new RRAM, based on both existent and expected near-term memristor devices. In particular, we consider the case of a die-stacked 3D memory that is integrated onto a logic die and evaluate which memory is best suited for the job. While still suffering a few shortcomings, RRAM proves itself a very interesting design alternative to well-established DRAM technologies.
30 Jul 2009
TL;DR: This paper describes a compact model of the spintronic memristor based on the magnetic-domain-wall motion mechanism that can be easily implemented by Verilog-A language and compatible to SPICE-based simulation.
Abstract: The 4th fundamental circuit elements — memristor received significant attentions after a real device was recently demonstrated for the first time. Besides the solid-state thin film memristive device, sprintonic memristor was also invented based on the magnetic technology [5]. In this paper, we describe a compact model of the spintronic memristor based on the magnetic-domain-wall motion mechanism. Our proposed compact model can be easily implemented by Verilog-A language and compatible to SPICE-based simulation. Furthermore, we discuss the corner model generation of spintronic memristors to improve the simulation efficiency of large scale or complex circuitry, e.g., memory array or some analog circuit design. The process variation effects of the model parameters of are considered in the corner model of spintronic memristor.
23 Jul 2009
TL;DR: In this paper, a fine-resolution programmable memristor is achieved by varying the amount of charges flowing or flux across the memristors, which can be used to form a fine resolution programmable resistance without perturbations by parasitic components.
Abstract: This paper demonstrates that memristors can be used to form a fine resolution programmable resistance without perturbations by parasitic components. By exploiting the memristor characteristics, fine resolution programmable memristance is achieved by varying the amount of charges flowing or flux across the memristor. Under the same circuit configuration with same or even less amount of parasitics, memristance is reconfigured with fine resolution by controlling the input pulse width and its frequency. This paper shows an example of pulse programmed memristors and their analog circuit applications.
TL;DR: An all-analogue stored-reference receiver architecture based on memristive devices that computes the correlation of a stored received reference waveform with the modulated data waveforms that could be used as an ultra-low power receiver for UWB signals.
Abstract: Presented is an all-analogue stored-reference receiver architecture based on memristive devices. The proposed structure computes the correlation of a stored received reference waveform with the modulated data waveforms. The reference signal, passed over a tapped delay line, is used to set the conductances of a chain of memristors. Applying a data signal to the so-programmed memristors, a current is driven that corresponds to the correlator output. The principle of operation is shown by computer simulations. If in practice it proves to be feasible, it could be used as an ultra-low power receiver for UWB signals.