CMOS Compatible Hf0.5Zr0.5O2 Ferroelectric Tunnel Junctions for Neuromorphic Devices

While machine learning algorithms are becoming more and more elaborate, their underlying artificial neural networks most often still rely on the binary von Neumann computer architecture. However, artificial neural networks access their full potential when combined with gradually switchable artificial synapses. Herein, complementary metal oxide semiconductor-compatible Hf0.5Zr0.5O2 ferroelectric tunnel junctions fabricated by radio-frequency magnetron sputtering are used as artificial synapses. On a single synapse level, their neuromorphic behavior is quantitatively investigated with spike-timing-dependent plasticity. It is found that the learning rate of the synapses mainly depends on the voltage amplitude of the applied stimulus. The experimental findings are well reproduced with simulations based on the nucleation-limited-switching model.