Multi-level control of conductive nano-filament evolution in HfO<sub>2</sub> ReRAM by pulse-train operations

Zhao, Liang; Chen, H. Y.; Wu, Shuo‐Jye; Jiang, Zizhen; Yu, Shimeng; Hou, Tuo‐Hung; Wong, H.-S. Philip; Nishi, Yoshio

doi:10.1039/c4nr00500g

Cited by 143 publications

(93 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The memristive switching from high-resistance state to low-resistance state could be attributed to CF formation at the grain boundaries containing a high concentration of oxygen vacancies 33 . On the other hand, electric field could play an important role in switching from low-resistance state to high-resistance state 34 . It was suggested that electrical pulses lead to a progressive narrowing of the CF, and finally a gap is formed and the memristor switches to the high-resistance state 33,34 .…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, electric field could play an important role in switching from low-resistance state to high-resistance state 34 . It was suggested that electrical pulses lead to a progressive narrowing of the CF, and finally a gap is formed and the memristor switches to the high-resistance state 33,34 .…”

Section: Resultsmentioning

confidence: 99%

“…1). The conductive filament (CF) model can be used to explain the resistance change in the HfO 2 -based resistive memristors 33,34 . Considering the bipolar nature of the memristor, the formation or rupture of some CF consisting of oxygen vacancies are responsible for the resistance change 35 .…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Associative memory realized by a reconfigurable memristive Hopfield neural network

et al. 2015

View full text Add to dashboard Cite

Although synaptic behaviours of memristors have been widely demonstrated, implementation of an even simple artificial neural network is still a great challenge. In this work, we demonstrate the associative memory on the basis of a memristive Hopfield network. Different patterns can be stored into the memristive Hopfield network by tuning the resistance of the memristors, and the pre-stored patterns can be successfully retrieved directly or through some associative intermediate states, being analogous to the associative memory behaviour. Both single-associative memory and multi-associative memories can be realized with the memristive Hopfield network.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Associative memory realized by a reconfigurable memristive Hopfield neural network

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The main concept underlying the multi-level cell (MLC) operation of RRAM is the tunability of the filament geometry by changing the maximum power dissipation through the device during the SET and RESET processes. Zhao et al [26] have noted that MLC operation can be used for synaptic weights by programming the device with different pulse amplitudes. This causes the device to latch onto different levels of resistance as it is undergoing the RESET transition.…”

Section: B Rram Synapse: Multi-level Cell Operationmentioning

confidence: 99%

Oscillatory Neural Networks Based on TMO Nano-Oscillators and Multi-Level RRAM Cells

Jackson

Sharma

Bain

et al. 2015

IEEE J. Emerg. Sel. Topics Circuits Syst.

View full text Add to dashboard Cite

In massively parallel computational tasks, such as pattern recognition, conventional computing architectures have insufficient power efficiency for energy constrained environments. This has made alternative architectures, such as neuromorphic computing, increasingly attractive. Oscillatory neural networks (ONNs) are one promising architecture, but efficient hardware implementations have been limited by shortcomings in CMOS technology, specifically in the efficient implementation of oscillators and synaptic weights. The authors have recently demonstrated that metal-oxide based resistive switching (RRAM) structures can be engineered to create low-power, scalable, voltage-controlled oscillators that utilize inherent meta-stability in the device. This work proposes an RRAM-based ONN that couples oscillatory "neurons" through weighted "synapses" using oscillator phase as the state-variable. This paper demonstrates a robust architecture using only a few logic gates per neuron to implement phase initialization and locking of these oscillators, and demonstrate their capability to identify stored patterns from noisy inputs. Using measured characteristics of RRAMs as oscillators and programmable resistors, compact models are derived and used to simulate both an 8-neuron and 20-neuron network.Index Terms-Metal-oxide resistive memory, neuromorphic computing, oscillatory neural networks, RRAM-based nano-oscillators.

show abstract

“…The bipolar VCM-type resistive switching relates to a redistribution of oxygen vacancies in the switching filament [1]. Whereas theoretical models predict a gradual reset behavior for VCMtype switching TMOs [2,3], in experiments different reset phenomena are reported: abrupt [4] and gradual, whereas the latter is interesting for neuromorphic applications [5,6]. In addition, the measured switching behaviors often deviate from the universal switching rule [7,8] predicting that for a system with linear I(V)-characteristic in the LRS, the voltages of set and reset should be symmetric and the LRS should scale with the current compliance during set.…”

Section: Introductionmentioning

confidence: 96%

Internal Cell Resistance as the Origin of Abrupt Reset Behavior in HfO2-Based Devices Determined from Current Compliance Series

Hardtdegen

Torre

Zhang

et al. 2016

2016 IEEE 8th International Memory Workshop (IMW)

View full text Add to dashboard Cite

The resistive switching behavior in different HfO2/TiO2 nano crossbar structures of 100 x 100 nm² size is analyzed by means of DC voltage sweeps. The devices fabricated from 3 nm thin ALD layers of HfO2 and TiO2 sandwiched between Pt and Hf or Ti electrodes show VCM-type bipolar resistive switching after electroforming. For increased compliance current (cc) during set from 50 µA to 800 µA, the set current runs into self-limitation while the reset behavior changes from gradual to abrupt. A model is defined with an internal resistance being in series with the local resistive switch. A recursive algorithm is applied to the cc series for calculation of the series resistor and evaluation of the intrinsic switching characteristic of HfO2-based cells. The intrinsic LRS turns out to be current compliance controlled and to follow the universal switching rule. Supported by compact modelling, we show that an abrupt reset behavior might arise even for materials with a gradual intrinsic reset characteristic in consequence of an internal series resistor.

show abstract

Multi-level control of conductive nano-filament evolution in HfO₂ ReRAM by pulse-train operations

Cited by 143 publications

References 32 publications

Associative memory realized by a reconfigurable memristive Hopfield neural network

Associative memory realized by a reconfigurable memristive Hopfield neural network

Oscillatory Neural Networks Based on TMO Nano-Oscillators and Multi-Level RRAM Cells

Internal Cell Resistance as the Origin of Abrupt Reset Behavior in HfO2-Based Devices Determined from Current Compliance Series

Contact Info

Product

Resources

About

Multi-level control of conductive nano-filament evolution in HfO2 ReRAM by pulse-train operations

Cited by 143 publications

References 32 publications

Associative memory realized by a reconfigurable memristive Hopfield neural network

Associative memory realized by a reconfigurable memristive Hopfield neural network

Oscillatory Neural Networks Based on TMO Nano-Oscillators and Multi-Level RRAM Cells

Internal Cell Resistance as the Origin of Abrupt Reset Behavior in HfO2-Based Devices Determined from Current Compliance Series

Contact Info

Product

Resources

About

Multi-level control of conductive nano-filament evolution in HfO₂ ReRAM by pulse-train operations