Crystal plasticity in classical molecular dynamics and mesoscopic upscaling

Thanks to new supercomputer architectures, classical molecular dynamics simulations will soon enter the realm of a thousand billion atoms, never before achieved, thus becoming capable of representing the plasticity of metals at the micron scale. However, such simulations generate a considerable amount of data, and the difficulty now lies in their exploitation in order to extract the statistical ingredients relevant to the scale of "mesoscopic" plasticity (the scale of continuous models).
The evolution of a material is complex, as it depends on lines of crystalline defects (dislocations) whose evolution is governed by numerous mechanisms. In order to feed models at higher scales, the quantities to be extracted are the velocities and lengths of dislocations, as well as their evolution over time. These data can be extracted using specific analysis techniques based on characterization of the local environment ('distortion score', 'local deformation'), a posteriori or in situ during simulation. Finally, machine learning tools can be used to analyze the statistics obtained and extract and synthesize (by model reduction) a minimal description of plasticity for models at higher scales.

Modeling silicon and germanium spin qubits

Silicon/Germanium spin qubits have attracted increasing attention and have made outstanding progress in the past two years. In these devices, the elementary information is stored as a coherent superposition of the spin states of an electron in a Si/SiGe heterostructure, or of a hole in a Ge/SiGe heterostructure. These spins can be manipulated electrically owing to the intrinsic (or to a synthetic) spin-orbit coupling, and get entangled through exchange interactions, allowing for the implementation of a variety of one- and two-qubit gates required for quantum computing and simulation. The aims of this postdoctoral position are to strengthen our understanding and support the development of electron and hole spin qubits based on Si/Ge heterostructures through analytical modeling as well as advanced numerical simulation. Topics of interests include spin manipulation & readout, exchange interactions in 1D and 2D arrays, coherence and interactions with other quasiparticles such as photons. The selected candidate will join a lively project bringing together > 50 people with comprehensive expertise covering the design, fabrication, characterization and modeling of spin qubits. He/She is expected to start early 2023, for up to three years.

Hybrid CMOS / spintronic circuits for Ising machines

The proposed research project is related to the search for hardware accelerators for solving NP-hard optimization problems. Such problems, for which finding exact solutions in polynomial time is out of reach for deterministic Turing machines, find many applications in diverse fields such as logistic operations, circuit design, medical diagnosis, Smart Grid management etc.
One approach in particular is derived from the Ising model, and is based on the evolution (and convergence) of a set of binary states within an artificial neural network (ANN).In order to improve the convergence speed and accuracy, the network elements may benefit from an intrinsic and adjustable source of fluctuations. Recent proof-of-concept work highlights the interest of implementing such neurons with stochastic magnetic tunnel junctions (MTJ).

The main goals will be the simulation, dimensioning and fabrication of hybrid CMOS/MTJ elements. The test vehicles will then be characterized in order to validate their functionality.

This work will be carried out in the frame of a scientific collaboration between CEA-Leti and Spintec.

Charge to spin conversion in HgTe topological insulators

The intrinsic spin-momentum locking of Dirac fermions at the surface or interface of topological insulators opens the path towards novel spintronic effects and applications.
Strained HgTe/CdTe is a model topological insulator and a very good candidate to design and demonstrate new spintronic devices exploiting the very large charge to spin conversion efficiency expected for such 2D systems. This postdoc position aims at realizing the first demonstration of the direct charge to spin conversion in topological HgTe nanostructures and use this demonstration as a building block for spin based logic elements.

Predictive design of heat management structures

Heat management is a paramount challenge in many cutting edge technologies, including new GaN electronic technology, turbine thermal coatings, resistive memories, or thermoelectrics. Further progress requires the help of accurate modeling tools that can predict the performance of new complex materials integrated in these increasingly demanding novel devices. However, there is currently no general predictive approach to tackle the complex multiscale modeling of heat flow through such nano and micro-structured systems. The state of the art, our predictive approach “”, currently covers the electronic and atomistic scales, going directly from them to predict the macroscopic thermal conductivity of homogeneous bulk materials, but it does not tackle a mesoscopic structure. This project will extend this predictive approach into the mesoscale, enabling it to fully describe thermal transport from the electronic ab initio level, through the atomistic one, all the way into the mesoscopic structure level, within a single model. The project is a 6 partner effort with complementary fields of expertise, 3 academic and 3 from industry. The widened approach will be validated against an extensive range of test case scenarios, including carefully designed experimental measurements taken during the project. The project will deliver a professional multiscale software permitting, for the first time, the prediction of heat flux through complex structured materials
of industrial interest. The performance of the modeling tool will be then demonstrated in an industrial setting, to design a new generation of substrates for power electronics based on innovating layered materials. This project is expected to have large impacts in a wide range of industrial applications, particularly in the rapidly evolving field of GaN based power electronics, and in all new technologies where thermal transport is a key issue.