2022
DOI: 10.1007/s10853-022-07067-1
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Impact of oxygen concentration at the HfOx/Ti interface on the behavior of HfOx filamentary memristors

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Cited by 10 publications
(7 citation statements)
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“…1,2 Taking inspiration from biological cognition, neuromorphic computing aims to enable artificial intelligence to perform closer to biological cognition for complex tasks such as self-driving, language translation, pattern and speech recognition, and realtime health monitoring. 3−10 Two-terminal memristors, 11,12 which use adaptive oxides such as TiO x , 13 HfO x , 14 TaO x , 15 AlO x , 16 and NiO 17 as the active layer, exhibit a change in resistance with the application of bias and are being considered for both non-volatile inmemory and brain-inspired neuromorphic computing. Among these, HfO x devices are attractive because of their CMOS compatibility, scalability (<10 nm), fast switching (∼ns), excellent switching endurance (>10 10 cycles), and data retention (10 years).…”
Section: Introductionmentioning
confidence: 99%
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“…1,2 Taking inspiration from biological cognition, neuromorphic computing aims to enable artificial intelligence to perform closer to biological cognition for complex tasks such as self-driving, language translation, pattern and speech recognition, and realtime health monitoring. 3−10 Two-terminal memristors, 11,12 which use adaptive oxides such as TiO x , 13 HfO x , 14 TaO x , 15 AlO x , 16 and NiO 17 as the active layer, exhibit a change in resistance with the application of bias and are being considered for both non-volatile inmemory and brain-inspired neuromorphic computing. Among these, HfO x devices are attractive because of their CMOS compatibility, scalability (<10 nm), fast switching (∼ns), excellent switching endurance (>10 10 cycles), and data retention (10 years).…”
Section: Introductionmentioning
confidence: 99%
“…Two-terminal memristors, , which use adaptive oxides such as TiO x , HfO x , TaO x , AlO x , and NiO as the active layer, exhibit a change in resistance with the application of bias and are being considered for both non-volatile in-memory and brain-inspired neuromorphic computing. Among these, HfO x devices are attractive because of their CMOS compatibility, scalability (<10 nm), fast switching (∼ns), excellent switching endurance (>10 10 cycles), and data retention (10 years) .…”
Section: Introductionmentioning
confidence: 99%
“…[8,9] The control of conductivity by an applied electric field is achieved in the oxide layer by charged oxygen vacancies forming and rupturing localized conductive filament paths for the preferential or undesirable transport of charge carriers. [10,11] Conductive filaments were identified to be the directionally aligned crystalline regions in amorphous HfO 2 consisting of monoclinic and orthorhombic oxygen-deficient phases. [12] Recently, HfO 2 layers with a thickness of a few nanometers demonstrated the potential to be implemented in ferroelectric memory devices.…”
Section: Introductionmentioning
confidence: 99%
“…Various engineered materials have recently been reported as the switching layer in memristive devices. Transition metal oxides (TMO) have been extensively investigated due to their thermally stable structures, excellent stochiometric control, sample preparation methods, and scalability [10][11][12][13][14]. Several studies have focused on TiO 2 -based memristive devices, including symmetric devices with an identical top electrode (TE) and bottom electrode (BE) and asymmetric devices with a different TE and BE, and both convincingly realized TiO 2 as a potential contender to be employed as a switching layer in memristive architectures [15][16][17].…”
Section: Introductionmentioning
confidence: 99%