Analog HfO2-RRAM Switches for Neural Networks

Resistive random access memories (RRAM) are one of the main constituents of the class of memristive technologies that are today considered very promising in semiconductor industry because of their high potential for several applications ranging from nonvolatile memories to neuromorphic hardware. The latter application is particularly interesting, since bio-inspired electronic systems have the ability to treat ill-posed problems with higher efficiency than conventional computing paradigms. In this work, we focus on HfO2- based RRAM devices and we analyse their switching dynamics in order to reach neuromorphic requirements. We present analogue memristive behaviour in HfO2 RRAM, which allows realizing a simple version of spike timing dependent plasticity learning rule. Finally, the experimental data are used to simulate an unsupervised spiking neuromorphic network for pattern recognition suitable for real-time applications.