HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks

In this paper, the use of HfO 2 -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 HfO 2 -based OxRAM technology offers good electrical properties in terms of endurance (>10 8 cycles), speed ( 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%).