CEA LETI

Achieving simultaneous broadband achromatic focusing and a wide field of view remains a significant challenge for metalenses. In this work, we begin with a quadratic phase profile, enabling full field-of-view designs, and apply dispersion engineering to minimize variations of the focal length across wavelengths, thereby substantially reducing both longitudinal and transverse chromatic aberrations. This is accomplished using only the propagation phase in waveguide-like rectangular meta-atoms, without relying on geometric phase contributions. The fabricated singlet metalens experimentally demonstrates a field of view of 86{\textdegree}, along with a tenfold reduction in focal length variations with wavelength compared to a conventional quadratic metalens, achieving a measured relative shift as low as 1.3% across the 1.5 $\mu$m - 1.6 $\mu$m range (limited by our experimental setup). This improvement also leads to a twofold increase in focusing efficiency relative to the reference metalens. These experimental results validate the effectiveness of our design strategy in simultaneously enhancing the operational bandwidth and field of view of metalenses. The demonstrated performance can directly benefit beam steering applications in the near-infrared wavelength range and provides a path toward achromatic, wide field-of-view metalenses in the visible range for imaging systems

This paper introduces the concept of seamlessly embedding AI-native Radio Access Networks (RANs) and integrated device-network approaches, aiming to extend AI capabilities across diverse devices - e.g., smartphones, Internet of Things (IoT) sensors, autonomous vehicles - and network infrastructures. By embedding AI deeply within RANs and fostering tight integration between devices and network architectures, we unlock unprecedented levels of intelligence, efficiency, flexibility, and performance in wireless communication systems. This integration enables devices and networks to collaboratively process, adapt, and optimize data in real-time, creating a synergistic AI-enabled ecosystem that forms the backbone of AI-native 6G. Unlike traditional wireless communication systems, which rely on static configurations and manual interventions, AI-native networks dynamically adapt to changing conditions, autonomously optimize resource allocation, and respond to diverse user demands and environmental factors. The 6GARROW project explores these transformative approaches, demonstrating how AI-native RANs and integrated device-network solutions deliver higher throughput, lower latency, and improved reliability. Beyond enhanced performance, this paradigm shift supports a diverse range of innovative services, including spatio-temporal communications, critical and compute-AI applications, omnipresent IoT, immersive user experiences, and global broadband connectivity. This paper outlines the vision, methodologies, and potential impact of the project, highlighting its role in advancing toward a more connected, intelligent, and adaptive wireless ecosystem for the future.

TiSiN and TiSiC films were prepared by co-sputtering physical vapor deposition. The 10 nm thick layers were tested as barrier for copper diffusion and compared to the most commonly used reference barriers TaN/Ta and TiN. The diffusion study was carried out by monitoring the sheet resistance variation of the copper layer as function of temperature. With this method, if a copper diffusion through the barrier occurs, a strong increase of the sheet resistance is observed, testifying the failure of the barrier. Sheet resistance variation in a temperature range from 30°C to 500°C was tested under Nitrogen atmosphere. To confirm the results obtained by the temperature-resistance method, the barriers performances were characterized by time-of-flight secondary ion mass spectrometry (ToF-SIMS). In this case, the samples were scanned to follow the copper depth profile after annealing at 500°C for 10 hours. Even though TaN/Ta remains the best copper barrier, TiSiN films show good potential as starting point for the development of alternative barrier materials.

Phase change materials for reconfigurable photonic integrated circuits Pierre Noé, Benoît Cluzel, Stéphane Malhouitre, and Benoît Charbonnier, discuss phase change materials for reconfigurable photonic integrated circuits. Phase-change materials (PCMs) have gained increasing interest over the past decade for their potential in photonic applications. This article reviews their properties, key advantages over competing reconfigurable photonic technologies, and the challenges limiting their widespread adoption.

Milliliter-wave radar-based human body sensing requires for precise modeling of the backscattered field. This article presents an Iterative Physical Optics model designed for evaluating the near field backscattering of lossy dielectric target. The model is first validated by millimeter wave measurement in anechoic chamber and applied to simplified human body, in order to examine the impact that proximity to the target can have on the electromagnetic response.

ESA Swarm satellites carry a magnetometry payload consisting of an absolute scalar magnetometer (ASM), a relative flux gate vector magnetometer (VFM), and a set of star trackers (STR). The primary role of the ASM is to provide precise 1 Hz absolute field intensity measurements, while the VFM and STR provide the additional data needed to accurately reconstruct the attitude of the vector field to produce the official nominal Swarm L1b magnetic data. Each ASM instrument, however, can also produce its own self-calibrated 1 Hz experimental vector data, or, when requested, 250 Hz scalar burst data. Self-calibrated 1 Hz experimental vector data have routinely been produced ever since launch and are still run when the ASM instruments are not in burst mode. Such experimental data provide an interesting possibility of building alternative field models to those built from nominal Swarm L1b magnetic data. This possibility has been used to produce the only DGRF 2020 candidate model entirely and only relying on such data in the context of the recent IGRF 2025 call for candidate models. All other candidate models relied on either nominal Swarm L1b, or data from other satellites and ground observatories.Here we will report on the way this unique DGRF candidate model was built, and on the post-calibration strategy that we used to further improve the quality of this model, only and entirely relying on a dedicated analysis of model residual signals. As will be discussed, our final candidate model turns out to be one of the DGRF 2020 candidate models closest to the final official DGRF model, which a posteriori provides encouraging evidence of both the quality of the Swarm ASM experimental vector mode data and the value of our post-calibration strategy.

Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear. This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes. The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands. These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.

International audience

In this article, we propose for the first time a Ge-rich GeSbTe stack targeting a segregation- tolerant phase-change memory (PCM) device ensuring high-temperature data retention (HTDR). We co-integrated an optimized GeN underlayer (GeN UL) with a Ge-rich GeSbTe material that is known to trigger phase segregation at temperatures compatible with the back-end-of-line (BEOL) thermal budget. A channel inside the GeN layer is reliably created after the low-voltage initialization step, reducing the active volume rather independently of the cell’s critical dimension. Using statistical results from 4-kb arrays, we demonstrate a 40% programming current reduction with respect to devices without underlayer. Moreover, we present a reduced drift of the low resistance “SET” state, known to affect Ge-rich PCM alloys, and HDTR up to 250 °C. Based on TEM/EDX analyses and TCAD simulations, we show how the combination of a reliable initialization step and the specific properties of our stack led to an innovative Ge/Sb-rich GeSbTe alloy featuring promising performances.