With the advancement of opto-electronic technology, 3D patterns with sub micrometer dimensions are more and more integrated in the device, especially on imaging and AR/VR systems. To fabricate such 3D structures using standard lithography technique requires numerous process steps: multiple lithography and pattern transfer, which is time and resource consuming.
With optical grayscale lithography, such 3D structures can be fabricated in single lithography step, therefore reducing significantly the number of process steps required in standard lithography. For high volume manufacturing of such 3D patterns, optical grayscale lithography with Deep-UV (DUV), 248nm and 193nm are the most relevant, as it is compatible with industrial production line. This technique of 3D lithography is however more complex than it seems, which requires advance lithography model and data-preparation flow to design optical mask corresponding to the desired 3D pattern.
During the past 20 years, « Biomass-to-liquid » processes have considerably grown. They aim at producing a large range of fuels (gasoline, kerozene, diesel, marine diesel oil) by coupling a biomass gazéification into syngaz unit (CO+CO2+H2 mixture) and a Fischer-Tropsch (FT) synthesis unit. Many demonstration pilots have been operated within Europe. Nevertheless, the low H/C ratio of bio-based syngaz from gasification requires the recycling of a huge quantity of CO2 at the inlet of gaseification process, which implies complex separation and has a negative impact on the overall valorization of biobased carbon. Moreover, the possibility to realize, in the same reactor, the Reverse Water Gas Shift (RWGS) and Fischer-Tropsch (FT) reaction in the same reactor with promoted iron supported catalysts has been proved (Riedel et al. 1999) and validated in the frame of a CEA project (Panzone, 2019).
Therefore, this concept coupled with the production of hydrogen from renewable electricity opens new opportunities to better valorize the carbon content of biomass.
The PhD is based on the coupled RWGS+FT synthesis in the same catalytic reactor. On the one hand a kinetic model will be developed and implemented in a multi-scale reactor model together with hydrodynamic and thermal phenomena. The model will be validated against experimental data and innovative design will be proposed and simulated. On the other hand, the overall PBtL process will be optimized in order to assess the potential of such a process.
Turbulent convection is one of the main drivers of geophysical and astrophysical flows, and is therefore a key element in climate modeling. It is also involved in many industrial flows. Transport efficiency is often limited by boundary layers whose nature and transitions as a function of control parameters are poorly understood.
The aim of this thesis will be to set up a convection experiment to probe the dissipation rate in boundary layers in the turbulent regime, using an innovative technique developed in the team: multi-scattered wave spectroscopy.
Neural Networks (NNs) are inspired by the brain’s computational and communication processes to efficiently address tasks such as data analytics, real time adaptive signal processing, and biological system modelling. However, hardware limitations are currently the primary obstacle to widespread adoption. To address this, a new type of circuit architecture called "neuromorphic circuit" is emerging. These circuits mimic neuron behaviour by incorporating high parallelism, adaptable connectivity, and in memory computation. Ion gated transistors have been extensively studied for their potential to function as artificial neurons and synapses. Even if these emerging devices exhibit excellent properties due to their ultra low power consumption and analog switching capabilities, they still need to be validated into larger systems.
At the RF and Energy Components Laboratory of CEA-Leti, we are developing new lithium-gated transistors as building blocks for deploying low-power artificial neural networks. After an initial optimization phase focused on materials and design, we are ready to accelerate the pace of development. These devices now need to be integrated into a real system to assess their actual performance and potential. In particular, both bio-inspired circuits and crossbar architectures for accelerated computation will be targeted.
During this 3-year PhD thesis, your (main) objective will be to design, implement, and test neural networks based on lithium-gated transistor crossbars (5x5, 10x10, 20x20) and neuromorphic circuits , along with the CMOS read and write logic to control them. The networks might be implemented using different algorithms and architectures, including Artificial Neural Network, Spiking Neural Networks and Recurrent Neural Networks, which will be then tested by solving spatial and/or temporal pattern recognition problems and reproduce biological functions such as pavlovian conditioning.
The blockchain is based on a consensus protocol, the aim of which is to share and replicate ordered data between peers in a distributed network. The protocol stack, embedded in the network's peer devices, relies on a proof mechanism that certifies the timestamp and ensures a degree of fairness within the network.
The consensus protocols used in the blockchains deployed today are not suitable for embedded systems, as they require too many communication and/or computing resources for the proof. A number of research projects, such as IOTA and HashGraph, deal with this subject and will be analysed in the state of the art.
The aim of this thesis is to build a consensus protocol that is frugal in terms of communications and computing resources, and whose protocol stack will be implemented in a secure embedded device. This protocol must be based on the proof of elapsed time from our laboratory's work, which is also frugal, called Proof-of-Hardware-Time (PoHT), and must satisfy the properties of finality and fairness. The complete architecture of a peer node in the network will be designed and embedded on an electronic board including a microprocessor and several hardware security components, in such a way that the proof resource cannot be parallelized. Communication between peers will be established in a distributed manner.