Data-driven RRAM device models using Kriging interpolation

• Published in: Scientific reports Vol. 12; no. 1; p. 5963
• Main Authors: Hossen, Imtiaz, Anders, Mark A., Wang, Lin, Adam, Gina C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/925/927/1007; Electronic equipment; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Standard deviation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-09556-4

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 TiO x 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.