Thermodynamic Modelling of Complex Oxides for Smart Sensors

The search for more efficient materials follows a pattern that has changed very little over the years, involving poorly automated phases of synthesis, characterization and measurement of functional properties. Although this pattern has proved its strength in creating knowledge bases, it remains ineffective because it is time-consuming and generally covers a reduced range of compositions. The project Hiway-2-mat (https://www.pepr-diadem.fr/projet/hiway-2-mat/) seeks to use high-throughput combinatorial approaches and develop autonomous configurations to explore the compositional spaces of complex oxide materials, with the aim of accelerating the discovery of materials for smart sensors. In this context, CALPHAD method is a valuable tool for materials exploration, as it can provide a number of useful insights into the role of oxidation state or oxygen partial pressure on phase stability, and on the degree of substitution of doping elements in an oxide matrix. The aim is to calculate phase diagrams of complex oxides based on available databases, either to better prepare combinatorial experiments, or to drive the autonomous robot on the fly, providing additional information for on-line characterization.
Your role will be to:
1)Perform thermodynamic simulations using CALPHAD method and Thermo-Calc Software to predict the stability range of a set of complex oxides (Ba/Ca/Sr)(Ti/Zr/Sn/Hf)O3 at different temperatures and oxygen partial pressures. In this step, the candidate will also perform a critical review of the thermodynamic data available in the literature.
2)Include key elements in the available database.
3)Develop a rapid screening method to search for the most promising compositions.
The candidate will work closely with the experimental platform development team to guide future trials and adapt the method to better meet the needs of large-scale production.

Development of innovative metal contacts for 2D-material field-effect-transistors

Further scaling of Si-based devices below 10nm gate length is becoming challenging due to the control of thin channel thickness. For gate length smaller than 10nm, sub-5nm thick Si channel is required. However, the process-induced Si consumption and the reduction of carrier mobility in ultrathin Si layer can limit the channel thickness scaling. Today, the main contenders that allow the extension of the roadmap to ultra-scaled devices are 2D materials, particularly the semiconducting transition metal dichalcogenides (TMD). Due to their unique atomically layered structure, they offer improved immunity to short-channel-effects in comparison to usual Si-based field-effect-transistors (FETs). This makes them very attractive for the application of more-Moore electronics.
However, the scalability of MOSFET device and the introduction of new material make source and drain contact a major issue. If many efforts have been made, in the past years, to reduce Fermi level pinning and Schottky barrier height, for many, these approaches are not industrially scalable. The main objective of this work is then to propose an in-depth understanding of electrical contact characteristics (based on different material) to identify the lowest contact resistance. The processes involved, offering an optimal contact resistance, must be compatible with wafer-scale processing for an integration in our 200/300mm advanced CMOS platform. The post-doc will in-depth study mechanisms enabling the formation of small contact resistances (between MoS2 and metal). It will have to identify the most promising contact material and to develop the associated deposition processes (ALD/PVD). Finally, electrical characterization of contact will be performed to qualify both material and interfaces enabling optimal operation of future 2D FETs

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).

Strain driven Group IV photonic devices: applications to light emission and detection

Straining the crystal lattice of a semiconductor is a very powerful tool enabling controlling many properties such as its emission wavelength, its mobility…Modulating and controlling the strain in a reversible fashion and in the multi% range is a forefront challenge. Strain amplification is a rather recent technique allowing accumulating very significant amounts of strain in a micronic constriction, such as a microbridge (up to 4.9% for Ge [1]), which deeply drives the electronic properties of the starting semiconductor. Nevertheless, the architectures of GeSn microlasers under strong deformation and recently demonstrated in the IRIG institute [2] cannot afford modulating on demand the applied strain and thus the emission wavelength within the very same device, the latter being frozen “by design”. The target of this 18 months post doc is to fabricate photonic devices of the MOEMS family (Micro-opto-electromechanical systems) combining the local strain amplification in the semiconductor and actuation features via an external stimulus, with the objectives to go towards: 1-a wide band wavelength tunable laser microsource and 2-new types of photodetectors, both in a Group IV technology (Si, Ge and Ge1-xSnx). The candidate will conduct several tasks at the crossroads between fabrication and optoelectronic characterization:
a-simulation of the mechanical operation of the expected devices using FEM softwares, and calculation of the electronic states of the strained semiconductor
b-fabrication of devices at the Plateforme Technologique Amont (lithography, dry etching, metallization, bonding), based on results of a
c-optical and material characterization of the fabricated devices (PL, photocurrent, microRaman, SEM…) at IRIG-PHELIQS and LETI.
A PhD in the field of semiconductors physics or photonics, as well as skills in microfabrication are required.

