Data-driven RRAM device models using Kriging interpolation

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.

Research progress in architecture and application of RRAM with computing-in-memory

“…It was not until 2008 that a prototype of the memristor was successfully prepared by Hewlett-Packard Laboratories. 11 In the time that follows, research studies focus on mathematical models, 12 resistance mechanisms, 13,14 across-array structures, preparation processing, 15 and fabrication materials. 16,17 The memristor used in memory applications is called resistive random-access memory (RRAM).…”

A Comparison of Different Gis-Based Interpolation Methods for Bathymetric Data: Case Study of Bawean Island, East Java

“…Hence, Kriging is generally known as Gaussian Process Regression (GPR). These processes have been developed since the 1940s by D. G. Krige, the South African engineer, and involved reams of applications (Hossen et al, 2022). This approach is frequently utilized in the geochemistry, geology, soil science, and ecological fields of mining and petroleum.…”

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

“…Here, variograms can be exploited to spatially define porosity–permeability distributions, thus providing insights into reservoir geometry 25 . Kriging techniques 26 are also useful for this purpose because they can assist in determining best-linear-unbiased estimates and/or predictions (BLUE/BLUP) 27 . In particular, kriging combined with SGSIM or its truncated modification provides useful spatial computations 28 of petrophysical parameters when constructing 3D geomodels incorporating stochastically derived porosity–permeability information 29 .…”