Binder-Free Synthesis of Nanostructured Amorphous Cobalt Phosphate for Resistive Memory and Artificial Synaptic Device Applications

The rise of artificial intelligence and machine learning demands versatile electronic devices for memory and brain-inspired computing applications. The electronic materials are the backbones of these applications. Considering this, a functional Co_x(PO₄)₂nanomaterial was synthesized for resistive memory and neuromorphic computing applications. The synthesized nanomaterial was well characterized by using X-ray diffraction, Fourier transform infrared spectroscopy, field emission-scanning electron microscopy, and X-ray photoelectron spectroscopy. The fabricated Ag/Co_x(PO₄)₂/ITO device shows bipolar resistive switching and memristive properties. The SET and RESET voltages were analyzed by using different statistical measures, and their distribution was studied by using the Weibull technique. The results suggested that the SET voltages were more uniformly distributed than the RESET voltage. The switching nonlinearity was modeled and predicted by using Holt's exponential smoothing-based statistical time series analysis method. In the case of nonvolatile memory tests, the device shows good endurance (10³cycles) and memory retention (3 × 10⁴s) with excellent memory window (1.7 × 10³) properties. Moreover, the device can mimic the potentiation-depression and spike-timing-dependent plasticity-based Hebbian learning rules, suggesting Co_x(PO₄)₂is a potential nanomaterial for the fabrication of artificial synapse. The detailed analysis of electrical results suggested that the space-charge-limited current-based charge transport was responsible for the device conduction, whereas the formation and rupture of conductive filament(s) were responsible for the resistive switching in the Ag/Co_x(PO₄)₂/ITO memristive device. The results of the present investigation suggested that the Co_x(PO₄)₂nanomaterial is a potential candidate for resistive memory and brain-inspired computing applications.