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).
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.