Fully Inkjet-Printed Memristors Based on Ag/Poly(4-Vinylphenol)/Ag Structure
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Printed memristors have gained a lot of attention due to their potential for large-area, fast and affordable manufacturing. Among printing techniques, inkjet printing technology offers key advantages for the fabrication of electronic devices, including non-contact deposition, compatibility with flexible substrates, and rapid prototyping. Importantly, it enables the deposition of very thin layers in the nanometer range using solution-processable materials such as polymers, small molecules, metallic nanoparticles and metal oxides, among others. Organic materials in memristors have garnered significant interest due to their chemically tunable functional groups, which can be engineered to modulate the resistive switching mechanism as well as the interfacial interactions between the conductive filament and the switching layer. However, the physical mechanisms governing resistive switching (RS) in organic memristors remain insufficiently understood. In this work, we study the memristive properties of symmetric Ag/cross-linked poly(4-vinylphenol) (cPVP)/Ag structures (Fig. 1(a) and (b)) fabricated using inkjet printing technology. Commercially available Ag nanoparticle-based conductive ink was used for the bottom (BE) and top electrodes (TE), and a custom-formulated polymer served as the dielectric layer. Electrical characterization was performed through current-voltage (I-V) measurements to evaluate the RS behavior. Variability, reliability, and degradation were further analyzed to provide deeper insight into the underlying switching mechanisms. The devices show stable and non-volatile resistive switching over multiple cycles, with well-defined memory windows (Fig. 1(c)). The switching behavior is mainly attributed to the formation and rupture of silver conductive filaments within the polymer layer. These results provide insight into the switching mechanisms of organic dielectric layers, highlighting their potential for applications in non-volatile memory and neuromorphic computing systems.