Novel Defects-Trapping ${\rm TaO}_{\rm X}/{\rm HfO}_{\rm X}$ RRAM With Reliable Self-Compliance, High Nonlinearity, and Ultra-Low Current

Chen, Yu‐Sheng; Lee, Heng-Yuan; Chen, Pang-Shiu; Chen, Wei-Su; Tsai, Kuang‐Lei; Gu, Pei-Yi; Wu, Tung Ying; Tsai, Chen-Han; Rahaman, S. Z.; Lin, Yu-Wei; Chen, Frederick; Tsai, Ming-Jinn; Ku, Tzu–Kun

doi:10.1109/led.2013.2294375

Cited by 53 publications

(26 citation statements)

References 7 publications

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“…The experimental results (reduced I Reset , oxide dependent LRS, similar variability for LRS and HRS) may be explained by bulk switching and conduction mechanisms rather than filamentary ones (Chen et al, 2014; Goux et al, 2014) when very low I CC (<20 μA) are used. We believe that in this case the current conduction in the LRS is dominated by trap-assisted tunneling as is the case for the HRS (Wong et al, 2012).…”

Section: Oxram Electrical Device Analysismentioning

confidence: 91%

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

et al. 2016

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In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (<1μs) enables real-time spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (<75 pJ) synapses. Synaptic weights are modulated through the application of an online learning strategy inspired by biological Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

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Section: Oxram Electrical Device Analysismentioning

confidence: 91%

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

et al. 2016

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“…The nitrogen‐rich SiN x layer at the Ta/SiN x interface as shown in Figure c is a series resistor that prevents the formation of a continuous filament. This interface resistor and the dendric Si‐DB conductive channel cause the self‐compliance behavior in all the samples …”

Section: Resultsmentioning

confidence: 99%

Influence of Nitrogen Concentration on Self‐Compliance Resistive Switching in Ta/SiN_x/Pt RRAM Devices

Gao

Jiang

Zhang

et al. 2018

Physica Status Solidi (a)

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In this paper, the resistive switching phenomena in CMOS-compatible Ta/ SiN x /Pt devices with different nitrogen concentrations are investigated. The SiN x resistance random-access memory (RRAM) devices show self-compliance RS characteristics with low operation voltage. This paper suggests that a dendric Si dangling-bond conductive channel and a nitrogen-rich SiN x layer formed at the Ta/SiN x interface are responsible for the self-compliance behavior. Lower operation current and energy consumption are achieved by increasing the nitrogen concentration of the SiN x films, which can be ascribed to increase of the band gap induced by the composition variation.

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“…The LRS resistances of devices increased gradually with the increasing duration of plasma oxidation (Table 1). In other words, the devices were loaded with a layer of Ta 2 O 5 with higher resistance after plasma oxidation [27]. These factors resulted in a gradual reduction of I reset and finally I reset decreased to approximately 40 A.…”

Section: The Drastic Reduction Of Reset Current and Improvement Of Dementioning

confidence: 99%

Drastic reduction of RRAM reset current via plasma oxidization of TaOx film

Chen

Feng

Bae

2015

Applied Surface Science

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Novel Defects-Trapping ${\rm TaO}_{\rm X}/{\rm HfO}_{\rm X}$ RRAM With Reliable Self-Compliance, High Nonlinearity, and Ultra-Low Current

Cited by 53 publications

References 7 publications

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

Influence of Nitrogen Concentration on Self‐Compliance Resistive Switching in Ta/SiN_x/Pt RRAM Devices

Drastic reduction of RRAM reset current via plasma oxidization of TaOx film

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Novel Defects-Trapping ${\rm TaO}_{\rm X}/{\rm HfO}_{\rm X}$ RRAM With Reliable Self-Compliance, High Nonlinearity, and Ultra-Low Current

Cited by 53 publications

References 7 publications

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting

Influence of Nitrogen Concentration on Self‐Compliance Resistive Switching in Ta/SiNx/Pt RRAM Devices

Drastic reduction of RRAM reset current via plasma oxidization of TaOx film

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Influence of Nitrogen Concentration on Self‐Compliance Resistive Switching in Ta/SiN_x/Pt RRAM Devices