Nonvolatile memories using two-dimensional materials and high-k oxides have gained attention for their potential to achieve robust analog switching, easy memristive device integration, and low-energy consumption. In this study, we fabricated Pt/TiN/HfO 2 /WS 2 /Pt memristive devices. To implement these devices, a WS 2 film was thermally evaporated under high vacuum conditions followed by HfO 2 growth using atomic layer deposition at 400 °C. Detailed analysis using highresolution transmission electron microscopy and X-ray photoelectron spectroscopy revealed diffusion of W and S atoms within the HfO 2 layer and extraction of oxygen by W atoms, thus resulting in a multilayer structure (HfWO y S x , W x−1 O y S x , and W 1−x O y S x ) with varying ratios of oxygen, tungsten, and sulfur atoms (x and y). The fabricated devices demonstrated consistent and stable analogue switching over numerous cycles, with exceptional endurance (2000 cycles) and retention (10 3 s). They exhibited high cycle-to-cycle consistency, as evidenced by the low-coefficient of variation (3.5% and 4.0% for the set and reset voltages, respectively). By modulating the reset stop voltage, we achieved five-level resistance states, thus making these devices capable of being used in artificial synapses. Furthermore, we observed analog switching with gradual resistance changes under different current compliance conditions by incrementally adjusting the reset−stop voltage. The memristor-based artificial synapses exhibited fundamental synaptic functions, such as long-term potentiation, long-term depression, paired-pulse facilitation, paired-pulse depression, and spiketiming-dependent plasticity for long-term and short-term plasticity. Moreover, we employed a three-layer artificial neural network for image recognition, achieving 94% accuracy using identical pulse amplitudes. These findings highlight the potential of HfO 2 /WS 2 bilayer films, enable controllable analogue switching, and simulate synaptic functions. They hold promise for future data storage memory and neuromorphic computing systems.