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In this paper, we investigate the recovery characteristics associated with negative bias temperature instability and channel hot carrier degradation in p-channel metal-oxide-semiconductor using a novel method of on-the-fly patterns of biases. We demonstrate experimentally that we can identify two types of mechanisms associated with degradation. One of them is associated with recovery dynamics, and the other relates to nonrecoverable degradation. [ABSTRACT FROM PUBLISHER]

In this paper, an analysis of the trapping in high- dielectrics and its origin is given. It is found that the defect is consistent with oxygen vacancies in monoclinic hafnia. Finally, guidelines are proposed to reduce these instabilities [ABSTRACT FROM PUBLISHER]

High-k gate dielectrics, particularly Hf-based materials, are likely to be implemented in CMOS advanced technologies. One of the important challenges in integrating these materials is to achieve lifetimes equal or better than their SiO2 counterparts. In this paper we review the status of reliability studies of high-k gate dielectrics and try to illustrate it with experimental results. High-k materials show novel reliability phenomena related to the asymmetric gate band structure and the presence of fast and reversible charge. Reliability of high-k structures is influenced both by the interfacial layer as well as the high-k layer. One of the main issues is to understand these new mechanisms in order to asses the lifetime accurately and reduce them. [ABSTRACT FROM PUBLISHER]

This paper gives an insight into the degradation mechanisms during negative and positive bias temperature instabilities in advanced CMOS technology with a 2-nm gate oxide. We focus on generated interface traps and oxide traps to distinguish their dependencies and effects on usual transistor parameters. negative bias temperature instability (NBTI) and positive bias temperature instability in both NMOS and PMOS have been compared and a possible explanation for all configurations has been suggested. Recovery and temperature effect under NBTI were also investigated showing different behaviors of the two components. [ABSTRACT FROM PUBLISHER]

Abstract: In this work we report an experimental observation of the current dependence on the defect generation probability driving to breakdown. We propose the MVHR model (multi-vibrational hydrogen release) based on the multi-vibrational excitation of the Si–H bond stretching mode. By this way we explain the power-law dependence of charge and time to breakdown and highlight its limit on PMOS inversion. [Copyright &y& Elsevier]

Background: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess speech-based systems' limitations, such as uncertainty, or fairness for a safe clinical deployment., Objective: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population., Methods: We included n=865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically analyzed participants' speech with a fully ML automatic pipeline to capture speech variability. Then, we modelled speech with pretrained deep learning models that were pre-trained on a large open free database and we selected the best one on the validation set. Based on the best speech modelling approach, we evaluated clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, education). We employed a train-validation-test split for all evaluations: to develop our models, select the best ones and assess the generalizability of held-out data., Results: The best model was WhisperM with a max pooling, and oversampling method. Our methods achieved good detection performance for all symptoms, depression (PHQ-9 AUC= 0.76F1=0.49, BDI AUC=0.78, F1=0,65), anxiety (GAD-7 F1=0.50, AUC=0.77) insomnia (AIS AUC=0.73, F1=0.62), and fatigue (MFI Total Score F1=0.88, AUC=0.68). These strengths were maintained for depression detection with BDI and Fatigue for abstention rates for uncertain cases (Risk-Coverage AUCs < 0.4). Individual symptom scores were predicted with good accuracy (Correlations were all significant, with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average Disparity Ratio (DR) = 0.86), to a lesser extent for education level (average Disparity Ratio (DR) = 0.47) and worse for age groups (average Disparity Ratio (DR) = 0.33)., Conclusions: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems. This approach offers promise for more accurate and nuanced mental health assessments, benefiting both patients and clinicians.