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Data-driven RRAM device models using Kriging interpolation

Researchers propose a two-tier Kriging/GPR approach for modeling jump tables of RRAM devices and tests it comparatively to the traditional binning approach using a broad range of synthetic Gaussian datasets with known mean and standard deviation profiles, as well as experimental data.

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New technical paper from The George Washington University and NIST with support from DARPA and others.

Abstract
“A two-tier Kriging interpolation approach is proposed to model jump tables for resistive switches. Originally developed for mining and geostatistics, its locality of the calculation makes this approach particularly powerful for modeling electronic devices with complex behavior landscape and switching noise, like RRAM. In this paper, a first Kriging model is used to model and predict the mean in the signal, followed up by a second Kriging step used to model the standard deviation of the switching noise. We use 36 synthetic datasets covering a broad range of different mean and standard deviation Gaussian distributions to test the validity of our approach. We also show the applicability to experimental data obtained from TiOx devices and compare the predicted vs. the experimental test distributions using Kolmogorov–Smirnov and maximum mean discrepancy tests. Our results show that the proposed Kriging approach can predict both the mean and standard deviation in the switching more accurately than typical binning model. Kriging-based jump tables can be used to realistically model the behavior of RRAM and other non-volatile analog device populations and the impact of the weight dispersion in neural network simulations.”

Find the open access technical paper here. Published April 2022.

Hossen, I., Anders, M.A., Wang, L. et al. Data-driven RRAM device models using Kriging interpolation. Sci Rep 12, 5963 (2022). https://doi.org/10.1038/s41598-022-09556-4.

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