Journal
JOURNAL OF COMPUTATIONAL ELECTRONICS
Volume 16, Issue 4, Pages 1121-1143Publisher
SPRINGER
DOI: 10.1007/s10825-017-1101-9
Keywords
Resistive switching memory; Memristor; Emerging memory; Nonvolatile memory; Device modeling; Transport modeling; Compact modeling; In-memory computing; Neuromorphic computing
Funding
- European Research Council (ERC) under the European Union [648635]
- European Research Council (ERC) [648635] Funding Source: European Research Council (ERC)
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The semiconductor industry is currently challenged by the emergence of Internet of Things, Big data, and deep-learning techniques to enable object recognition and inference in portable computers. These revolutions demand new technologies for memory and computation going beyond the standard CMOS-based platform. In this scenario, resistive switching memory (RRAM) is extremely promising in the frame of storage technology, memory devices, and in-memory computing circuits, such as memristive logic or neuromorphic machines. To serve as enabling technology for these new fields, however, there is still a lack of industrial tools to predict the device behavior under certain operation schemes and to allow for optimization of the device properties based on materials and stack engineering. This work provides an overview of modeling approaches for RRAM simulation, at the level of technology computer aided design and high-level compact models for circuit simulations. Finite element method modeling, kinetic Monte Carlo models, and physics-based analytical models will be reviewed. The adaptation of modeling schemes to various RRAM concepts, such as filamentary switching and interface switching, will be discussed. Finally, application cases of compact modeling to simulate simple RRAM circuits for computing will be shown.
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