Work

A Modeling Methodology for Resistive RAM Based on Stanford-PKU Model With Extended Multilevel Capability

Year: 2019

Type: article

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.

Source: IEEE Transactions on Nanotechnology

DOI: 10.1109/tnano.2019.2922838

Authors: John Reuben, Dietmar Fey, Christian Wenger

Institutions: Friedrich-Alexander-Universität Erlangen-Nürnberg, Leibniz Institute for High Performance Microelectronics

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: Deutsche Forschungsgemeinschaft

Award: Integrierte MemristorBasierte RechnerArchitekturen (IMBRA)