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Hafnium oxide-based ferroelectric field effect transistors: From materials and reliability to applications in storage-class memory and in-memory computing
No full textInternational audienceHafnium oxide-based ferroelectric field-effect transistors (FeFETs) are redefining non-volatile memory (NVM) by enabling low-power,high-speed, and compatibility with advanced complementary metal–oxide–semiconductor nodes. Exploiting polarization-inducedthreshold voltage shifts in ultra-scaled gate stacks, FeFETs achieve sub-5 V write voltages, <10 ns switching, on/off ratios .103, .106 sdata retention, and endurance up to 108 cycles under optimized stress. This review consolidates recent advances in orthorhombic phasestabilization via dopant engineering, interfacial optimization, and defect dynamics that dictate performance variability. Compared toresistive RAM, phase-change, magnetic, and flash memories, FeFETs demonstrate superior integration potential for storage-classmemory and compute-in-memory applications. Silicon-channel devices already achieve <100 ns read/write speeds and programmingenergy near 100 fJ/bit, with scalability beyond the 28 nm node. Innovations—such as La doping, asymmetric gate stacks, and oxidesemiconductors, such as indium gallium zinc oxide and molybdenum sulfide—have enabled sub-1 V operation and endurance .1010cycles. Reliability concerns including wake-up and fatigue are linked to oxygen vacancy migration, interface trap formation, and phaseboundary evolution, elucidated through cycling endurance, data retention, and low-frequency noise analysis. We also highlight industrialprogress in stacked FeFET arrays and 3D NVM structures, targeting commercialization by 2028–2030. This article charts a completetrajectory from material to system level, establishing FeFETs as a cornerstone for secure, fast, and energy-efficient next-generationmemory
Data-Driven Approaches for Indirect Aging Estimation in Power Converters
No full textNational audienceThis paper presents an indirect method to estimate the aging of active and passive components in a synchronous buck converter. By monitoring electrical indicators such as efficiency, current ripple, voltage overshoot, and settling time, we demonstrate the ability to detect parameter change resulting from component degradation, including ESR increase, inductance variation, and MOSFET on-resistance (R ds,on ) rise. A combination of simulation sensitivity analysis, principal component analysis (PCA), and regression models is used to evaluate indicator relevance and build aging estimators. Experimental results confirm that this approach can detect changes in capacitor parameters, validating the possibility of monitoring degradation simply from microcontroller measures
Monitoring and Sensing System for People's Behavior During Fall Events Based on Mobility Analysis
No full textInternational audienceObjectivesObserving the activities of the elderly in natural life is a crucial issue nowadays to better understand their potential behavioral changes and predict risks. To this end, a comprehensive hardware and software infrastructure has been designed by a multidisciplinary team of researchers and pre-tested in a smart flat lab. It enables to collect relevant data and develop algorithms to analyze activities and detect changes such as falls, wandering or other risky situations. This study was carried out in a shared house by 12 independent elderly people. The study focuses on episodes of falls in the house, and analyzes mobility behavior before and after falls to observe the person's rehabilitation in the home.Materials and MethodsEach resident's room and the two shared spaces were equipped with motion and magnetic contact sensors to record movements and entry/exit activities. 9 months of data were collected and analyzed, highlighting patterns of activity and changes in these behaviors, particularly when a fall occurred and then when the usual behavior returned, if at all. Two levels of analysis were implemented: the detection of deviation in activity indicators for each individual, and the detection of drift in the established behavior pattern over time. The classification technique used to extract the patterns is the K-means partitioning algorithm. We also used the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method to validate the performance of the K-means method.ResultsData analysis was carried out on the 4 falls recorded during the observation period, involving 4 of the house's occupants. The results highlight the relationship between model conduct and events related to falls and returns from hospitalization. Detection was validated by share house carers' annotations, acting as a ground truth, on the days when falls occurred. The first results of pattern recognition with clustering methods show that the K-means method provides more convincing results than the DBSCAN method. In this study, by observing the movement signals of residents who fell during the course of the study, we were able to identify characteristic post-fall behaviors
Lie Group Based Approach for GNSS Signal Phase Modeling
