Frequency-dependent redox-based 1S1R synapse with a short-term plasticity TIO2-based exponential selector

Abstract

Short-term plasticity plays a crucial role in the hardware implementation of artificial neural networks (ANN) as it enables temporal information processing capability. However, the short-term plasticity feature is rather challenging to reproduce from a single non-volatile resistive random-access memory (RRAM) component due to its requirement for a certain degree of volatility. Nonetheless, if the selector in one selector-one RRAM (1S1R) integration demonstrates short-term plasticity, it enables the 1S1R device to perform temporal information processing even in the absence of short-term plasticity in RRAM.In this thesis, an exponential selector of Pt/TiO2/Pt structure is introduced to demonstrate the short-term plasticity feature, which is shown to be dependent on the electrodeoxide interface through plasma treatment, and a microscopic model is proposed to explain the observed feature. Thereafter, the short-term plasticity and nonlinearity of the exponential selector are tuned by modulating the oxygen vacancy defects in the TiO2 layer. As the concentration of oxygen vacancy defects increases, the dominant conduction mechanism of the exponential selector transitions from Schottky emission to Poole-Frenkel emission.Additionally, a 1S1R synaptic device is developed based on the Pt/TiO2/Pt exponential selector and a Pt/HfO2/Ti RRAM structure. The Pt/TiO2/Pt selector with short-term plasticity is integrated not only to suppress the sneak current but also to enable the temporal information processing feature, while the Pt/HfO2/Ti RRAM structure enables the long-term memory capability of the 1S1R synapse. Frequency-dependent multilevel switching is experimentally demonstrated in the 1S1R synaptic device, exhibiting the capability of temporal information processing. Furthermore, a 2x2 crossbar array based on the developed 1S1R device is characterised under the worst-case scenario, demonstrating the potential of using this 1S1R synaptic device in the hardware implementation of ANN.