Fabrication and Modelling of Zinc Oxide thin film Memristor for Digital Applications

With the introduction of machine learning and deep learning, the world of artificial intelligence has experienced a drastic improvement in the performance benchmarks on various challenging tasks. These tasks can range from heart rate detection for healthcare applications of AI, to more traditional applications of AI such as object detection, speech processing and autonomous driving. 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.