A modeling methodology for resistive RAM based on stanford-PKU model with extended multilevel capability
Reuben JR, Fey D, Wenger C (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 18
Pages Range: 647-656
Article Number: 8741201
DOI: 10.1109/TNANO.2019.2922838
Abstract
Modeling of resistive RAMs (RRAMs) is a herculean task due to its non-linearity. While the exigent need for a model has motivated research groups to formulate realistic models, the diversity in RRAMs' characteristics has created a gap between model developers and model users. This paper bridges the gap by proposing an algorithm by which the parameters of a model are tuned to specific RRAMs. To this end, a physics-based compact model was chosen due to its flexibility, and the proposed algorithm was used to exactly fit the model to different RRAMs, which differed greatly in their material composition and switching behavior. Furthermore, the model was extended to simulate multiple low resistance states (LRS), which is a vital focus of research to increase memory density in RRAMs. The ability of the model to simulate the switching from a high resistance state to multiple LRS was verified by measurements on 1T-1R cells.
Authors with CRIS profile
John Reuben Prabahar
Lehrstuhl für Informatik 3 (Rechnerarchitektur)
Dietmar Fey
Lehrstuhl für Informatik 3 (Rechnerarchitektur)
Involved external institutions
Germany (DE)How to cite
APA:
Reuben, J.R., Fey, D., & Wenger, C. (2019). A modeling methodology for resistive RAM based on stanford-PKU model with extended multilevel capability. IEEE Transactions on Nanotechnology, 18, 647-656. https://doi.org/10.1109/TNANO.2019.2922838
MLA:
Reuben, John Reuben, Dietmar Fey, and Christian Wenger. "A modeling methodology for resistive RAM based on stanford-PKU model with extended multilevel capability." IEEE Transactions on Nanotechnology 18 (2019): 647-656.
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