Memory Switching and Threshold Switching in a 3D Nanoscaled NbO<sub>X</sub> System

Luo, Qing; Zhang, Xumeng; Yu, Jie; Wang, Wei; Gong, Tiancheng; Xu, Xiaoxin; Yin, Jigang; Yuan, Peng; Tai, Lu; Dong, Danian; Lv, Hangbing; Long, Shibing; Liu, Qi; Liu, Ming

doi:10.1109/led.2019.2904279

Cited by 28 publications

(17 citation statements)

References 27 publications

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“…The forming step has previously been attributed to a soft breakdown process that generates a channel of crystalline NbO 2 within the oxide film 41,46,48 , which exhibits Joule-heating-driven NDR that underlies the threshold switching behavior 40,41,49 . During switching, ~2 µA threshold current is observed, which is much lower than devices of NbO 2 /TiN reported previously 47,50 (see Supplementary Fig. 1).…”

Section: Resultscontrasting

confidence: 60%

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An artificial spiking afferent nerve based on Mott memristors for neurorobotics

et al. 2020

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Neuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

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Section: Resultscontrasting

confidence: 60%

“…The memristor devices used in this study are based on a 3D vertical metal–insulator–metal structure similar to those reported by us earlier 47 . The device has a titanium nitride (TiN) top electrode, a niobium oxide (NbO x ) switching layer, and a poly-Si bottom electrode (see Methods for the details of fabrication processes).…”

Section: Resultsmentioning

confidence: 85%

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

et al. 2020

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“…The four voltage values (V th+ , V hold+ , V th− , and V hold− ) increase along with the increase of CC, while the device still kept threshold-switching property even when CC is up to 100 mA. As a comparison, the working current of the other research was low to a high microamps level (Wang et al, 2018;Lee et al, 2020;Gao et al, 2017;Park et al, 2018;Luo et al, 2019), whereas the working current of the Ti/NbO x /Ti/Pt device has been increased by 10 times or more. This indicated that the Ti/ NbO x /Ti/Pt device has the great potential to drive RRAM under ultra-large working current.…”

Section: Resultsmentioning

confidence: 75%

Research on Improving the Working Current of NbOx-Based Selector by Inserting a Ti Layer

Zeng

Tan

et al. 2021

Front. Mater.

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To achieve the highest possible integration storage density in the V-point structure, the working current of the selector in the one-selection one-resistance (1S1R) structure should match with the resistance random access memory (RRAM). In this study, a selector device is designed with a Ti/NbOx/Ti/Pt structure through the magnetron sputtering method and achieves excellent performance of threshold switching under ultra-large compliance current (CC) up to 100 mA. Furthermore, both the switching voltages and the OFF-state resistance of the device demonstrate excellent stability even when CC is increased to a milliampere level, attributed from the existence of metallic NbO in the switching layer. This study provides evidence that a Ti/NbOx/Ti/Pt device has a great potential to drive RRAM in the V-point structure.

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“…1a after electroforming. By the electroforming process, an oxygen-deficient filamentary conducting channel is formed 31 – 34 . Among the filament, a highly oxygen deficient part (i.e., the anode interface) was crystallized to an insulating NbO 2 as the NbO 2 phase is thermodynamically stable.…”

Section: Resultsmentioning

confidence: 99%

Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

Kim

et al. 2021

Nat Commun

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The intrinsic stochasticity of the memristor can be used to generate true random numbers, essential for non-decryptable hardware-based security devices. Here, we propose a novel and advanced method to generate true random numbers utilizing the stochastic oscillation behavior of a NbOx mott memristor, exhibiting self-clocking, fast and variation tolerant characteristics. The random number generation rate of the device can be at least 40 kb s−1, which is the fastest record compared with previous volatile memristor-based TRNG devices. Also, its dimensionless operating principle provides high tolerance against both ambient temperature variation and device-to-device variation, enabling robust security hardware applicable in harsh environments.

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Memory Switching and Threshold Switching in a 3D Nanoscaled NbO_X System

Cited by 28 publications

References 27 publications

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Research on Improving the Working Current of NbOx-Based Selector by Inserting a Ti Layer

Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

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Memory Switching and Threshold Switching in a 3D Nanoscaled NbOX System

Cited by 28 publications

References 27 publications

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Research on Improving the Working Current of NbOx-Based Selector by Inserting a Ti Layer

Self-clocking fast and variation tolerant true random number generator based on a stochastic mott memristor

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Product

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Memory Switching and Threshold Switching in a 3D Nanoscaled NbO_X System