Spin memristive systems: Spin memory effects in semiconductor spintronics

Pershin, Yuriy V.; Ventra, Massimiliano Di

doi:10.1103/physrevb.78.113309

Cited by 176 publications

(86 citation statements)

References 18 publications

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“…Examples -Several systems have been found to satisfy the above properties [1], [2], [3], [7], [8], [5], [9], and many more are likely to be found which fit this classification. In particular, memristive behavior is a property of thermistors [3], molecular systems [4], spintronic devices [5] and thin film nanostructures [7], [8], [9], as well as numerous examples presented in Ref.…”

Section: Memristive Systemsmentioning

confidence: 99%

“…In particular, memristive behavior is a property of thermistors [3], molecular systems [4], spintronic devices [5] and thin film nanostructures [7], [8], [9], as well as numerous examples presented in Ref. [14].…”

Section: Memristive Systemsmentioning

confidence: 99%

“…Many systems belong to this class, including the thermistor [3] (whose internal state depends on the temperature), molecules whose resistance changes according to their atomic configuration [4], or spintronic devices whose resistance varies according to their spin polarization [5], [6]. Recently, memristive behavior and memory storage have been reported in solid-state TiO 2 thin films [7], [8], where the change in resistance is realized by the ionic motion of oxygen vacancies activated by current flow.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, it should not come as a surprise that many of the above examples refer to nanoscale systems, whose resistance is likely to depend on their state and dynamical history, at least within (possibly very short) times scales dictated by the fundamental state variables that control their operation [5].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

2009

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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.

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Section: Memristive Systemsmentioning

confidence: 99%

Section: Memristive Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

2009

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“…This has resulted in various attempts to build memristor devices based on different underlying physical principles, as well as efforts to understand pre-existing devices in the context of memristive systems. Examples include thin films (Williams 2008;Yang et al 2008;Borghetti et al 2009;Driscoll et al 2009;Pickett et al 2009;), nanoparticle assemblies ), spintronics (Pershin & Di'Ventra 2008) or neurobiological systems (Pershin et al 2009;Perez-Carrasco et al 2010). On the theoretical front, however, the analysis of these systems has been mostly restricted to numerics (i.e.…”

Section: Introductionmentioning

confidence: 99%

Quantitative measure of hysteresis for memristors through explicit dynamics

et al. 2012

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We introduce a mathematical framework for the analysis of the input-output dynamics of externally driven memristors. We show that, under general assumptions, their dynamics comply with a Bernoulli differential equation and hence can be nonlinearly transformed into a formally solvable linear equation. The Bernoulli formalism, which applies to both charge-and flux-controlled memristors when either current or voltage driven, can, in some cases, lead to expressions of the output of the device as an explicit function of the input. We apply our framework to obtain analytical solutions of the i-v characteristics of the recently proposed model of the Hewlett-Packard memristor under three different drives without the need for numerical simulations. Our explicit solutions allow us to identify a dimensionless lumped parameter that combines device-specific parameters with properties of the input drive. This parameter governs the memristive behaviour of the device and, consequently, the amount of hysteresis in the i-v. We proceed further by defining formally a quantitative measure for the hysteresis of the device, for which we obtain explicit formulas in terms of the aforementioned parameter, and we discuss the applicability of the analysis for the design and analysis of memristor devices.

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Photon‐Memristive System for Logic Calculation and Nonvolatile Photonic Storage

Zhang

Meng

Bai

et al. 2020

Adv Funct Materials

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Memristor-based architectures have shown great potential for developing future computing systems beyond the era of von Neumann and Moore's law. However, the monotonous electrical input for dynamic resistance regulation limits the developments of memristors. Here, a concept of a photon-memristive system, which realizes memristance depending on number of photons (optical inputs), is proposed. A detailed theoretical derivation is performed and the memristive characteristics, as stimulated by the optical inputs based on a hybrid system, consisting of a low-dimension photoelectric semiconductor and a ferroelectric substrate are determined. The photon-memristive system is also suitable for nonvolatile photonic memory since it possesses three or more-bit data storage, desirable resistance-change space, and an ON/OFF ratio of nearly 10 7 . The integrated circuit based on several photonmemristive systems also realizes available photon-triggered in-memory computing. The photon-memristive system expands the definition of memristors and emerges as a new data storage cell for future photonic neuromorphic computational architectures.

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Spin memristive systems: Spin memory effects in semiconductor spintronics

Cited by 176 publications

References 18 publications

Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

Quantitative measure of hysteresis for memristors through explicit dynamics

Photon‐Memristive System for Logic Calculation and Nonvolatile Photonic Storage

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