Fracture dynamics in crystalline layer transfer technology
Smart Cut™ is a technology discovered at CEA and now industrially used for the manufacture of advanced substrates for electronics. However, the physical phenomena involved are still the focus of numerous studies at CEA. In Smart Cut™, a thin material layer is transferred from one wafer to another using a key fracture annealing step upon which a macroscopic fracture initiate & propagates at several km/s [i].
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Improving technology requires a solid understanding of the physical phenomena involved in the fracture step. The aim of this PhD project is thus to address the mechanisms involved in fracture initiation, propagation and post-fracture vibrations
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On the CEA-Grenoble site, with industrial interest, the student will use and further develop existing experimental setups to investigate the fracture behavior in brittle materials, including optical laser reflections [iv], time-resolved synchrotron diffracting imaging [iii], and ultra-fast direct imaging [ii].
In addition, python-based data analysis algorithms will be developed to extract quantitative information from the different datasets. This will enable the student to determine involved mechanisms and evaluate the influence of the wafer processing parameters on the fracture behavior, and thus propose improvement methods.
References :
[i] https://pubs.aip.org/aip/apl/article/107/9/092102/594044
[ii] https://journals.aps.org/prapplied/abstract/10.1103/PhysRevApplied.15.024068
[ii] https://journals.iucr.org/j/issues/2022/04/00/vb5040/index.html
[iv] https://pubs.aip.org/aip/jap/article/129/18/185103/158396
Microwave Near Field Sensing in Heterogeneous Media
This thesis focuses on the development of microwave near-field sensing techniques for applications in biomedicine, agronomy, and geophysics. The primary objective is to design low-complexity algorithms that effectively solve complex inverse problems related to the characterization and detection of dielectric properties with various geometric distributions in heterogeneous media.
The candidate will begin by conducting a comprehensive review of existing radar-based and advanced signal processing methods. A precise physical model of microwave propagation in near-field conditions will be developed, serving as the foundation for new detection methods based on the concept of physics-driven iterative tomography. The ultimate goal is to formulate efficient algorithms suitable for real-time applications and validate them through experimental implementation. To achieve this, an evolving prototype setup will be developed, progressing from 2D media to more complex 3D scenarios.
This interdisciplinary project combines physical modeling, algorithm development, and practical experimentation. It presents an opportunity to advance the field of microwave imaging, with significant implications for biomedical and environmental applications.
High-isolation power supply
With the rapid evolution of technologies and the growing challenges of miniaturization and resource management, power converters are facing ever more stringent performance requirements. To meet these needs, the use of wide-bandgap semiconductors such as SiC (silicon carbide) and GaN (gallium nitride) is becoming increasingly common. These materials significantly increase the switching speed of converters, reducing losses and improving efficiency.
However, this switching speed brings additional challenges: the steepness of the switching edges can cause stray currents that interfere with switch controls. To counter these undesirable effects, it is necessary to use switch drivers offering a high level of insulation. The traditional solution is based on high-frequency magnetic transformers, but these devices are expensive, take up a lot of space and offer limited insulation.
Thesis objective: the aim of this thesis is to design a new solution for powering wide-gap component drivers, by replacing magnetic transformers with piezoelectric transformers. This innovative approach aims to reduce costs, space requirements and improve the overall efficiency of power conversion systems.
Supervision and ressources: the selected candidate will work as part of a leading-edge research team, renowned for its expertise in the field of power conversion using piezoelectric resonators. The team has the resources and know-how to support the development and validation of this innovative technology.
Identification versus anonymisation from an embedded client operating on a blockchain
The first worldwide deployment of a blockchain dates back to 2010 with Bitcoin, which introduced a completely digital monetary system and a crypto-currency, bitcoin. Within Bitcoin, all transactions are publicly accessible and traceable, which should generate trust between stakeholders. However, the traceability of transactions, and ultimately of the crypto-currency, does not imply the traceability of users authenticated by an account address, or more precisely by a set of account addresses that are independent of each other. In this context, it can be complex to trace the individuals or legal entities owning the crypto-currency.
