1,823 research outputs found
Universal Switching Behavior,” in Resistive switching – from fundamental redox-processes to device applications
No full textUniversal set/reset characteristics of metal-oxide resistance switching memories
No full textResistance switching occurs in metal oxides after dielectric breakdown, as a result of localized thermally-activated ion migration and chemical reduction/oxidation. These phenomena form the fundamental basis for the resistive switching memory (RRAM), which is a novel memory device with high scaling potential for future non-volatile memories. The device is programmed/erased by the set/reset operations, consisting of the electrically-induced formation and dissolution of a conductive filament (CF) through the metal oxide layer. Understanding and modeling the set and reset processes is essential for developing device simulation tools and predicting the scalability and reliability of RRAM. This work studies set/reset characteristics for unipolar/bipolar RRAM devices. It is shown that the cell resistance after set and the reset current are universal functions of the compliance current, that is the maximum current flowing during the set operation for the formation of the CF. The universal reset characteristic is described in terms of a universal reset voltage, resulting from a weak dependence of the dissolution temperature on material parameters. Finally, the reliability of RRAM is reviewed focussing on data retention and discussing possible methods to improve resistance stability for non-volatile applications.</jats:p
Understanding phase change memory reliability and scaling by physical models of the amorphous chalcogenide phase
No full textAbstractPhase change memory (PCM) devices are based on the electrically-induced change of phase within an active chalcogenide material. PCM features large resistance window, fast threshold/phase switching and high endurance, thus motivating a broad interest as potential Flash replacement and/or nonvolatile storage class memory. Despite the relatively mature progress of research and technology, there is still a wide debate about the ultimate scaling perspective for PCMs. Structural relaxation, crystallization and noise affecting the amorphous chalcogenide phase need to be addressed by accurate physical models for a realistic scaling projection. This work discusses the scaling of PCM devices in terms of the conduction mechanisms and structural stability of the amorphous chalcogenide phase. Resistance window narrowing, current fluctuations, resistance drift and crystallization in the amorphous phase will be explained by a unified model for thermal excitation of the structure by many-phonon phenomena. The downscaling of the reset current, needed to reduce the cell area in memory arrays, and thermal disturb between adjacent cells during reset will be finally addressed to assess the scaling capability of high-density PCM crossbar architectures.</jats:p
Size-dependent switching and reliability of NiO RRAMs
No full textResistive-switching memory (RRAM) based on the metal-insulator transition in some metal oxides are attracting interest as potential post-Flash nonvolatile memory devices. RRAM offers high capability for geometrical scaling, thanks to its two terminal structure and the ability for 3D stacking, However, the mechanisms of switching and reliability and their dependence on cell scaling are still under debate. This work reviews the state-of-the-art understanding of NiO RRAMs, discussing the dependence of switching and reliability on cell resistance linked to the size and nature of the localized conductive path in the cell.</jats:p
Brain-inspired computing with resistive switching memory (RRAM): Devices, synapses and neural networks
No full textThe human brain can perform advanced computing tasks, such as learning, recognition, and cognition, with extremely low power consumption and low frequency of neuronal spiking. This is attributed to the highly-parallel and the event-driven scheme of computation, where energy is used only when and where it is needed for processing the information. To mimic the human brain, the fundamental challenges are the replication of the time-dependent plasticity of synapses and the achievement of the high connectivity in biological neuron networks, where the ratio between synapses and neurons is around 104. This combination of high computing capability and density scalability can be obtained with the nanodevice technology, notably by resistive-switching memory (RRAM) devices. In this work, the recent advances in RRAM device technology for memory and synaptic applications are reviewed. First, RRAM devices with improved window and reliability thanks to SiOxdielectric layer are discussed. Then, the application of RRAM in neuromorphic computing are addressed, presenting hybrid synapses capable of spike-timing dependent plasticity (STDP). Brain-inspired hardware featuring learning and recognition of input patterns are finally presented
Modeling the universal set/reset characteristics of bipolar RRAM by field- and temperature-driven filament growth
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