Nanoscale Switching and Degradation of Resistive Random Access Memory Studied by In Situ Electron Microscopy

Arita, Masashi; Tsurumaki‐Fukuchi, Atsushi; Takahashi, Yasuo

doi:10.5772/intechopen.69024

Cited by 1 publication

(2 citation statements)

References 57 publications

(133 reference statements)

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“…However, in conventional ECM or VCM-based memristors, the random formation and rupture of conductive filaments result in significant temporal and spatial variations. [212][213][214][215][216] To F I G U R E  (A) Schematic illustration of the phase-separated oxide (PSO) memristor (left). The set voltage variation of the PSO-memristive device after 1000 current-voltage (I-V) sweeps (middle) and all 32 devices (right).…”

Section: Mechanismmentioning

confidence: 99%

“…However, in conventional ECM or VCM‐based memristors, the random formation and rupture of conductive filaments result in significant temporal and spatial variations. [ 212–216 ] To address these limitations, reducing the stochasticity of filament formation and rupture is critical. Numerous researches have focused on mitigating this randomness.…”

Section: Memristor‐based Artificial Sensory Systemmentioning

confidence: 99%

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Artificial sensory system based on memristive devices

Kwon,

Kim,

Kim

et al. 2023

Exploration

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In the biological nervous system, the integration and cooperation of parallel system of receptors, neurons, and synapses allow efficient detection and processing of intricate and disordered external information. Such systems acquire and process environmental data in real‐time, efficiently handling complex tasks with minimal energy consumption. Memristors can mimic typical biological receptors, neurons, and synapses by implementing key features of neuronal signal‐processing functions such as selective adaption in receptors, leaky integrate‐and‐fire in neurons, and synaptic plasticity in synapses. External stimuli are sensitively detected and filtered by “artificial receptors,” encoded into spike signals via “artificial neurons,” and integrated and stored through “artificial synapses.” The high operational speed, low power consumption, and superior scalability of memristive devices make their integration with high‐performance sensors a promising approach for creating integrated artificial sensory systems. These integrated systems can extract useful data from a large volume of raw data, facilitating real‐time detection and processing of environmental information. This review explores the recent advances in memristor‐based artificial sensory systems. The authors begin with the requirements of artificial sensory elements and then present an in‐depth review of such elements demonstrated by memristive devices. Finally, the major challenges and opportunities in the development of memristor‐based artificial sensory systems are discussed.

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