No full textInternational audienceLeveraging carrier phase observations within Global Navigation Satellite Systems receivers allows centimeter-level positioning accuracy. However, carrier phase observations are significantly affected by additive noise, which is assumed to follow a von Mises distribution, thereby degrading the performance of phase-based positioning estimators. To improve the modeling of carrier phase observations, we propose a novel approach that constrains the parameters of the von Mises distri-bution-specifically, the angular location modeling the phase and its dispersion parameter κ modeling the noise-to evolve within the Lie group space . To estimate these parameters, we employ a Lie group maximum likelihood estimator, solved through a Newton algorithm on Lie groups. This approach demonstrates advantages in terms of robustness and precision, especially when dealing with a small number of observations, compared to traditional Euclidean-based methods
Post-clustering Inference under Dependence
No full textRecent work by Gao et al. (JASA 2022) has laid the foundations for post-clustering inference, establishing a theoretical framework allowing to test for differences between means of estimated clusters. Additionally, they studied the estimation of unknown parameters while controlling the selective type I error. However, their theory was developed for independent observations identically distributed as p-dimensional Gaussian variables, where the parameter estimation could only be performed for spherical covariance matrices. Here, we aim at extending this framework to a more convenient scenario for practical applications, where arbitrary dependence structures between observations and features are allowed. We establish sufficient conditions for extending the setting presented by Gao et al. to the general dependence framework. Moreover, we assess theoretical conditions allowing the compatible estimation of a covariance matrix. The theory is developed for hierarchical agglomerative clustering algorithms with several types of linkages, and for the k-means algorithm. We illustrate our method with synthetic data and real data of protein structures
Differential Privacy in Practice: Lessons Learned From 10 Years of Real-World Applications
No full textInternational audienceDifferential privacy (DP) is a widespread data protection mechanism. However, its application in real-world scenarios has been challenging. To shed some light on this, we offer a critical analysis of 21 DP deployments by top-tier companies and institutions over the past decade
A Noninvasive Framework for Heart Function Assessment by Multitask Learning
No full textInternational audienceAccurate assessment of cardiac function is vital for preventing and managing cardiovascular diseases (CVDs). Recent advancements in machine learning, especially convolutional neural networks (CNNs) and multitask learning (MTL), have improved the precision of echocardiogram evaluations. However, existing methods often overlook the intrinsic relationships among ejection fraction (EF), end-diastolic volume (EDV), and end-systolic volume (ESV), which are essential for accurate assessments. We propose a noninvasive framework for heart function assessment (FHFA) using MTL that utilizes a 3-D CNN to extract key spatiotemporal features from echocardiogram videos. By employing an MTL strategy and weight distribution mechanism, this framework enhances the accuracy of EF predictions and provides a comprehensive assessment of cardiac structure and function. This approach ensures that the model effectively integrates auxiliary task information while focusing on the primary task, resulting in a more precise analysis of cardiac function. The experimental results on the Echonet-Dynamic dataset demonstrate that our method achieves an average absolute error of 3.89, a root-mean-square error (RMSE) of 5.13, and an R2 value of 0.82, outperforming existing methods. Future work will focus on automatic weight optimization, model compression, and improving computational efficiency for broader clinical applications
Post-mortem lung biopsies in fatal Covid-19 acute respiratory distress syndrome: a prospective cohort study of 169 patients (HISTOCOVID)
No full textInternational audienceAbstract Background Refractory acute respiratory distress syndrome (ARDS) is the leading cause of death in patients with Covid-19. Large studies of lung pathology in patients who died of Covid-19-ARDS may help to understand the mechanisms of death and to guide further research. Methods This prospective multicentre cohort study included 338 post-mortem, percutaneous, lung biopsies from 169 patients who died of Covid-19-ARDS between 22/04/2020 and 08/03/2021 in 26 intensive care units in France. The biopsies were done at the bedside by the intensivist immediately after death, using a 14G needle and following anatomical landmarks. A pathologist examined all biopsies, describing all elementary lesions and establishing a final histopathological diagnosis. Results Lung parenchyma was evaluable in 155/169 (92%) patients. Early, proliferative-phase diffuse alveolar damage (DAD) was the most common finding (39%), followed by late proliferative-phase DAD (32%) and exudative-phase DAD (18%); fibrotic-phase DAD was present in three (2%) patients. Organising pneumonia (OP) and acute fibrinous and organising pneumonia (AFOP) were evidenced in 21 (13%) and 16 (9%) patients, respectively. Unclassified interstitial lesions were seen in 33 (21%) patients. Microthrombi were uncommon (6%). Conclusions DAD was the most common pathological pattern, whereas collagen fibrosis and microthrombi were rare. A quarter of patients had evidence of OP or AFOP. This substantial prevalence of corticosteroid-sensitive patterns suggests that selected patients with refractory Covid-19-ARDS might benefit from higher doses or longer courses of corticosteroids. Trial registration. ClinicalTrials.gov NCT04675281. Registered 19 December 2020
