Design and implementation of an alternative to representation predicates in Frama-C
Frama-C is a collaborative platform for analysis of C programs. Among the different tools available in Frama-C, WP is dedicated to deductive verification of programs, allowing mathematical proof of functional properties and absence of runtime errors. This tool is used for years in industry.
Separation logic is the most promising way to allow verification of properties for programs involving complex data structures. However, it is hard to use for industrial use-cases. The main reason for this is that it is hard to encode into automatic proof tools. Thus, its use requires a lot of work from users.
In a recent work, we described how to extend WP tooling tout describe the memory footprint of C data-structures. The idea is to provide a specification language that allows to capture most of the power of separation logic without having to encode it in proof tools. The goal of this postdoc is to experiment the use of this formalism to describe real world use cases and implement its support in Frama-C and WP.
Superconducting BEOL integration for upcoming quantum devices
Controlling and manipulating quantum information using advanced nanoelectronic technologies represents a major challenge currently undertaken by CEA-LETI and its partners. A key objective of this project is to achieve the integration of quantum devices within Fully Depleted Silicon-On-Insulator (FD-SOI) technology on a 300 mm platform. The success of this integration critically depends on the development of superconducting interconnects, which are essential for ensuring the thermomagnetic isolation of quantum devices in addition to ensuring the electrical continuity of the device.
The proposed integration scheme builds on a CEA-LETI patent that enabled the fabrication of TaN/TiN-based superconducting interconnections, exhibiting a critical temperature (Tc) on the order of one kelvin. The goal of this research project is to explore the integration of superconducting materials with higher critical temperatures (around ten Kelvins) in order to enhance thermomagnetic isolation and improve overall device performance. This postdoctoral project aims to investigate the potential of newly developed high-temperature superconducting materials — such as ZrN, HfN, and NbTiN — produced by ICPMS-CNT and CEA-LETI, as well as their integration into the existing process flow. Using an innovative direct etch approach, the postdoc will study the impact of the process step on the superconducting properties. The influence of line dimensions on the superconducting properties such as critical temperature and current density of the materials will be also investigated. Based on the results obtained, process and integration adjustments will be proposed to optimize performances.
Thermal properties of 3D aluminum nitride structures for electronic packaging
The 12-month postdoctoral fellowship is part of the overall 3DNAMIC project, funded by the Occitanie region and involving the Materials platform of the DRTDOCC department and the Laplace laboratory. A thesis began in December 2024 aimed at “the study and characterization of 3D aluminum nitride ceramics for the thermal packaging and management of electronic components.”
The postdoc is scheduled to begin at approximately in September 2026, with the following main objectives:
Objective 1: Perform a comparative analysis of the thermal properties of ceramics produced by AF elements and on model structures using different materials available in the CEA materials platform.
Objective 3: Propose, qualify, and validate, numerically and then experimentally, heat dissipation structures for ceramics obtained by FA as part of the 3DNAMIC project.
Integrating dynamic CRDTs replicas
Existing modeling frameworks have limited collaboration capabilities. Collaboration at the model level is one of the top desired features. However, most solutions rely on cloud-based and centralized databases as their technological solution. While these solutions ease collaboration among connected partners by employing concurrency control techniques, they do not support disconnected collaboration scenarios, which is an important feature for designing local-first software. This situation presents a significant compromise: utilizing cloud-based solutions with loss of data ownership control versus adopting separate instances without collaborative capabilities.
The objective of this postdoctoral project is to contribute to and extend an existing local-first Model-Based Systems Engineering (MBSE) framework, 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 communication layer utilizing CRDTs to
seamlessly synchronize distributed, offline-capable engineering models.
The postdoctoral researcher will conduct a state-of-the-art review of communication and group membership approaches in P2P environments. One major issue to be taken into account is the entry and exit of members in a group, so the CRDT state is always
coherent. The components will be integrated into our CRDT and modeling framework.
