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With the technological scaling of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the scarcity of circuit design margins, the characteristics of device reliability have garnered widespread attention. Traditional single-mode reliability mechanisms and modeling are less sufficient to meet the demands of resilient circuit designs. Mixed-mode reliability mechanisms and modeling have become a focal point of future designs for reliability. This paper reviews the mechanisms and compact aging models of mixed-mode reliability. The mechanism and modeling method of mixed-mode reliability are discussed, including hot carrier degradation (HCD) with self-heating effect, mixed-mode aging of HCD and Bias Temperature Instability (BTI), off-state degradation (OSD), on-state time-dependent dielectric breakdown (TDDB), and metal electromigration (EM). The impact of alternating HCD-BTI stress conditions is also discussed. The results indicate that single-mode reliability analysis is insufficient for predicting the lifetime of advanced technology and circuits and provides guidance for future mixed-mode reliability analysis and modeling.
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http://dx.doi.org/10.3390/mi15010127 | DOI Listing |
Anal Bioanal Chem
August 2025
Department of Chemistry, Faculty of Science, Dicle University, Diyarbakir, 21000, Turkey.
Mixed-mode chromatography is increasingly valued for retaining analytes with diverse polarity and charge by integrating hydrophilic interaction (HILIC), reversed-phase (RPLC), and ion-exchange mechanisms. However, designing stationary phases that are both easy to synthesize and chromatographically versatile remains challenging. This study presents DEA-Mix-SP, a novel silica-based stationary phase functionalized with diethanolamine via [2-(3,4-epoxycyclohexyl)ethyl]trimethoxysilane, offering a simple synthetic route for broad-spectrum separation.
View Article and Find Full Text PDFPolymers (Basel)
July 2025
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada.
Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption.
View Article and Find Full Text PDFJ Med Internet Res
July 2025
Computer Information Systems and Business Analytics, James Madison University, 800 South Main Street, Harrisonburg, VA, 22807, United States, 1 7164586270.
Background: The increased integration of telehealth services into health care systems, especially during the COVID-19 pandemic, transformed patient-provider interactions. Despite numerous benefits that promote health equity and resource allocation, patients' acceptance and use of telehealth have declined post pandemic. To enhance health care delivery and patient satisfaction, we study the factors of this decline from the perspective of patient characteristics that influence the adoption and use of telehealth services.
View Article and Find Full Text PDFBMC Public Health
May 2025
Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland.
Background: Low response rates are an increasing problem in population-based gambling surveys. Selective non-response may cause biased findings. Supporting information from administrative registers, whenever available for non-respondents can be utilized to estimate the effect of non-response to the gambling-related outcomes.
View Article and Find Full Text PDFNat Commun
May 2025
State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, China.
Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM).
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