Magnetic Tunnel Junctions at Boundaries
Spin electronics, thanks to the additional degree of freedom provided by electron spin, enables the deployment of a rich physics of magnetism on a small scale, but also provides breakthrough technological solutions in the field of microelectronics (storage, memory, logic, etc.) as well as for magnetic field measurement.
In the field of life sciences and health, giant magnetoresistance (GMR) devices have demonstrated the possibility of measuring the very weak fields produced by excitable cells on a local scale (Caruso et al, Neuron 2017, Klein et al, Journal of Neurophysiology 2025).
Measuring the information contained in the magnetic component associated with neural currents (or magnetophysiology) can, in principle, provide a description of the dynamic, directional and differentiating neural landscape. It could pave the way for new types of implants, thanks to their immunity to gliosis and their longevity.
The current bottleneck is the very small amplitude of the signal produced (<1nT), which requires averaging the signal in order to detect it.
Tunnel magnetoresistances (TMR), in which a spin-polarised tunnel current is measured, offer sensitivity performance that is more than an order of magnitude higher than GMR. However, they currently have too high a level of low-frequency noise to be fully beneficial, particularly in the context of measuring biological signals.
The aim of this thesis is to push back the current limits of TMRs by reducing low-frequency noise, positioning them as break sensors for measuring very weak signals and exploiting their potential as amplifiers for small signals.
To achieve this objective, an initial approach based on exploring the materials composing the tunnel junction, in particular those of the so-called free magnetic layer, or on improving the crystallinity of the tunnel barrier, will be deployed. A second approach, consisting of studying the intrinsic properties of low-frequency noise, particularly in previously unexplored limits, at very low temperatures where intrinsic mechanisms are reached, will guide the most promising solutions.
Finally, the most advanced structures and approaches at the state of the art thus obtained will be integrated into devices that will provide the building blocks for going beyond the state of the art and offering new possibilities for spin electronics applications. These elements will also be integrated into systems for 2D (or even 3D) mapping of the activity of a global biological system (neural network) and for evaluating capabilities for clinical cases (such as epilepsy or motor rehabilitation).
It should be noted that these improved TMRs may have other applications in the fields of physical instrumentation, non-destructive testing, and magnetic imaging.
Optimization of gamma radiation detectors for medical imaging. Time-of-flight positron emission tomography
Introduction
Innovative functional imaging technologies are contributing to the CEA's ‘Medicine for the Future’ priority. Positron emission tomography (PET) is a nuclear medical imaging technique widely used in oncology and neurobiology. The decay of the radioactive tracer emits positrons, which annihilate into two photons of 511 keV. These photons are detected in coincidence and used to reconstruct the distribution of tracer activity in the patient's body.
We're proposing you to contribute to the development of an ambitious, patented technology: ClearMind. The first prototype is in our laboratories. This gamma photon detector uses a monolithic scintillating crystal of high density and atomic number, in which Cherenkov and scintillation photons are produced. These optical photons are converted into electrons by a photoelectric layer and multiplied in a MicroChannel plate. The induced electrical signals are amplified by gigahertz amplifiers and digitized by SAMPIC fast acquisition modules. The opposite side of the crystal will be fitted with a matrix of silicon photomultiplier (SiPM).
Today we have our first prototype, and we are preparing two more.
The proposed work
You will work in an advanced instrumentation laboratory in a particle physics environment .
The first step will be to optimize the "components" of ClearMind detectors, in order to achieve nominal performance. We'll be working on scintillating crystals, optical interfaces, photoelectric layers and associated fast photodetectors (MCP-PMT and SiPM), and readout electronics.
We will then characterize the performance of the prototype detectors on our measurement benches, which are under continuous development. The data acquired will be interpreted using in-house analysis software written in C++ and/or Python.
Finally, we will compare the physical behavior of our detectors to Monté-Carlo simulation software (Geant4/Gate).
A particular effort will be devoted to the development of ultra-fast scintillating crystals in the context of a European collaboration.
Supervision
The successful candidate will work under the joint supervision of Dominique Yvon and Viatcheslav Sharyy (DRF/IRFU & BIOMAPS). The CaLIPSO group at IRFU & BIOMAPS specializes in the development and characterization of innovative PET detectors, including detailed detector simulation. As part of the project, we are working closely with IJCLabs in Orsay, which is developing our readout and acquisition electronics, CEA/DM2S, which is working in particular on trusted AI algorithms, CPPM in Marseille, which is evaluating our detectors under PET imaging acquisition conditions, and UMR BIOMAPS (CEA/SHFJ), working on image calculation algorithms.
Requirements
Knowledge of the physics of particle-matter interaction, radioactivity and the principles of particle detectors is essential. A strong interest in instrumentation and laboratory work is recommended. Basic programming skills, e.g. C++, Gate/Geant4 physics simulation software, are important.
Skills acquired
Good knowledge of state-of-the-art particle detector and positron emission tomography technologies. Simulation principles and techniques for particle-matter interaction and detection systems. Analysis of complex data.
Contact
Dominique Yvon, dominique.yvon@cea.fr
Viatcheslav Sharyy, viatcheslav.sharyy@cea.fr