SKM 2023 – wissenschaftliches Programm
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DY: Fachverband Dynamik und Statistische Physik
DY 37: Data Analytics of Complex Dynamical Systems (joint session DY/SOE)
DY 37.7: Vortrag
Donnerstag, 30. März 2023, 11:30–11:45, MOL 213
Wave Digital Optimization of a Modified Compact Models of 1T-1R Random Resistive Access Memory Cells — •Bakr Al Beattie1, Max Uhlmann2, Gerhard Kahmen2, and Karlheinz Ochs1 — 1Ruhr-Universität Bochum, Lehrstuhl für digitale Kommunikationssysteme, Bochum, Deutschland — 2Leibniz-Institut für innovative Mikroelektronik, Frankfurt (Oder), Deutschland
Random Resistive Access Memory (RRAM) cells are popular memristive devices that are commonly used in neuromorphic applications. In this context, RRAM cells are usually utilized to embed synaptic plasticity, a property that is exhibited by biological synapses, into analog-based artificial neural networks. However, since RRAM-based technology has yet to reach a state of maturity, circuit designers are usually forced to make use of compact models to avoid dealing with device-to-device variabilities. The Stanford PKU model is a well-established compact model that has been developed to capture the dynamics of 1T-1R RRAM cells. In this contribution, we present a modified compact model, based on the Stanford PKU model, that takes more properties of real RRAM cells into account, such as the RESET voltage shift in multilevel devices. To demonstrate the capabilities of our model, we exploit the wave digital concept to apply a live parameter optimization, which fits the model parameters to a technologically reproducible device from the Leibniz Institute for High Performance Microelectronics (IHP).