Variability analysis in memristors based on electrodeposited prussian blue
This work presents a comprehensive analysis of the variability and reliability of the resistive switching (RS) behavior in Prussian Blue (a mixed-valence iron(III/II) hexacyanoferrate compound) thin films, used as the active layer. These films are fabricated through a simple and scalable electrochemical process, and exhibit robust bipolar resistive switching, making them suitable both for neuromorphic computing applications and hardware cryptography. A detailed statistical evaluation was conducted over 100 consecutive switching cycles using multiple parameter extraction techniques to assess cycle-to-cycle (C2C) variability in key RS parameters, including set/reset voltages and corresponding currents. One and two-dimensional coefficients of variation (1DCV and 2DCV) were calculated to quantify variability and identify application potential. Results demonstrate moderate variability compatible with neuromorphic computing and cryptographic functionalities, including physical unclonable functions and true random number generation. These findings position Prussian Blue-based memristors as promising candidates for low-cost, stable, and multifunctional memory. • Memristors based on Prussian blue materials have been fabricated and measured. • A comprehensive analysis of resistive switching parameters is presented. • The statistical study shows the device appropriateness for neuromorphic computing.
Authors
- Lindiomar Borges de Avila (ORCID: https://orcid.org/0000-0001-9039-2498)
- J.B. Roldán (ORCID: https://orcid.org/0000-0003-1662-6457)
- Christian K. Müller
- Marco Antonio Villena (ORCID: https://orcid.org/0000-0001-5547-3380)
- Flavio Abreu Araujo (ORCID: https://orcid.org/0000-0001-7157-3197)
- D. Maldonado (ORCID: https://orcid.org/0000-0002-3475-9440)
- A. Cantudo (ORCID: https://orcid.org/0009-0001-6973-7890)
Institutions
- Universidad de Granada (ES)
- Universidad Rey Juan Carlos (ES)
- Westsächsische Hochschule Zwickau (DE)
- UCLouvain (BE)
Publication Details
- Journal
- Microelectronic Engineering
- Published
- 2025-06-19
- DOI
- https://doi.org/10.1016/j.mee.2025.112376
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19