Assessment of the variability of the I-V characteristic of HfO2-based resistive switching devices and its simulation using the quasi-static memdiode model

Altres ajuts: acords transformatius de la UABVariability of the conduction characteristics of filamentary-type resistive switching devices or resistive RAMs (RRAMs) is a hot research topic both in academia and industry because it is currently considered one of the major showstoppers for the successful development and application of this technology. In this work, we thoroughly investigate the statistics of the cycle-to-cycle (C2C) variability observed in the experimental current-voltage (I-V) curves of HfO-based memristive structures using the fitdistrplus package for the R language. This exploratory analysis allows us to identify which parametric probability distributions are the most suitable candidates for describing our data. This study involves graphical tools such as the density, skewness-kurtosis (S-K), and quantile-quantile (Q-Q) plots. The analysis is completed with the aid of goodness-of-fit statistics (Kolmogorov-Smirnov, Cramer-von Mises, Anderson-Darling) and criteria (Akaike's and Bayesian). The selected distributions are incorporated into the SPICE script of the quasi-static memdiode model for resistive switching devices and used for simulating uncorrelated C2C variability. Finally, a one-way sensitivity analysis is carried out in order to test the impact of the model parameters variation in the output characteristics of the device

Stochastic resonance effect in binary STDP performed by RRAM devices

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The beneficial role of noise in the binary spike time dependent plasticity (STDP) learning rule, when implemented with memristors, is experimentally analyzed. The two memristor conductance states, which emulate the neuron synapse in neuromorphic architectures, can be better distinguished if a gaussian noise is added to the bias. The addition of noise allows to reach memristor conductances which are proportional to the overlap between pre- and post-synaptic pulses.This research was funded by the Spanish MCIN/AEI/10.13039/501100011033, Projects PID2019- 103869RB and TEC2017-90969-EXP. The Spanish MicroNanoFab ICTS is acknowledged for sample fabrication.Peer ReviewedPostprint (author's final draft

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation

SPICE modeling of cycle-to-cycle variability in RRAM devices

Altres ajuts: Acord transformatiu CRUE-CSICIn this work, we investigated how to include uncorrelated cycle-to-cycle (C2C) variability in the LTSpice quasi-static memdiode model for RRAM devices. Variability in the I-V curves is first addressed through an in-depth study of the experimental data using the fitdistrplus package for the R language. This provides a first approximation to the identification of the most suitable model parameter distributions. Next, the selected candidate distributions are incorporated into the model script and used for carrying out Monte Carlo simulations. Finally, the experimental and simulated observables (set and reset currents, transition voltages, etc.) are statistically compared and the model estimands recalculated if it is necessary. Here, we put special emphasis on describing the main difficulties behind this seemingly simple procedure

Beneficial role of noise in Hf-based memristors

The beneficial role of noise in the performance of Hf-based memristors has been experimentally studied. The addition of an external gaussian noise to the bias circuitry positively impacts the memristors characteristics by increasing the OFF/ON resistances ratio. The known stochastic resonance effect has been observed, when changing the standard deviation of the noise. The influence of the additive noise on the memristor current-voltage characteristic and on the set and reset related parameters are also presented

Stochastic resonance effect in binary STDP performed by RRAM devices

The beneficial role of noise in the binary spike time dependent plasticity (STDP) learning rule, when implemented with memristors, is experimentally analyzed. The two memristor conductance states, which emulate the neuron synapse in neuromorphic architectures, can be better distinguished if a gaussian noise is added to the bias. The addition of noise allows to reach memristor conductances which are proportional to the overlap between pre- and post-synaptic pulses