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An electronic synaptic device based on HfO<sub>2</sub>TiO<sub>x</sub> bilayer structure memristor with self-compliance and deep-RESET characteristics
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Citations
37
References
2018
Year
We reported on a Ti/HfO<sub>2</sub>/TiO<sub>x</sub>/Pt memristor with self-compliance, deep-RESET characteristics and excellent switching performance, including ultrafast program/erase speed (10 ns), a large memory window (10<sup>3</sup>) and good pulse endurance (10<sup>7</sup> cycles). The self-compliance and deep-RESET characteristics are beneficial for protecting the device from permanent breakdown in both SET and RESET processes especially under the pulse operation mode. In addition to bistable state switching, we also achieved multiple or even continuous conductance state switching under a DC sweep and a pulse-train operation mode in the Ti/HfO<sub>2</sub>/TiO<sub>x</sub>/Pt memristor, which can be seen as a substitution of a biological synapse. The capability of continuous modulation conductance (synaptic weight) in the Ti/HfO<sub>2</sub>/TiO<sub>x</sub>/Pt memristor was investigated and the potentiation and depression characteristics of the synaptic weight could be precisely tuned by the number or amplitude of the input pulse-train. Moreover, clear experimental evidence of short-term plasticity (STP) and long-term plasticity (LTP) in a single memristor was also demonstrated. Increasing the pulse amplitude or width, or decreasing the interval of two adjacent pulses of the input pulse-train resulted in the memristor behavior transitioning from STP to LTP. The realization of those important synaptic functions in the Ti/HfO<sub>2</sub>/TiO<sub>x</sub>/Pt memristor may be suitable for applications in artificial neural systems.
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Experimental Demonstration and Tolerancing of a Large-Scale Neural Network (165 000 Synapses) Using Phase-Change Memory as the Synaptic Weight Element Geoffrey W. Burr, R. M. Shelby, Severin Sidler, IEEE Transactions on Electron Devices Large-scale Neural NetworkEngineeringNeural Networks (Machine Learning)Synaptic TransmissionNeural Network Simulator | 2015 | 904 |
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