Crypto-currency is not the only use case supported by blockchain technology. The deployment of Ethereum in 2014, based on the use of smart contracts, opened up many other uses, in particular the protection of identifying data. In this area, the need for traceability versus furtivity can vary greatly from one use case to another. For example, on a blockchain that records the access of a worker owning an employment certificate to an industrial site, no information enabling the worker to be identified or his activity to be traced should appear. On the other hand, in the case of data collected by IoT sensors and processed by remote Edge devices, traceability of data and processing is desirable.
The thesis proposes to study different techniques for tracing digital assets on a blockchain, for stealthing their owners, and offering the possibility of auditing and identification by an authorised body. The aim is to build embedded devices, Edge or personal possibly embedding artificial intelligence, secured by hardware components, integrating different cryptographic solutions and account, data or identity wallet structures to meet the needs of the different use cases envisaged.
Self Forming Barrier Materials for Advanced BEOL Interconnects
Context : As semiconductor technology scales down to 10 nm and below, Back End of Line (BEOL) scaling presents challenges, particularly in maintaining the integrity of copper interconnects, where line/via resistance and copper fill are key issues. Copper (Cu) interconnections must resist diffusion and delamination while maintaining optimal conductivity. In the traditional Cu damascene process, metal barriers and a Cu seed layer are deposited by PVD to enable electrochemical copper deposition. As dimensions shrink, it becomes increasingly difficult to incorporate tantalum-based diffusion barriers, even with techniques like atomic layer deposition (ALD), as the barrier thickness must be reduced to just a few nanometers. To address this challenge, a self-forming barrier (SFB) process has been proposed. This process uses copper alloys containing elements such as Mn, Ti, Al, and Mg, which segregate at the Cu-dielectric interface, forming an ultra-thin barrier while also serving as a seed layer for electroplating.
Thesis Project: The PhD candidate will join a leading research team to explore and optimize materials for SFBs using Cu alloys. Focus areas include:
- Material Selection & Characterization: develop and analyze Cu alloy thin films by electrochemical and PVD methods to study their microstructure and morphology.
- Barrier Formation: Control alloy migration at the Cu/dielectric interface during thermal annealing and assess barrier effectiveness.
- Electrical & Mechanical Properties: Evaluate SFB impact on electrical resistance, electromigration, and delamination, especially in accelerated tests.
Required skills : Master's degree in electrochemistry or materials science with a strong interest in applied research. A pronounced interest in experimental work, skills in thin film deposition, electrochemistry and materials characterization (AFM, SEM, XPS, XRD, SIMS). You should be able to conduct bibliographic research and organize your work efficiently.
Work Environment: The candidate will work in a renowned laboratory with state-of-the-art 200/300 mm facilities and will participate in the CEA’s NextGen Project on advanced interconnects for high reliability applications.
Advanced functions for monitoring power transistors (towards greater reliability and increased lifespan of power converters for energy)
In order to increase the power of electronic systems, a common approach is to parallelize components within modules. However, this parallelization is complicated by the dispersion of transistor parameters, both initial and post-aging. Fast switching of Wide Bandgap (WBG) semiconductors components often requires slowdowns to avoid over-oscillation and destruction.
An intelligent driving scheme, including adjusted control, control of internal parameters of circuits and devices, as well as a feedback loop, could improve reliability, service life and reduce the risk of breakage.
The objectives of the thesis will be to develop, study and analyze the performance of control and piloting functions of power components, in silicon carbide (SiC) or gallium nitride (GaN), which could ultimately be implemented in a dedicated integrated circuit (ASIC type).
This thesis subject aims to solve critical problems in the parallelization of power components, thus contributing to eco-innovation by increasing the lifespan of power modules.
The design of integrated circuits requires, at the end of the chain, circuit editing and failure analysis tools. One of these tools is the probing of electrical potential levels using an electron beam available in a SEM (Scanning Electron Microscope) to determine the electrical signal present in an area of the circuit, which may be a metal level or a transistor. This electronic probing technique was widely used in the 90s, and then partially abandoned despite a few recurrent publications on the technique. In recent years, this technique has been revived by using the backside of the component, probing via the silicon substrate and accessing the active areas of the component.
