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Register a new userApplicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices
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Added by
Eike Linn
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Highly accurate and predictive models of resistive switching devices are needed to enable future memory and logic design. Widely used is the memristive modeling approach considering resistive switches as dynamical systems. Here we introduce three evaluation criteria for memristor models, checking for plausibility of the I-V characteristics, the presence of a sufficiently non-linearity of the switching kinetics, and the feasibility of predicting the behavior of two anti-serially connected devices correctly. We analyzed two classes of models: the first class comprises common linear memristor models and the second class widely used non-linear memristive models. The linear memristor models are based on Strukovs initial memristor model extended by different window functions, while the non-linear models include Picketts physics-based memristor model and models derived thereof. This study reveals lacking predictivity of the first class of models, independent of the applied window function. Only the physics-based model is able to fulfill most of the basic evaluation criteria.
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We report on resistive switching of memristive electrochemical metallization devices using 3D kinetic Monte Carlo simulations describing the transport of ions through a solid state electrolyte of an Ag/TiO$_{text{x}}$/Pt thin layer system. The ion transport model is consistently coupled with solvers for the electric field and thermal diffusion. We show that the model is able to describe not only the formation of conducting filaments but also its dissolution. Furthermore, we calculate realistic current-voltage characteristics and resistive switching kinetics. Finally, we discuss in detail the influence of both the electric field and the local heat on the switching processes of the device.
We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative example, the series circuit of a binary memristor and capacitor is considered in detail. Some analytical solutions are found. Our work offers a novel analytical/numerical tool for modeling complex stochastic networks, which may find a broad range of applications.
We present a unique application of OxRAM devices in CMOS Image Sensors (CIS) for dynamic range (DR) improvement. We propose a modified 3T-APS (Active Pixel Sensor) circuit that incorporates OxRAM in 1T-1R configuration. DR improvement is achieved by resistive compression of the pixel output signal through autonomous programming of OxRAM device resistance during exposure. We show that by carefully preconditioning the OxRAM resistance, pixel DR can be enhanced. Detailed impact of OxRAM SET-to-RESET and RESET-to-SET transitions on pixel DR is discussed. For experimental validation with specific OxRAM preprogrammed states, a 4 Kb 10 nm thick HfOx (1T-1R) matrix was fabricated and characterized. Best case, relative pixel DR improvement of ~ 50 dB was obtained for our design.
The resistive switching phenomenon in MgO-based tunnel junctions is attributed to the effect of charged defects inside the barrier. The presence of electron traps in the MgO barrier, that can be filled and emptied, locally modifies the conductance of the barrier and leads to the resistive switching effects. A double-well model for trapped electrons in MgO is introduced to theoretically describe this phenomenon. Including the statistical distribution of potential barrier heights for these traps leads to a power-law dependence of the resistance as a function of time, under a constant bias voltage. This model also predicts a power-law relation of the hysteresis as a function of the voltage sweep frequency. Experimental transport results strongly support this model and in particular confirm the expected power laws dependencies of resistance. They moreover indicate that the exponent of these power laws varies with temperature as theoretically predicted.
In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where information is stored in terms of their conductance levels, are especially well suited for in-memory computing. In particular, memristive devices, when organized in a crossbar configuration can be used to perform matrix-vector multiply operations by exploiting Kirchhoffs circuit laws. To explore the feasibility of such in-memory computing cores in applications such as deep learning as well as for system-level architectural exploration, it is highly desirable to develop an accurate hardware emulator that captures the key physical attributes of the memristive devices. Here, we present one such emulator for PCM and experimentally validate it using measurements from a PCM prototype chip. Moreover, we present an application of the emulator for neural network inference where our emulator can capture the conductance evolution of approximately 400,000 PCM devices remarkably well.
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