[1] A. Gassenq et al, Appl. Phys. Lett.108, 241902 (2016)
[2] J. Chrétien et al, ACS Photonics2019, 6, 10, 2462–2469

Development of irradiation resistant silicon materials and integration in photovoltaïcs cells for space applications

Historically, photovoltaic (PV) energy was developed together with the rise of space exploration. In the 90’s, multijunction solar cells based on III-V materials progressively replaced silicon (Si) cells, taking advantage of higher efficiency levels and electrons/protons irradiation resistance. Nowadays, the space environment is again looking at Si based PV applications: request of higher PV power, moderated space mission lengths, cost reduction issues (€/W Si ~ III-V/500), higher efficiencies p-type Si PV cells… Solar cells are exposed to cosmic irradiation in space, especially to electrons and protons fluxes. The latter’s affect the cells performances, essentially because of bulk defect formations and charge carrier recombination. In order to use Si based solar cells in space, we need to increase their irradiation resistance, which is the main goal of this post-doc position. To do so, the work will first consist in elaborating new Si materials, with increased irradiation resistance. Compositional aspects of the Si will be modified, particularly by introducing elements limiting the formation of bulk defects under irradiations, developing electrical passivation properties. The electronic properties of the materials will be deeply characterized before and after controlled irradiation. Then, this Si material will be used to fabricate heterojunction solar cells. Their performances will be evaluated again before and after irradiation. Such experimental work could be supported by numerical simulation at the device scale.

Development of large area substrates for power electronics

Improving the performance of power electronics components is a major challenge for reducing our energy consumption. Diamond appears as the ultimate candidate for power electronics. However, the small dimensions and the price of the substrates are obstacles to the use of this material. The main objective of the work is to overcome these two difficulties by slicing the samples into thin layers by SmartCut™ and by tiling these thin layers to obtain substrates compatible with microelectronics.
For this, various experiments will be carried out in a clean room. Firstly, the SmartCut™ process must be made more reliable. Characterizations such as optical microscopy, AFM, SEM, Raman, XPS, electrical, etc. will be carried out in order to better understand the mechanisms involved in this process.
The candidate might be required to work on other wide-gap materials studied in the laboratory such as GaN and SiC, which will allow him to have a broader view of substrates for power electronics.

Design of 2D Matrix For Silicum Quantum computing with Validation by Simulation

The objective is to design a 2D matrix structure for quantum computing on silicon in order to consider structures of several hundred physical Qubits.

In particular the subject will be focused on:
- The functionality of the structure (Coulomb interaction, RF and quantum)
- Manufacturing constraints (simulation and realistic process constraint)
- The variability of the components (Taking into account the variability parameter and realistic defectivity)
- The constraints induced on the algorithms (error correction code)
- Scalability of the structure to thousands of physical Qubits

The candidate will work within a project of more than fifty people with expertise covering the design, fabrication, characterization and modeling of spin qubits as well as related disciplines (cryoelectronics, quantum algorithms, quantum error correction, …)

Developement of relaxed pseudo-substrate based on InGaN porosified by electrochemical anodisation

As part of the Carnot PIRLE project starting in early 2021, we are looking for a candidate for a post-doctoral position of 24 months (12 months renewable) with a specialty in material science. The project consists in developing a relaxed pseudo-substrate based on III-N materials for µLEDs applications, especially for emission in red wavelength. The work will focus on developing an InGaN-based epitaxy MOCVD growth process, on an innovative substrate based on electrochemically anodized and relaxed materials. He (She) will have characterize both the level of relaxation of the re-epitaxied layer and its crystalline quality. These two points will promote the epitaxial regrowth of an effective red LED. The candidate will be part of the team, working on the PIRLE project, will be associated to the work on red LED growth and its optical and electro-optical characterizations.

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