Variability in HfO2-based memristors under pulse operation
We have studied device-to-device variability in TiN/Ti/HfO 2 /TiN devices under pulse operation. We measured extensively memristive devices that are CMOS integrated with different pulse trains, changing the pulse width and amplitude for groups of more than one hundred devices. The statistical parameters of the measured current were extracted to better understand the device physics under the pulse operation regime. An analytical model to describe synaptic depression and potentiation behavior in the device conductance is introduced, it fits accurately the means of the current data for all the pulse trains under study. In addition, an explanation of the measurements is enlightened with kinetic Monte Carlo simulations that allow the study of resistive switching at the atomic level. Finally, the probability distribution functions of the measured currents in some of the pulses within the pulse series employed are analyzed to extract the probability distribution that works better. A proposal for the implementation of device-to-device variability in the Stanford models is introduced. • Device-to-device variability in memristors under pulse operation is analyzed. • The statistical parameters of the measured current were extracted. • A model for synaptic depression and potentiation behavior is introduced. • The measurements are enlightened with kinetic Monte Carlo simulations. • An implementation of variability in the Stanford model is suggested.
Authors
- JB Roldan
- Fernando Jesus Navas-Gomez (ORCID: https://orcid.org/0000-0001-5047-3349)
- D. Maldonado (ORCID: https://orcid.org/0000-0002-3475-9440)
- Christian Wenger (ORCID: https://orcid.org/0000-0003-3698-2635)
- E. Pérez
- C. Acal
- H. Ortiz
- A. Cantudo
Institutions
- Universidad de Granada (ES)
- Universidad Rey Juan Carlos (ES)
- Brandenburg University of Technology Cottbus-Senftenberg (DE)
- Leibniz Institute for High Performance Microelectronics (DE)
Publication Details
- Journal
- Microelectronic Engineering
- Published
- 2026-01-22
- DOI
- https://doi.org/10.1016/j.mee.2026.112445
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Agencia Estatal de Investigación