Doped HfO2 based nanoelectronic memristive devices for self-learning neural circuits and architecture

In this work we introduce a two-terminal memristive device using Mn doped HfO₂. The devices can emulate synaptic behavior based on their transient characteristics. These properties can be exploited to show spike-timing based learning in a network of neurons and synapses. We use the device characteristics to simulate a 4×4 crossbar array of synapses and observe the evolution of the weights over time. The effect of device variability on the performance of synaptic weight update has been examined based on different test conditions of initial randomness and variation in percentage change of strength during spike-timing based updates. Some inferences have been drawn regarding the need of additional circuits for improving reliability of the cross-bar arrays. We believe this study is critical in assessing the design constraints and requirements necessary for integrating memristive devices in crossbars for spike based computations.