Variability in Resistive Memories

Abstract

Research and development efforts in the nonvolatile memory arena are focused on a reduced set of innovative components, among which we can include memristors. [1,2] Memristors are expected to be key players in the electronics landscape of the coming years largely because of the powerful applications that stand upon their unique features. [1,[3][4][5] The switching mechanisms behind memristors differ significantly depending on the physical properties of the structures and the materials involved. [1,[3][4][5][6][7] To list some of these mechanisms, we can highlight those devices based on phase-change materials, which can be switched reversibly between amorphous and crystalline phases with different electrical resistivity (phasechange memories, PCMs); [8] devices that take advantage of the magnetic and electrical properties exhibited by some materials with different architectures (magnetic RAMs, MRAMs); [9] also structures where materials with switchable electrical polarization give rise to hysteresis curves of the polarization versus electrical field that can be engineered for storing information (ferroelectric FET, FFET); [10] and, finally, resistive RAMs (RRAMs) where the dielectric conduction properties are altered by means of the internal ion movement and concurrent redox reactions used to generate different resistive states. [1,3,11,12]

Thermal Compact Modeling and Resistive Switching Analysis in Titanium Oxide-Based Memristors

“…An average curve is highlighted in red to let the reader see the I−V curve shape, which looks similar to that of valence change memories based on HfO 2 . 21,28 The set and reset voltages and currents (current levels at the I−V point determined by the corresponding set and reset voltages) have been obtained by making use of different techniques (the numerical details of each procedure are described in the Supporting Information note 1). The set (reset) voltages and currents extracted from the experiments are plotted in Figure 1c,d (e and f).…”

Organic iontronic memristors for artificial synapses and bionic neuromorphic computing

“…1–3 The bottleneck arising from the constant shuttle of data between the processing and memory units not only produces significant energy consumption but also imposes limitations on the computing speed. 4,5 The academia and industry are actively seeking alternative computing architectures to sustain the advancement of computational power, given the termination of Moore's law and the limitations in further transistor miniaturization. 6–8 The most promising alternative is neuromorphic computing, which draws inspiration from the human brain and integrates processing and memory into a unified entity.…”

Reliable Memristive Synapses Based on Parylene-MoOx Nanocomposites for Neuromorphic Applications

“…Trade-offs between specific parameters, such as retention and endurance, have also been revealed . The reason lies in the stochastic nature of the RS in the materials used in memristors . Thus, finding optimal materials to fabricate analog memristors suitable for use in NCSs is a critical development area in neuromorphic computing …”

“…20 The reason lies in the stochastic nature of the RS in the materials used in memristors. 21 Thus, finding optimal materials to fabricate analog memristors suitable for use in NCSs is a critical development area in neuromorphic computing. 17 Memristors typically have a sandwich structure of a metal/RS layer/metal.…”