Conductance quantization in memristive devices with electrodeposited Prussian blue-based dielectrics
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Memristors Prussian blue Resistive memory
Fecha
2025-11-12Referencia bibliográfica
A. Cantudo et al. Materials Science in Semiconductor Processing 203 (2026) 110253. doi:10.1016/j.mssp.2025.110253
Patrocinador
MCIN/AEI/10.13039/501100011033 PID2022-139586NB-C44, RYC2022-035618-I; FEDER, EU; FSE+; Deutsche Forschungsgemeinschaft (No. 531524052)Resumen
Identifying new, scalable materials for memristive devices is critical to advance next-generation memory and
neuromorphic technologies. In this context, electrodeposited Prussian Blue (PB), a mixed-valence iron hex
acyanoferrate compound, is emerging as a highly promising candidate due to its low-cost synthesis, CMOS
compatibility, and rich redox chemistry. Here, we report both experimental evidence and theoretical modeling of
conductance quantization in memristive devices employing PB as the active dielectric layer. PB thin films were
synthesized via electrodeposition and integrated into a conventional metal–insulator–metal (MIM) structure (Ag/
PB/Au), which exhibits robust and reproducible resistive switching behavior. Notably, we observe quantized
conductance steps at integer and half-integer multiples of the quantum of conductance (G
0
=2e2/h), indicative
of atomic-scale filament formation and ballistic electron transport. To interpret these findings, we use a quantum
transport model based on the finite-bias Landauer formalism, incorporating a series resistance and a non-ideality
parameter (
α
), which successfully reproduces the experimental I V characteristics. An algorithm is also
introduced to extract the model parameters directly from measured data. The emergence of quantized states is
attributed to the properties of PB due to its open-framework structure, mixed Fe2/Fe3 valence, and reversible
ionic mobility, which allow the formation of atomic conduction channels. These results highlight the potential of
PB-based memristors for multilevel memory storage and neuromorphic computing, while offering a scalable,
CMOS-compatible, and sustainable materials platform.





