The computational efficiency of artificial intelligence is becoming more and more constrained by the so called Von Neumann bottleneck causing the transition toward analog Compute-in-Memory (CIM) architectures. This requires synaptic devices capable of combining non-volatile storage, high resolution and as linear as possible conductance modulation. This thesis investigates the potential of ultra-thin (43 nm) Y-36 Lithium Niobate (LiNbO_3) films to address these requirements, through devices characterization and system-level benchmarking. Through electrical characterization of Metal-Ferroelectric-Metal devices an optimized pulse protocol was developed to handle the switching dynamics. This approach was able to set 102 distinct conductance states (more than 6-bit precision) within an analog window operating at currents as low as" " 3" " μ"A" .