2023
DOI: 10.1021/acsaelm.3c00131
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Trade-off between Gradual Set and On/Off Ratio in HfOx-Based Analog Memory with a Thin SiOx Barrier Layer

Abstract: HfO x -based synapses are widely accepted as a viable candidate for both in-memory and neuromorphic computing. Resistance change in oxide-based synapses is caused by the motion of oxygen vacancies. HfO x -based synapses typically demonstrate an abrupt nonlinear resistance change under positive bias application (set), limiting their viability as analog memory. In this work, a thin barrier layer of AlO x or SiO x is added to the bottom electrode/ oxide interface to slow the migration of oxygen vacancies. Electri… Show more

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Cited by 4 publications
(2 citation statements)
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“…A detailed explanation of the functions used to define the filament shape is available in. [78] The resistive switching process of the devices was dictated by three primary factors: the oxygen vacancy (V o ) concentration gradient, the local electric field, and the local thermal field influenced by Joule heating. A previously published model proposed by Ielmini et al [79] posits that the switching process could be understood by resolving three key governing equations, specifically the drift and diffusion continuity equation, the current conservation equation, and the Fourier Joule heating equation, denoted as Equations 1-3, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…A detailed explanation of the functions used to define the filament shape is available in. [78] The resistive switching process of the devices was dictated by three primary factors: the oxygen vacancy (V o ) concentration gradient, the local electric field, and the local thermal field influenced by Joule heating. A previously published model proposed by Ielmini et al [79] posits that the switching process could be understood by resolving three key governing equations, specifically the drift and diffusion continuity equation, the current conservation equation, and the Fourier Joule heating equation, denoted as Equations 1-3, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize the field of seizure detection by automating the analysis of EEG recordings, thereby drastically reducing the associated time and cost [ 15 , 16 ]. By leveraging advanced algorithms capable of identifying complex patterns in large datasets [ 17 , 18 , 19 , 20 , 21 ], ML-based systems can achieve high levels of accuracy. In fact, in some instances, ML surpasses human performance.…”
Section: Introductionmentioning
confidence: 99%