Threshold switching and electrical self-oscillation in niobium oxide films

Liu, Xinjun; Li, Shuai; Nandi, Sanjoy Kumar; Venkatachalam, Dinesh Kumar; Elliman, R. G.

doi:10.1063/1.4963288

Cited by 74 publications

(57 citation statements)

References 53 publications

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“…2b ) and the corresponding average temperatures within the crosspoint area (Fig. 2c ) at different current levels (obtained using a current source) reveal that the temperature at the onset of NDR-1 is ~400 K, and that around NDR-2 is ~1000 K, which coincides with the MIT 1 , 5 , 13 – 17 . The temperature distribution within most of the crosspoint area (Fig.…”

Section: Resultsmentioning

confidence: 69%

“…The repeatable current–voltage curve (Fig. 1c ) obtained by sweeping the applied current exhibits a region of current-controlled NDR (“NDR-1”), wherein the curve is single-valued for any value of current, and has been routinely observed in NbO 2 before 1 , 4 , 5 , 16 . However, at higher currents, this was followed by a rectangular hysteretic region consisting of a pair of sharp NDRs (“NDR-2”), which is neither current-controlled nor voltage-controlled.…”

Section: Resultsmentioning

confidence: 90%

“…Negative differential resistance (NDR) in NbO 2 is a manifestation of local activity that underlies threshold switching, generation of an action potential (in a neuristor), self-oscillations, and chaotic behavior, which are all being intensely researched for potential applications in neuromorphic or non-Boolean computing 1 – 9 . However, for several decades, there has been a controversy on whether the NDR is caused by a low-temperature (usually <500 K) non-linear-transport-driven thermal instability 4 , 10 – 12 or a high-temperature Mott metal-insulator transition (MIT) 1 , 5 , 13 – 17 . This issue is crucial for development of compact predictive models to study behaviors of larger computational systems constructed with NDR elements.…”

Section: Introductionmentioning

confidence: 99%

“…This issue is crucial for development of compact predictive models to study behaviors of larger computational systems constructed with NDR elements. Although accurate modeling of the low-current behavior consisting of the current-controlled NDR has been achieved recently 1 , 4 , 5 , 11 , 12 , a direct physicochemical measurement of the material temperature and localization effects during different NDR events have not been reported.…”

Section: Introductionmentioning

confidence: 99%

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Physical origins of current and temperature controlled negative differential resistances in NbO2

et al. 2017

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Negative differential resistance behavior in oxide memristors, especially those using NbO2, is gaining renewed interest because of its potential utility in neuromorphic computing. However, there has been a decade-long controversy over whether the negative differential resistance is caused by a relatively low-temperature non-linear transport mechanism or a high-temperature Mott transition. Resolving this issue will enable consistent and robust predictive modeling of this phenomenon for different applications. Here we examine NbO2 memristors that exhibit both a current-controlled and a temperature-controlled negative differential resistance. Through thermal and chemical spectromicroscopy and numerical simulations, we confirm that the former is caused by a ~400 K non-linear-transport-driven instability and the latter is caused by the ~1000 K Mott metal-insulator transition, for which the thermal conductance counter-intuitively decreases in the metallic state relative to the insulating state.

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Section: Resultsmentioning

confidence: 69%

Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Physical origins of current and temperature controlled negative differential resistances in NbO2

et al. 2017

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“…The minimal energy consumption as low as ~38 pJ per spike event was achieved. We believe the energy consumption could be further reduced by using a NbO x device with a lower threshold voltage, a smaller V H – V TH window, and a testing circuit with smaller parasitic capacitance 15,55 . To further demonstrate the frequency evolution under continuously increased voltage, a triangular pulse (from 1.9 V to 2.5 V) with 1 V/ms ramp was taken as the input stimuli signal, as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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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Origin of Current‐Controlled Negative Differential Resistance Modes and the Emergence of Composite Characteristics with High Complexity

Liu

Nandi

et al. 2019

Adv Funct Materials

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Current-controlled negative differential resistance has significant potential as a fundamental building block in brain-inspired neuromorphic computing. However, achieving desired negative differential resistance characteristics, which is crucial for practical implementation, remains challenging due to little consensus on the underlying mechanism and unclear design criteria. Here, we report a material-independent model of current-controlled negative differential resistance to explain a broad range of characteristics, including the origin of the discontinuous snap-back response observed in many transition metal oxides. This is achieved by explicitly accounting for a non-uniform current distribution in the oxide film and its impact on the effective circuit of the device, rather than a material-specific phase transition. The predictions of the model are then compared with experimental observations to show that the continuous S-type and discontinuous snap-back characteristics serve as fundamental building blocks for composite behaviour with higher complexity. Finally, we demonstrate the potential of our approach for predicting and engineering unconventional compound behaviour with novel functionality for emerging electronic and neuromorphic computing applications.

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Threshold switching and electrical self-oscillation in niobium oxide films

Cited by 74 publications

References 53 publications

Physical origins of current and temperature controlled negative differential resistances in NbO2

Physical origins of current and temperature controlled negative differential resistances in NbO2

An artificial spiking afferent nerve based on Mott memristors for neurorobotics

Origin of Current‐Controlled Negative Differential Resistance Modes and the Emergence of Composite Characteristics with High Complexity

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