Development of an ultrafast on-the-fly I DLIN technique to study NBTI in plasma and thermal oxynitride p-MOSFETs

An ultrafast on-the-fly technique is developed to study linear drain current (I DLIN) degradation in plasma and thermal oxynitride p-MOSFETs during negative-bias temperature instability (NBTI) stress. The technique enhances the measurement resolution (ldquotime-zerordquo delay) down to 1 mus and helps to identify several key differences in NBTI behavior between plasma and thermal films. The impact of the time-zero delay on time, temperature, and bias dependence of NBTI is studied, and its influence on extrapolated safe-operating overdrive condition is analyzed. It is shown that plasma-nitrided films, in spite of having higher N density, are less susceptible to NBTI than their thermal counterparts.© IEE

Material dependence of NBTI physical mechanism in silicon oxynitride (SiON) p-MOSFETs: A comprehensive study by ultra-fast on-the-fly (UF-OTF) I(DLIN) technique

An Ultra-Fast On-The-Fly (UF-OTF) I(DLIN) technique having 1 mu s resolution is developed and used to study gate insulator process dependence of NBTI in Silicon Oxynitride (SiON) p-MOSFETs. The Nitrogen density at the Si-SiON interface and the thickness of SiON layer are shown to impact temperature, time, and field dependencies of NBTI. The plausible material dependence of NBTI physical mechanism is explored

Material dependence of negative bias temperature instability (NBTI) stress and recovery in SiON p-MOSFETs

Negative Bias Temperature Instability (NBTI) is studied in Silicon Oxynitride (SiON) p-MOSFETs using a recently developed Ultra-Fast On-The-Fly linear drain current (UF-OTF I(DLIN)) method. It is shown that both stress and recovery phases of NBTI are strongly influenced by SiON gate insulator process. Gate insulator nitrogen (N) spatial distribution is shown to impact interface trap generation (Delta N(IT)) and hole trapping (Delta N(h)) components of overall threshold voltage shift (Delta V(T)). A simple, self consistent method is proposed to isolate Delta N(IT) and Delta N(h). It is shown that the time dynamics of stress and recovery phases are strongly correlated, at short time governed by trapping and detrapping of holes, and at long time by generation and passivation of interface traps

Theory and practice of on-the-fly and ultra-fast V_T measurements for NBTI degradation: Challenges and opportunities

On-the-fly and ultra-fast VT are popular characterization techniques for analyzing NBTI degradation. We show that these techniques do not probe the intrinsic NBTI degradation directly and hence require suitable correction. The 'corrected' data allows us to explore the subtlety of relaxation dynamics by various measurements and suggest a theoretical basis for log-t relaxation consistent within R-D framework

Metrics from Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data from the Second TemPredict Study

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses

Metrics from Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data from the Second TemPredict Study.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses

A GPU accelerated Lennard-Jones system for immersive molecular dynamics simulations in virtual reality

Interactive tools and immersive technologies make teaching more engaging and complex concepts easier to comprehend are designed to benefit training and education. Molecular Dynamics (MD) simulations numerically solve Newton’s equations of motion for a given set of particles (atoms or molecules). Improvements in computational power and advances in virtual reality (VR) technologies and immersive platforms may in principle allow the visualization of the dynamics of molecular systems allowing the observer to experience first-hand elusive physical concepts such as vapour-liquid transitions, nucleation, solidification, diffusion, etc. Typical MD implementations involve a relatively large number of particles N = O( 104 ) and the force models imply a pairwise calculation which scales, in case of a Lennard-Jones system, to the order of O( N2 ) leading to a very large number of integration steps. Hence, modelling such a computational system over CPU along with a GPU intensive virtual reality rendering often limits the system size and also leads to a lower graphical refresh rate. In the model presented in this paper, we have leveraged GPU for both data-parallel MD computation and VR rendering thereby building a robust, fast, accurate and immersive simulation medium. We have generated state-points with respect to the data of real substances such as CO 2 . In this system the phases of matter viz. solid liquid and gas, and their emergent phase transition can be interactively experienced using an intuitive control panel