Emulating artificial neuron and synaptic properties with SiO <sub>2</sub> -based memristive devices by tuning threshold and bipolar switching effects
Panagiotis Bousoulas, Marianthi Panagopoulou, Nikos Boukos, Dimitris Tsoukalas
Abstract
Abstract The implementation of neuromorphic computations within a fully memristive neural network is considered the holy grail of the artificial intelligence era. In order to attain this goal, it is quite important to develop robust and configurable electronic devices capable of emulating spiking neuronal and synaptic plasticity activities. Along these lines, we report here the direct impact of oxygen concentration as well as of the homo-bilayer material configuration of SiO 2 -conductive bridge memories to the manifestation of tunable threshold and bipolar switching effects. Interestingly, while the bilayer structure of Ag/SiO x /SiO y /TiN ( x < y ) exhibits only bipolar switching effect, the respective single-layer structures of Ag/SiO y /TiN and Ag/SiO x /TiN operate under either threshold switching or both modes. Insights regarding the impact of oxygen concentration into the conducting filament growth process are provided. The manifestation of the two switching modes permits the emulation of various synaptic effects, such as short-term plasticity and long-term plasticity whereas the modulation of the conductance values allows the synaptic weight tuning by controlling the amplitude or the frequency of the triggering signals. Moreover, arbitrary neuron characteristics were obtained from our volatile memory devices without integrating any other auxiliary circuit. Our approach provides valuable insights towards the realization of artificial neural networks from the same material configuration with biological-like dynamic behavior.