These debugging and failure analysis tools are also tools for attacking integrated circuits. This thesis topic falls within the scope of hardware cybersecurity and so-called invasive attacks. The PhD student will implement this electron beam probing technique on commercial SEMs and under conditions of use specific to cybersecurity. Various techniques will be considered to improve the probed signals and their use.
Complex 3D structuring based on DNA origami
The rapid evolution of new technologies, such as autonomous cars and renewable energy, requires the development of increasingly complex structures. To achieve this, many surface patterning techniques are available today. In microelectronics, optical lithography is the standard method for creating micro- and nanometric patterns. However, it remains limited in terms of the diversity of shapes it can produce.
In recent years, a promising approach has been developed within the laboratories of CBS (INSERM in Montpellier) and the CEA Leti (Grenoble): DNA origami assembly. This technology exploits the self-assembly properties of the DNA origami polymer chain. The assembly of nanometric DNA origami ultimately forms micrometric structures. The aim of this PhD is to explore new perspectives by combining 2D and 3D origami to create novel structures. These patterns could be of great interest for applications in fields such as optics or energy.
Power and data transmission via an acoustic link for closed metallic environments
This thesis focuses on the transmission of power and data through metal walls using acoustic waves. Ultimately, this technology will be used to power, read and control systems located in areas enclosed in metal, such as pressure vessels, ship hulls and submarines.
Because electromagnetic waves are absorbed by metal, acoustic waves are needed to communicate data or power through metal walls. These are generated by piezoelectric transducers bonded to either side of the wall. The acoustic waves are poorly attenuated by the metal, resulting in numerous reflections and multiple paths.
The aim of the thesis will be to develop a robust demonstrator of this technology, enabling the remote powering and communication of acoustic data through metal walls. This work will be based on advanced modelling of the acoustic channel in order to optimise the performance of the power and data transmission device. It will also involve developing innovative electronic building blocks to determine and maintain an optimum power transmission frequency, impacted by environmental conditions and typically by temperature.
The goal of this thesis will be the development and implementation of a communication system embedded in an FPGA and/or microcontroller in order to send sensor data through a metal wall of variable thickness. The limitations due to the imperfections of the channel and the electronics will lead to the invention of a large number of compensation methods and systems in the digital and/or analogue domain. Work will also have to be carried out on the choice of piezoelectric transducers and the characterisation of the channel, in conjunction with the acoustic wave activities of the laboratory working on the transmission of acoustic power.
Contact : nicolas.garraud@cea.fr and esteban.cabanillas@cea.fr
Integrity, availability and confidentiality of embedded AI in post-training stages
With a strong context of regulation of AI at the European scale, several requirements have been proposed for the "cybersecurity of AI" and more particularly to increase the security of complex modern AI systems. Indeed, we are experience an impressive development of large models (so-called “Foundation” models) that are deployed at large-scale to be adapted to specific tasks in a wide variety of platforms and devices. Today, models are optimized to be deployed and even fine-tuned in constrained platforms (memory, energy, latency) such as smartphones and many connected devices (home, health, industry…).
However, considering the security of such AI systems is a complex process with multiple attack vectors against their integrity (fool predictions), availability (crash performance, add latency) and confidentiality (reverse engineering, privacy leakage).
In the past decade, the Adversarial Machine Learning and privacy-preserving machine learning communities have reached important milestones by characterizing attacks and proposing defense schemes. Essentially, these threats are focused on the training and the inference stages. However, new threats surface related to the use of pre-trained models, their unsecure deployment as well as their adaptation (fine-tuning).
Moreover, additional security issues concern the fact that the deployment and adaptation stages could be “on-device” processes, for instance with cross-device federated learning. In that context, models are compressed and optimized with state-of-the-art techniques (e.g., quantization, pruning, Low Rank Adaptation) for which their influence on the security needs to be assessed.
The objectives are:
(1) Propose threat models and risk analysis related to critical steps, typically model deployment and continuous training for the deployment and adaptation of large foundation models on embedded systems (e.g., advanced microcontroller with HW accelerator, SoC).
(2) Demonstrate and characterize attacks, with a focus on model-based poisoning.
(3) Propose and develop protection schemes and sound evaluation protocols.