Scientific direction Development of key enabling technologies
Transfer of knowledge to industry

PhD : selection by topics

Modélisation/caractérisation mécanique et triboélectrique du procédé de nanoimpression en interfaces souples

Département Technologies Silicium (LETI)

Laboratoire

01-10-2019

SL-DRT-19-0977

hubert.teyssedre@cea.fr

The flexible molds used in nanoimprint lithography allow to reduce the impact of a particle on the defectivity of a patterning step: its flexibility is used to conform the shape of the defects without impacting the surrounding structures. This flexibility is usually obtained by using single-material or composite polymer materials that have the ability to reproduce patterns having critical dimensions of a few tens of nanometers. The state of the art materials can be transformed from a viscous state (and thus able to flow in nanostructures) at room temperature to a state of elastic solid by photo-polymerization at 365 nm while having an anti-adhesive free surface. This elastic state is fundamental for the performance of replications: the material must have sufficient stiffness to prevent buckling or irreversible deformation during the process, but it must have enough flexibility to be demolded from the resin to be printed without damaging the patterns created in the latter. Nevertheless the use of these flexible molds reinforces the appearance of electrostatic charges during the separation of the mold and the substrate. These charges are usually dissipated macroscopically by means of antistatic bars or ionized air jets, but they can persist on the extreme surface of the flexible stamp and cause deformation of the structures. The objective of this thesis is to study through AFM measurements the behavior of these interfaces.

Spike based processing chain for signal classification

Département Architectures Conception et Logiciels Embarqués (LIST-LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-09-2019

SL-DRT-19-0990

dominique.morche@cea.fr

The expansion of the internet of things is conditioned by our ability to develop innovative systems able to apprehend and understand the environment while having an ultra low power consumption, compatible with energy harvesting. To reach such a goal, one of the solution which is knowing a considerable renewed interest is the use of acoustic signals. Their low frequencies undoubtedly induces a low power consumption in the circuit interface and their low cost eases the dissemination of this solution. There's a huge applicative potential: wake-up by key words (the well known ?ok google?), choc detection, source localization, event classification, surveillance, and machine health monitoring. In order to implement such complex functions in an energy efficient manner, the potential of neural networks is more and more considered. However today, these solutions are too power consuming. To reduce this power, several alternatives are considered. One of the most promising is the coding of the signal in spike, coherently with neuromorphic architecture. Recently, CEA-LETI has developed a new ADC architecture which directly generate some spike and the best power efficiency in the state of the art has been reached. The aim of this PhD is to follow up this work by implementing in the analog domain some feature extraction in order to reduce the complexity of the neural network processing. To reach the best energy efficiency, a joint optimization between the analog, digital and algorithmic part is mandatory. In the scope of this PhD, CEA-LETI and EPDFL are collaborating to develop this new analog processing interface, adapted to neural networks based on spike processing. The main objective is ti setup a methodology to reduce the power consumption in all the sensing systems. The automotive applications will be particularly considered. Other application areas and different kind of signal might be also studied.

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