Forming-Free NbO<sub>
                     <i>x</i>
                  </sub>-Based Memristor Enabling Low-Energy-Consumption Artificial Spiking Afferent Nerves

Ding, Yaxin; Yuan, Peng; Yu, Jie; Chen, Yuting; Jiang, Pengfei; Wang, Yuan; Xu, Youqian; Lv, Shuxian; Dang, Zhiwei; Wang, Boping; Xu, Xiaoxin; Gong, Tiancheng; Luo, Qing

doi:10.1109/ted.2022.3191988

Cited by 9 publications

(6 citation statements)

References 26 publications

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“…The energy consumption was estimated by integrating the power over a period of time and dividing the resulting energy by the number of corresponding spikes. We derive a value of about 80 pJ/spike, which is comparable to the value found in other papers for NbO x devices [31,32]. In the future, reducing the external capacitance (here parasitic) could drastically reduce the energy consumption.…”

Section: Fabrication and Methodssupporting

confidence: 86%

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Drouhin

Grelier

et al. 2022

Neuromorph. Comput. Eng.

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Hardware spiking neural networks hold the promise of realizing artificial intelligence with high energy efficiency. In this context, solid-state and scalable memristors can be used to mimic biological neuron characteristics. However, these devices show limited neuronal behaviors and have to be integrated in more complex circuits to implement the rich dynamics of biological neurons. Here we studied a NbO x memristor neuron that is capable of emulating numerous neuronal dynamics, including tonic spiking, stochastic spiking, leaky-integrate-and-fire features, spike latency, temporal integration. The device also exhibits phasic bursting, a property that has scarcely been observed and studied in solid-state nano-neurons. We show that we can reproduce and understand this particular response through simulations using non-linear dynamics. These results show that a single NbO x device is sufficient to emulate a collection of rich neuronal dynamics that paves a path forward for realizing scalable and energy-efficient neuromorphic computing paradigms.

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Section: Fabrication and Methodssupporting

confidence: 86%

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Drouhin

Grelier

et al. 2022

Neuromorph. Comput. Eng.

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“…The oscillatory neural network (ONN) is a neuromorphic computing architecture based on relaxation oscillators for the application of image segmentation and pattern recognition [3][4][5][6]. An ONN is modeled on the synchronous oscillatory behavior observed in biological brains, and the ONN circuit consists of oscillatory neurons and synaptic devices [7,8]. In an ONN, the relaxation oscillator acts as an oscillatory neuron that generates multiple pulses with the oscillating frequency and phase.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of temperature-modulated NbO₂-based relaxation oscillator via interfacial and bulk treatments

et al. 2023

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This work demonstrates oscillation frequency modulation in a NbO2-based relaxation oscillator device, in which the oscillation frequency increases with operating temperature and source voltage, and decreases with load resistance. An annealing-induced oxygen diffusion at 373 K was carried out to optimize the stoichiometry of the bulk NbO2 to achieve consistent oscillation frequency shift with device temperature. The device exhibits stable self-sustained oscillation in which the frequency can be modulated between 2 MHz and 33 MHz, and a wider operating voltage range can be obtained. An additional surface treatment step was employed during fabrication to reduce the surface roughness of the bottom electrode and to remove surface contaminants that affect the interfacial properties of the device. The device frequency tunability coupled with high oscillating frequency and high endurance capability of more than 1.5×108 cycles indicate that the Pt/NbO2/Pt device is particularly suitable for applications in oscillatory neural network.

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“…Materials exhibiting both RSs have been observed to depend on the bias voltage, thickness or a change in the electrode [ 13 , 14 , 15 , 16 ]. Transition metal oxides such as WO 3 , Nb 2 O 5 , TaO x and HfO x are the most common type of active materials in analog RS memristors [ 8 , 17 , 18 , 19 , 20 ]. Additionally, the combination of the advantages of resistive random-access memory and a ferroelectric field-effect transistor has been reported in Hf 0.5 Zr 0.5 O 2 films for its application to high-accuracy on-chip deep neural networks [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Digital and Analog Resistive Switching Behavior in Si-NCs Embedded in a Si/SiO2 Multilayer Structure for Neuromorphic Systems

et al. 2023

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In this work, we report the digital and analog resistive-switching (RS) characteristics in a memristor based on silicon nanocrystals (Si-NCs) integrated into a complementary metal-oxide-semiconductor (MOS) structure. Si-NCs with a diameter of 5.48 ± 1.24 nm embedded in a SiO2/Si-NCs/SiO2 multilayer structure acts as an RS layer. These devices exhibit bipolar RS with an intermediate resistance step during SET and RESET processes, which is believed to lie in the Si-NCs layer acting as charge-trapping nodes. The endurance studies of about 70 DC cycles indicate an ON/OFF ratio of ~106 and a retention time larger than 104 s. Long-term potentiation (LTP, −2 V) and long-term depression (LTD, +4 V) are obtained by applying consecutive identical pulse voltages of 150 ms duration. The current value gradually increases/decreases (LTP/LTD) as the pulse number increases. Three consecutive identical pulses of −2 V/150 ms (LTP) separated by 5 and 15 min show that the last current value obtained at the end of each pulse train is kept, confirming an analog RS behavior. These characteristics provide a possible way to mimic biological synapse functions for applications in neuromorphic computing in Si-NCs-based CMOS structures.

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Forming-Free NbO_x-Based Memristor Enabling Low-Energy-Consumption Artificial Spiking Afferent Nerves

Cited by 9 publications

References 26 publications

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Enhancement of temperature-modulated NbO₂-based relaxation oscillator via interfacial and bulk treatments

Digital and Analog Resistive Switching Behavior in Si-NCs Embedded in a Si/SiO2 Multilayer Structure for Neuromorphic Systems

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Forming-Free NbO x -Based Memristor Enabling Low-Energy-Consumption Artificial Spiking Afferent Nerves

Cited by 9 publications

References 26 publications

Characterization and modeling of spiking and bursting in experimental NbO x neuron

Characterization and modeling of spiking and bursting in experimental NbO x neuron

Enhancement of temperature-modulated NbO2-based relaxation oscillator via interfacial and bulk treatments

Digital and Analog Resistive Switching Behavior in Si-NCs Embedded in a Si/SiO2 Multilayer Structure for Neuromorphic Systems

Contact Info

Product

Resources

About

Forming-Free NbO_x-Based Memristor Enabling Low-Energy-Consumption Artificial Spiking Afferent Nerves

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Characterization and modeling of spiking and bursting in experimental NbO_x neuron

Enhancement of temperature-modulated NbO₂-based relaxation oscillator via interfacial and bulk treatments