We report the development of physics based models for resistive random-access memory (RRAM) devices. The models are based on a generalized memristive system framework and can explain the dynamic resistive switching phenomena observed in a broad range of devices. Furthermore, by constructing a simple subcircuit, we can incorporate the device models into standard circuit simulators such as SPICE. The SPICE models can accurately capture the dynamic effects of the RRAM devices such as the apparent threshold effect, the voltage dependence of the switching time, and multi-level effects under complex circuit conditions. The device and SPICE models can also be readily expanded to include additional effects related to internal state changes, and will be valuable to help in the design and simulation of memory and logic circuits based on resistive switching devices.
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http://dx.doi.org/10.1039/c1nr10557d | DOI Listing |
ACS Nano
January 2025
IBM Research Europe - Zurich, 8803 Rüschlikon, Switzerland.
Devices with a highly nonlinear resistance-voltage relationship are candidates for neuromorphic computing, which can be achieved by highly temperature dependent processes like ion migration. To explore the thermal properties of such devices, Scanning Thermal Microscopy (SThM) can be employed. However, due to the nonlinearity, the high resolution and quantitative method of AC-modulated SThM cannot readily be used.
View Article and Find Full Text PDFRecent Pat Nanotechnol
January 2025
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands.
The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era.
View Article and Find Full Text PDFNanotechnology
January 2025
Department of Physics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang Area, Shanghai 200240, Shanghai, 200240, CHINA.
Both stability and multi-level switching are crucial performance aspects for resistive random-access memory (RRAM), each playing a significant role in improving overall device performance. In this study, we successfully integrate these two features into a single RRAM configuration by embedding Ag-nanoparticles (Ag-NPs) into the TiN/Ta2O5/ITO structure. The device exhibits substantially lower switching voltages, a larger switching ratio, and multi-level switching phenomena compared to many other nanoparticle-embedded devices.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
January 2025
Peter Gruenberg Institut (PGI-7), Forschungszentrum Juelich GmbH, Juelich, Germany.
The thirst for more efficient computational paradigms has reignited interest in computation in memory (CIM), a burgeoning topic that pivots on the strengths of more versatile logic systems. Surging ahead in this innovative milieu, multi-valued logic systems have been identified as possessing the potential to amplify storage density and computation efficacy. Notably, ternary logic has attracted widespread research owing to its relatively lower computational and storage complexity, offering a promising alternative to the traditional binary logic computation.
View Article and Find Full Text PDFLight Sci Appl
January 2025
Department of Electronic Engineering, Tsinghua University, Beijing, China.
The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs).
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