Analyse des Systèmes Interconnectés avec Dynamiques Nonlinéaires et Hybrides
No full textNational audienceThe complexity of modern control systems can often be attributed to two elements: firstly, such systems involve logic-based decision making which results in dynamics at different time scales, and secondly, these systems comprise several subsystems which play an important role in shaping the properties of the integrated system. Following this viewpoint, the thesis addresses the analysis techniques for interconnection of systems described by switching, nonsmooth, or more generally, hybrid dynamics in both deterministic and stochastic framework.Starting from some earlier work, we first present the classical cascade configuration for time-dependent switched systems where the stability conditions are formulated for a certain class of switching signals using multiple Lyapunov functions, and the notion of input-to-state stability. As a generalization, and using the tools from nonsmoth analysis, we study the feedback interconnections of Filippov differential inclusions (for state-dependent switched systems) with application to observer-based control, and anti-maximal monotone differential inclusions (for projected systems, complementarity systems, and sweeping processes) with application to analyzing certain optimization algorithms. Moving forward, and in the spirit of studying a broader class of interconnections, we study graph-coupled nonlinear systems where the exchange of information between agents is described by switching, but jointly-connected, graphs. The analysis of such systems is carried out by developing singular perturbation theory for hybrid systems, where we propose a novel decomposition of hybrid systems resulting in a continuous-time quasi-steady-state system and a purely discrete-time boundary layer system with constrained switching. We provide conditions for asymptotic practical stability, which in the setting of graph-based interconnections, translate to checking some properties of the graphs and the stability of reduced-order subsystems.In the final part of the thesis, we step away from the deterministic framework and study interconnections in stochastic setting that appear in the design of certain filtering algorithms. The first such class of interconnections is seen in ensemble filters (for systems described by stochastic differential equations and discrete observations) where we propose algorithms for computing the approximation of the posterior distribution of the state conditioned upon the measurements by simulating particles resulting from continuous-discrete McKean-Vlasov type differential equations. We then develop appropriate tools for analyzing the interconnection of particles coupled to each other via the empirical mean and empirical covariance. Another class of interconnections is seen in studying filtering algorithms with unknown parameters (such as noise covariances), where we use Bayesian inference algorithms and the optimal estimate is described by a probabilisitic weighted sum of the conditional posteriors. Under certain assumptions on system dynamics, we study asymptotic convergence for such algorithms towards the optimal solution determined by complete information of the parameters
Toward a Real-Time Framework for Accurate Monocular 3D Human Pose Estimation with Geometric Priors
No full textInternational audienceMonocular 3D human pose estimation remains a challenging and ill-posed problem, particularly in real-time settings and unconstrained environments. While direct imageto-3D approaches require large annotated datasets and heavy models, 2D-to-3D lifting offers a more lightweight and flexible alternative-especially when enhanced with prior knowledge. In this work, we propose a framework that combines real-time 2D keypoint detection with geometry-aware 2D-to-3D lifting, explicitly leveraging known camera intrinsics and subject-specific anatomical priors. Our approach builds on recent advances in self-calibration and biomechanically-constrained inverse kinematics to generate large-scale, plausible 2D-3D training pairs from MoCap and synthetic datasets. We discuss how these ingredients can enable fast, personalized, and accurate 3D pose estimation from monocular images without requiring specialized hardware. This proposal aims to foster discussion on bridging data-driven learning and model-based priors to improve accuracy, interpretability, and deployability of 3D human motion capture on edge devices in the wild