Study of the thermo-mechanical strains in the HEMT AlGaN/GaN on silicon
Fabricating the HEMT AlGaN/GaN device is complex and leads to the formation of crystalline defects. These strains, in the GaN layer, leads to crackings in the GaN layer or leads to a delamination at the top interface. Moreover, these mechanical strains conjugated to thermal strains during device working, can lead to a degradation of the electrical performance of the device.
This heterogeneous assembly, involve a complex behaviour. The various materials used, react differently to the thermal-mechanical strains. The requested work is to study and to model the distortion of this structure, in order to evaluate the strains effects on the electrical performance on lateral and vertical devices.
Automatic driving of a finite element software based upon a domain decomposition strategy. Application to ultrasonic non-destructive testing.
One the most important field of activity at the DISC (Department of Imaging and Simulation for Control) of CEA - LIST is to provide a comprehensive set of tools for modeling and simulation for Non-Destructive Testing (NDT). These tools are gathered within the computational platform CIVA. Most of the ultrasound models -- elaborated by the LSMA (research laboratory for Simulation and Modeling in Acoustics) -- are based upon semi-analytical methods. Although very efficient, these methods suffer from a loss of precision as soon as some critical phenomena (e.g. head waves or caustics) or some particular features of the material (e.g. flaws or heterogeneities ) appear in the control experiment. To circumvent these limitations, one of the field of research in the LSMA is to build coupling schemes between semi-analytical and numerical methods. Following this strategy, a computational software based upon high-order finite elements combined with domain decomposition strategies is developped in order to address 3D configurations. The work proposed here focuses on increasing the complexity of the configurations reachable within this coupling strategy. A typical example being the fluid-structure interaction in the case of flaws reaching the bottom of the material to control.
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 “ShengBTE.org”, 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.
Scalable digital architecture for Qubits control in Quantum computer
Scaling Quantum Processing Units (QPU) to hundreds of Qubits leads to profound changes in the Qubits matrix control: this control will be split between its cryogenic part and its room temperature counterpart outside the cryostat. Multiple constraints coming from the cryostat (thermal or mechanical constraints for example) or coming from Qubits properties (number of Qubits, topology, fidelity, etc…) can affect architectural choices. Examples of these choices include Qubits control (digital/analog), instruction set, measurement storage, operation parallelism or communication between the different accelerator parts for example. This postdoctoral research will focused on defining a mid- (100 to 1,000 Qubits) and long-term (more than 10,000 Qubits) architecture of Qubits control at room temperature by starting from existing QPU middlewares (IBM QISKIT for example) and by taking into account specific constraints of the QPU developed at CEA-Leti using solid-state Qubits.
Application of formal methods for interferences management
Within a multidisciplinary technological research team of experts in SW/HW co-design tools by applying formal methods, you will be involved in a national research project aiming at developing an environment to identify, analyze and reduce the interferences generated by the concurrent execution of applications on a heterogeneous commercial-off-the-shelf (COTS) multi-core hardware platform.
Design of in-memory high-dimensional-computing system
Conventional von Neumann architecture faces many challenges in dealing with data-intensive artificial intelligence tasks efficiently due to huge amounts of data movement between physically separated data computing and storage units. Novel computing-in-memory (CIM) architecture implements data processing and storage in the same place, and thus can be much more energy-efficient than state-of-the-art von Neumann architecture. Compared with their counterparts, resistive random-access memory (RRAM)-based CIM systems could consume much less power and area when processing the same amount of data. This makes RRAM very attractive for both in-memory and neuromorphic computing applications.
In the field of machine learning, convolutional neural networks (CNN) are now widely used for artificial intelligence applications due to their significant performance. Nevertheless, for many tasks, machine learning requires large amounts of data and may be computationally very expensive and time consuming to train, with important issues (overfitting, exploding gradient and class imbalance). Among alternative brain-inspired computing paradigm, high-dimensional computing (HDC), based on random distributed representation, offers a promising way for learning tasks. Unlike conventional computing, HDC computes with (pseudo)-random hypervectors of D-dimension. This implies significant advantages: a simple algorithm with a well-defined set of arithmetic operations, with fast and single-pass learning that can benefit from a memory-centric architecture (highly energy-efficient and fast thanks to a high degree of parallelism).
Simulation of silicon solar cells based on n-type material : modelling and architecture optimisation.
INES is actually developping new fabrication technologies for n-type silicon solar cells. Working on simulation of photovoltaic solar cells enables the speed-up of the developement of new technologies: physical interpretation of characterisation results, support to device design, optimisation of processing steps and evaluation of original designs.
This subject open for post-doc position is focused on the study of semi-empirical models for materials and process steps for n-type solar cells. These basic road-blocks will be assembled in a complete model by using a multi-scale simulation tool. In the end, this global model will allow optimising of the p-type emitter geometrical structure, the efficiency of carrier collection on the back side or the geometry of metallisation for electrical contacts.
Electrochemical device for purifying hydrogen in a reformed gas
This project aims to establish a new research and development on purification devices for fuel reformers for hydrogen fuel cells. This work is of prime importance for fuel cell systems fed by different sources of hydrogen. Used in "power full" or "range extender" modes, the reformer and gas purification system are elements of the chain that have to be optimized.
Objective is to develop an electrochemical device for purifying the gas from a reformer whose basic principle is similar to that of a PEM electrolyzer. The gases from the reformer undergo a selective electrocatalytic oxidation to separate hydrogen and conventional pollutants directly power a fuel cell.
The project will focus on selection and characterization of catalysts electrocatalytic performance and the achievement of functional prototypes. These developments will assess the economic relevance of the device vis-à-vis other systems and identify areas of research to develop thereafter.
Abstract interpretation of ACSL annotations
Frama-C is a set of tools dedicated
to the analysis of C software. In Frama-C, different analyses
techniques are implemented as plug-ins within the same framework.
Part of the glue that holds the various plug-ins together is
the ACSL annotation language. ACSL is a formal specification
language for C programs.
Each verification plug-in is supposed to interpret ACSL
annotations as best it can. A plug-in can also, when it needs to
make an assumption, express it as an ACSL property so that
another plug-in can be used to verify this assumption.
This post-doctoral position consists in improving the precision of Frama-C’s value analysis, based on Abstract Interpretation, for constructs that are not currently handled. The treatment of some constructs will require specific abstract domains to be designed.
http://frama-c.com
http://frama-c.com/value.html
http://frama-c.com/acsl.html