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Temperature control of diffusive memristor hysteresis and artificial neuron spiking
Memristive devices are promising elements for energy-efficient neuromorphic computing and future artificial intelligence systems. For diffusive memristors, resistive switching occurs because of the sequential formation and rupture of conduction filaments between device electrodes due to drift and diffusion of silver nanoparticles in the dielectric matrix. This process is governed by the applied electric voltage. Here, both in experiment and in simulations we demonstrate that varying temperature offers an efficient control of memristor states and charges transport in the device. By raising and lowering the device temperature it was shown that the memristive state can be reset, even if it cannot be done by varying the applied voltage. In addition, a change in the spiking regime was observed when the spiking was generated in the memristive circuit at a constant applied voltage, but different device temperatures. Our simulations demonstrate a good qualitative agreement with the experiments, and help to explain the effects reported.
Neuromorphic memristive circuits to simulate inhibitory and excitatory dynamics of neuron networks: from physiological similarities to deep learning
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