Engineering UGR-VCMTCF RRAM Model for Adjusting to TiN/Ti/HfO<sub>2</sub>/W Devices

RRAMs operate based on resistive switching phenomena within several structuring technologies, enabling reversible switching between high and low resistance states with electrical stimuli. UGR-VCMTCF model developed at Granada University, which is based on the Stanford-PKU model, extends its flexibility. In this paper, we improve this model to adjust the switching behavior to a real $15 \times 15 \mu \mathbf{m}^{2} \mathbf{T i N} / \mathbf{T i} / \mathbf{H f O}_{2} / \mathbf{W}$ RRAM sample device from IMB CNM by adding some conditions (on circuit level as well as in the model itself). Our improvements fit the nominal characteristics of the device, introduces more flexibility in some parameters and modifies the variability scheme. The comparison between the empirical and the simulation outcomes demonstrate a better match.

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Published
2024-11-13
DOI
https://doi.org/10.1109/dcis62603.2024.10769120
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

Engineering UGR-VCMTCF RRAM Model for Adjusting to TiN/Ti/HfO2/W Devices

Daniel Arumí, Antonio Calomarde, Salvador Manich, V. Mahboubi et al.
Advanced Memory and Neural Computing
article

Engineering UGR-VCMTCF RRAM Model for Adjusting to TiN/Ti/HfO2/W Devices

Daniel Arumí, Antonio Calomarde, Salvador Manich, V. Mahboubi, R. Rodríguez, Álvaro Gómez Gómez
article en

Abstract

RRAMs operate based on resistive switching phenomena within several structuring technologies, enabling reversible switching between high and low resistance states with electrical stimuli. UGR-VCMTCF model developed at Granada University, which is based on the Stanford-PKU model, extends its flexibility. In this paper, we improve this model to adjust the switching behavior to a real $15 \times 15 \mu \mathbf{m}^{2} \mathbf{T i N} / \mathbf{T i} / \mathbf{H f O}_{2} / \mathbf{W}$ RRAM sample device from IMB CNM by adding some conditions (on circuit level as well as in the model itself). Our improvements fit the nominal characteristics of the device, introduces more flexibility in some parameters and modifies the variability scheme. The comparison between the empirical and the simulation outcomes demonstrate a better match.

Universitat Politècnica de Catalunya (ES)
Affordable and clean energy
Openalex Percentile: Top 81%
Advanced Memory and Neural Computing
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