RESUMO
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.
RESUMO
Temperature increases during dielectrophoresis (DEP) can affect the response of biological entities, and ignoring the effect can result in misleading analysis. The heating mechanism of a DEP device is typically considered to be the result of Joule heating and is overlooked without an appropriate analysis. Our experiment and analysis indicate that the heating mechanism is due to the dielectric loss (Debye relaxation). A temperature increase between interdigitated electrodes (IDEs) has been measured with an integrated micro temperature sensor between IDEs to be as high as 70 °C at 1.5 MHz with a 30 Vpp applied voltage to our ultra-low thermal mass DEP device. Analytical and numerical analysis of the power dissipation due to the dielectric loss are in good agreement with the experiment data.