Hardware edge detection system based on a self-assembled vertically aligned ferroelectric memristor

Real-time image processing faces significant challenges in traditional von Neumann systems due to data transfer bottlenecks between the processor and memory. Neuromorphic computing offers a promising alternative. This study introduces a self-assembled, vertically aligned ferroelectric memristor based on Ba0.6Sr0.4TiO3:(ZrO2)0.92(Y2O3)0.08 (BST:YSZ) for electronic synaptic functions. The memristor demonstrates stable, tunable resistance states that effectively mimic synaptic behavior. Using its neuromorphic architecture, we developed an image edge detection system. The system's output closely matches simulation results, achieving a mean square error of 3.88, a peak signal-to-noise ratio of 27.88 dB, and a structural similarity index of 0.97, indicating high computational accuracy. These findings highlight the potential of ferroelectric memristors for neuromorphic edge computing, particularly in real-time image processing applications.