The HfOx-based ferroelectric memristor is in the spotlight due to its complementary metal-oxide-semiconductor compatibility and scaling compared to existing perovskite-based ferroelectric memory. However, ferroelectric properties vary depending on the coefficient of thermal expansion of the top electrode, which is caused by strain engineering. When tungsten (W) with a small coefficient of thermal expansion is used as an electrode, the ferroelectric properties are improved, although the reliability is poor due to the diffusion of W atoms. Here, TiN can be used to prevent the diffusion of W. This metal nitride successfully suppresses the leakage current and induces a larger remanent polarization of 19.7 μC cm-2, a smaller coercive voltage of 9.26 V, and a faster switching speed. W/TiN/HAO/n+ Si can also exhibit multi-level characteristics and achieve a 10% read margin in 320 × 320 arrays. Ferroelectrics can also be applied to neuromorphic computing by imitating synaptic properties such as potentiation, depression, paired-pulse facilitation, and excitatory postsynaptic current. Using short-term plasticity, successful implementation in reservoir computing is also realized, achieving 95% classification accuracy. This paper shows promise for the use of memristors in artificial neural networks.