Modeling and integrating Local-First Data Types
Existing modeling frameworks have limited collaboration capabilities. Collaboration at model level is one of the top desired features as identified in the literature. However, most port of solutions primarily rely on cloud-based and centralized databases as their technological solution. While these solutions ease collaboration among connected partners by employing concurrency control techniques or adopting a "last writer wins" policy, they do not support disconnected collaboration scenarios, which is an important feature for designing local-first sofware. This situation presents a significant compromise: utilizing cloud-based solutions and sacrificing data ownership control versus adopting separate instances and without collaborative capabilities. The objective of this postdoctoral project is to contribute and extend an existing local-first Model-Based Systems Engineering (MBSE) framework, related to this work [5], built upon specialized Conflict-free Replicated Data Types (CRDTs). The goal is to enable real-time collaboration through modeling-specific CRDTs. The proposed approach involves extending a middleware layer utilizing CRDTs to seamlessly synchronize distributed, offline-capable engineering models.
DTCO for RF & mmW Applications:Focus on Homogeneous & Heterogeneous Chiplet Hybrid Bonding Challenge
In recent years, there have been numerous technological advancements in silicon-based semiconductors. However, the limits in terms of frequency performance and power seem to have been reached, requiring the development of new type III-V devices (such as InP and GaN) that are faster, more powerful and well adapted for new RF mmW applications. For reasons of flexibility, performance, and cost, it is crucial to co-integrate these new high-performance III-V components with the more traditional silicon technologies. This is one of the major objectives of the proposed topic.
The focus will be on the design and optimisation of millimetre-wave RF circuits using 3D heterogeneous hybrid bonding assembly technology. In recent years, numerous test vehicles have been fabricated and characterised to demonstrate the advantages and disadvantages of the hybrid bonding assembly process for millimetre wave RF applications. The aim is to extend this work and focus the studies and research on real RF systems, such as millimetre-wave power amplifiers. The DTCO (Design and Technology Co-Optimisations) approach will not only enable the design of efficient 3D RF circuits, but will also allow the adaptation of different 3D design rules to make 3D hybrid bonding technology relevant for the production of millimetre-scale 3D integrated systems.
2D materials electrical characterization for microelectronics
Future microelectronic components will be ever smaller and ever more energy-efficient. To meet this challenge, 2D materials are excellent candidates, thanks to their remarkable dimensions and electronic properties (high mobility of charge carriers, high light emission/absorption). What's more, they feature van der Waals (vdW) surfaces, i.e. no dangling bonds, enabling them to retain their properties even at very small dimensions (down to the monolayer). New 2D materials and vdW stacks with novel physical properties are being discovered every day. However, integrating them and measuring their performance in circuits remains an ongoing challenge, as their properties must be preserved during integration.
The aim of this post-doc is to develop components for qualifying 2D materials for microelectronic (RF transistor) and spintronic (magnetic memory) applications in horizontal configuration on silicon. A vertical measurement method has already been developed by CEA LETI. Building on these developments, the candidate will develop this measurement system and characterize various materials produced in MBE by CEA-IRIG. The work will involve transferring these layers onto chips, optimizing the electrical contacts and developing the in-plane electrical measurement chain.
High-performance computing using CMOS technology at cryogenic temperature
Advances in materials, transistor architectures, and lithography technologies have enabled exponential growth in the performance and energy efficiency of integrated circuits. New research directions, including operation at cryogenic temperatures, could lead to further progress. Cryogenic electronics, essential for manipulating qubits at very low temperatures, is rapidly developing. Processors operating at 4.2 K using 1.4 zJ per operation have been proposed, based on superconducting electronics. Another approach involves creating very fast sequential processors using specific technologies and low temperatures, reducing energy dissipation but requiring cooling. At low temperatures, the performance of advanced CMOS transistors increases, allowing operation at lower voltages and higher operating frequencies. This could improve the sequential efficiency of computers and simplify the parallelization of software code. However, materials and component architectures need to be rethought to maximize the benefits of low temperatures. The post-doctoral project aims to determine whether cryogenic temperatures offer sufficient performance gains for CMOS or should be viewed as a catalyst for new high-performance computing technologies. The goal is particularly to assess the increase in processing speed with conventional silicon components at low temperatures, integrating measurements and simulations.
Design and Implementation of a Neural Network for Thermo-Mechanical Simulation in Additive Manufacturing
The WAAM (Wire Arc Additive Manufacturing) process is a metal additive manufacturing method that allows for the production of large parts with a high deposition rate. However, this process results in highly stressed and deformed parts, making it complex to predict their geometric and mechanical characteristics. Thermomechanical modeling is crucial for predicting these deformations, but it requires significant computational resources and long calculation times. The NEUROWAAM project aims to develop a precise and fast thermomechanical numerical model using neural networks to predict the physical phenomena of the WAAM process. An internship in 2025 will provide a database through thermomechanical simulations using the CAST3M software. The post-doc's objective is to develop a neural network architecture capable of learning the relationship between the manufacturing configuration and the thermomechanical characteristics of the parts. Manufacturing tests on the CEA's PRISMA platform will be conducted to validate the model and prepare a feedback loop. The CEA List's Interactive Simulation Laboratory will contribute its expertise in accelerating simulations through neural networks and active learning to reduce training time.