Tools and diagnostic methods for the reuse of electronic components
The Autonomy and Sensor Integration Laboratory (LAIC) at CEA-Leti has the primary mission of developing sensor systems for the digitalization of systems. 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).
In a context of exponential growth in electronics and scarcity of resources, the reuse of electronic components from end-of-life systems represents a promising avenue to limit environmental impact and support the development of a circular economy. The objective of this project is to develop an advanced diagnostic methodology to assess the health status of electronic components, particularly power components, to reintegrate them into a less constraining second-life cycle.
The postdoctoral researcher will be tasked with developing a comprehensive approach to evaluate the reuse potential of electronic components, with the aim of reintroducing them into second-life cycles. This will include:
- Identifying relevant health indicators to monitor the performance evolution of components (e.g., MOSFETs, IGBTs, capacitors, etc.);
- Setting up test benches and sensors adapted to measure electrical, thermal, or mechanical parameters, with the goal of detecting signs of aging;
- Analyzing degradation modes through experimental tests and failure models;
- Developing algorithms for predicting the remaining useful life (RUL) adapted to different usage scenarios;
- Contributing to scientific publications, the valorization of results, and collaboration with project partners.
Analysis of Gas Effluents for More Eco-Responsible Plasma Etching Processes
Traditionally used fluorinated gases, such as CF4 and C4F8, exhibit extremely high Global Warming Potentials (GWPs), significantly contributing to climate change. To address these environmental challenges, the project aims to promote the use of alternative low-GWP gases in combination with efficient exhaust abatement systems at the reactor outlet, while maintaining high-performance plasma etching processes. In this context, the postdoctoral researcher will be responsible for the analysis and characterization of gaseous species in an industrial plasma etching reactor using mass spectrometry. These measurements will be compared with the gas effluent at the outlet of the pumping and abatement systems. The main objectives are (i) to quantify the Destruction Removal Efficiency (DRE) of high and low GWP fluorocarbon gases during plasma processing and within the pumping and abatement stages, and (ii) to identify and propose innovative, environmentally responsible alternatives to minimize the release of high-GWP gaseous effluents.
Diamond-based electrochemical sensors for monitoring water pollution in urban environments
This postdoctoral position is offered by CEA List as part of the European UrbaQuantum project ("A novel, Integrated Approach to Urban Water Quality Monitoring, Management and Valorisation"), part of the HORIZON-CL6-2024-ZEROPOLLUTION-02 call for projects. The main objective of this project is to develop, in response to climate change, sensors, models, and protocols for better management of the water cycle in urban environments.
At the Sensors and Instrumentation for Measurement Laboratory (LCIM)of CEA List the postdoctoral fellow will contribute to the development of electrochemical sensors based on synthetic diamond and associated measurement protocols for the detection of pollutants such as pharmaceuticals, heavy metals, PFAS, and pesticides. These sensors will be miniaturized and integrated into a microfluidic cell, in partnership with CEA-Leti, then tested under real-world field conditions.
Design and development of a modular high-side test bench for application validation of Grand Gap components
Wide bandgap transistors (GaN, SiC) play a key role in power electronics, but their industrial integration remains hampered by implementation difficulties. The high-side component, within a bridge arm structure, is particularly sensitive to voltage and current transients, which are highly dependent on routing, topology, and switching modes (ZVS, ZCS). Its floating nature makes measurements complex and can disrupt switching during application testing. A methodology adapted to fast transients was developed during a thesis, resulting in a patented test bench for characterizing low-side components. The subject of the postdoctoral research presented here aims to adapt this methodology to high-side components, which are more complex to drive and measure, in order to characterize and model aging due to gate transients under realistic conditions. The test bench will enable the generation of reproducible stress profiles on low-side and high-side components, and the precise measurement of key parameters such as threshold voltage and dynamic instabilities. To achieve these objectives, a new bench will be designed, incorporating specific control and measurement systems, with a view to application testing and targeted aging tests.
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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.