Modelling Simulation of Resistive RAM devices for Analog and Digital Paradigms: An Experimental Review

Over the years, with the introduction of non-volatile memory devices such as memristors and other CMOS relevant devices, the ability to manufacture hardware which can emulate the properties of a biological synapse has become a realistic possibility. As a result, this has lead to an improvement in the corresponding computer vision and the machine learning applications from a software perspective which can support the advancement in resistive memory technology such as using memristors for neuromorphic applications. However, the increasing complexity of deep learning models has increased the demand for more sophisticated hardware architecture which enables the full potential of such complex networks on devices with resource constraints. In this work, we perform dual ion beam sputtering and also fabricate a metal oxide memristor consisting of the format: Al/ZnO/Al. We found that our memristor displayed characteristics such as bipolar resistive switching characteristics. Moreover, upon investigation, we observed that a partially amorphous thin film of ZnO and the suitable number if vacancies of oxygen help greatly in inducing the memristor to have reliable and stable behaviour in the memory cell regions. Furthermore, upon further investigation, we found that the oxide ions in the lattice can play a significant role in changing in resistance of the memristor. The endurance of the memristor had good retention and reliable voltage difference for over 300 cycles.