Digital correction of the health status of an electrical network
Cable faults are generally detected when communication is interrupted, resulting in significant repair costs and downtime. Additionally, data integrity becomes a major concern due to the increased threats of attacks and intrusions on electrical networks, which can disrupt communication. Being able to distinguish between disruptions caused by the degradation of the physical layer of an electrical network and an ongoing attack on the energy network will help guide decision-making regarding corrective operations, particularly network reconfiguration and predictive maintenance, to ensure network resilience. This study proposes to investigate the relationship between incipient faults in cables and their impact on data integrity in the context of Power Line Communication (PLC). The work will be based on deploying instrumentation using electrical reflectometry, combining distributed sensors and AI algorithms for online diagnosis of incipient faults in electrical networks. In the presence of certain faults, advanced AI methods will be applied to correct the state of the health of the electrical network's physical layer, thereby ensuring its reliability.
Advanced reconstruction methods for cryo-electron tomography of biological samples
Cryo-electron tomography (CET) is a powerful technique for the 3D structural analysis of biological samples in their near-native state. CET has seen remarkable advances in instrumentation in the last decade but the classical weighted back-projection (WBP) remains by far the standard CET reconstruction method. Due to radiation damage and the limited tilt range within the microscope, WBP reconstructions suffer from low contrast and elongation artifacts, known as ‘missing wedge’ (MW) artifacts. Recently, there has been a revival of interest in iterative approaches to improve the quality and hence the interpretability of the CET data.
In this project, we propose to go beyond the state-of-the-art in CET by (1) applying curvelet- and shearlet-based compressed sensing (CS) algorithms, and (2) exploring deep learning (DL) strategies with the aim to denoise et correct for the MW artifacts. These approaches have the potential to improve the resolution of the CET reconstructions and facilitate the segmentation and sub-tomogram averaging tasks.
The candidate will conduct a comparative study of iterative algorithms used in life science, and CS and DL approaches optimized in this project for thin curved structures.
Simulation of thermal transport at sub-Kelvin
Thermal management in quantum computers is an urgent and crucial task. As the number of qubits rapidly scales, more electric circuits are placed close to qubits to operate them. Joule-heating of these circuits could significantly warm the qubit device, degrading its fidelity. With intensive activity in quantum computing at Grenoble, we (CEA-LETI, Grenoble, France) are looking for an enthusiastic post-doc researcher to study thermal transport at cryogenic temperature (sub-Kelvin).
The post-doc will apply the finite-element non-equilibrium Green’s function [1], developed in the group of Natalio Mingo at CEA-Grenoble, to simulate phonon transport in various designed structures. The simulation result promotes comparison with on-going experiments and constructive discussions in order to optimize the thermal management.
[1] C. A. Polanco, A. van Roekeghem, B. Brisuda, L. Saminadayar, O. Bourgeois, and N. Mingo, Science Advances 9, 7439 (2023).
Development of optoelectronic systems for quantum sensor technologies
The main mission of CEA LETI's Autonomy and Sensor Integration Laboratory (LAIC) is to develop sensor systems, and in particular quantum sensors for high-precision magnetic field measurement applications. The team's activities are at the interface of hardware (electronics, optronics, semiconductors), software (artificial intelligence, signal processing) and systems (electronic architecture, mechatronics, multiphysics modeling). The Swarm project (https://swarm.cnes.fr/en/), which put our quantum sensors for measuring the Earth's magnetic field into orbit in 2013, is one of our track records, and a new program with similar objectives gets underway this year.
Quantum technologies are strategic for the development of sensors with unrivalled performances, as we have demonstrated in magnetometry. Our challenge today is to adapt these developments and this know-how to new physics.
To support our developments in quantum sensors, we are looking for an opto-electronics post-doc researcher to design new quantum sensors and develop the associated optical benches. This post-doc position will have a significant experimental component.
Your main mission will be to participate to the development of these new sensors and their associated characterization benches, interfacing with CEA experts in the field.
More specifically, your mission will revolve around the following actions:
• Design and assembly of quantum sensors (optical fibers, RF sources, photodetectors)
• Participation in modeling the physical phenomena involved
• Design and build the optical characterization benches
• Development of the control electronics
• Publication of results in scientific journals
• Presentation of work in international conferences
• Patents proposal
Exploring microfluidic solutions for manufacturing targets for fusion power generation
As part of a call for projects on "innovative nuclear reactors", the TARANIS project involves studying the possibility of energy production by a power laser-initiated inertial confinement fusion power plant. The current context, which encourages the development of low-carbon energies, and the fusion experiments carried out by the NIF's American teams, make it very attractive to conduct high-level research aimed at eventually producing an economically attractive energy source based on inertial fusion.
Among the many technical hurdles to be overcome, the production of fusion targets with a suitable reaction scheme compatible with energy production is a major challenge. The CEA has the know-how to produce batches of capsules containing the fusible elements of the reaction. However, the current process is not suitable for mass production of hundreds of thousands of capsules per day at an acceptable cost.
One high-potential avenue lies in the use of microfluidic devices, for which the Microfluidic Systems and Bioengineering Laboratory (LSMB) of the Health Technologies and Innovation Department (DTIS) of CEA's DRT has recognized expertise.