HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks

In this paper, the use of HfO₂-based oxide-based resistive memory (OxRAM) devices operated in binary mode to implement synapses in a convolutional neural network (CNN) is studied. We employed an artificial synapse composed of multiple OxRAM cells connected in parallel, thereby providing synaptic efficacies. Electrical characterization results show that the proposed HfO₂-based OxRAM technology offers good electrical properties in terms of endurance (>10⁸ cycles), speed (<;10 ns), and low energy (<;10 pJ), and thus being well suited for neuromorphic applications. A device physical model is developed in order to study the variability of the resistance as a function of the stochastic position of oxygen vacancies in 3-D. Finally, the proposed binary OxRAM synapse has been used for CNN system-level simulations. High accuracy (recognition rate > 98%) is demonstrated for a complex visual pattern recognition application. We demonstrated that the resistance variability and the reduced memory window of the OxRAM cells when operated at extremely low programming conditions (<;10 pJ per switching event) have a small impact on the performances of proposed OxRAM-based CNN (recognition rate 94%).