Actualités : Technologies micro et nano
08 octobre 2019
Optimization of Residual Gas Analysis mass spectrum through machine learning
Context And background : LCFC Lab offers a complete RGA testing solution for outgassing and hermetic studies of microelectronic and MEMS package cavities. Two ultra high vacuum apparatus has been developed as well as a devoted software package for qualitative and quantitative RGA data analysis. To strengthen the RGA activity, software optimization and improvement is […] >>
08 octobre 2019
3D sequential integration for high density sensing applications.
Context : 3D sequential integration enables to achieve the highest 3D contact density between stacked levels compared to other existing techniques. However it requires to process stacked devices with a limited thermal budget. Leti institute is pioneer in this domain and has a unique expertise on low temperature devices for computing applications. This internships goal […] >>
08 octobre 2019
From technology to integrated circuits: optimization and validation of parasitics modeling into PDKs
Context : Parasitic resistances and capacitances in integrated circuit produce circuit performance degradations (i.e speed and power consumption) when CMOS technologies are scaling down. They also need to be accounted accurately while designing Non Volatile Memory (NVM) advanced circuit for neuromorphic applications or high power circuit with GaN technology to anticipate heating effect. Parasitic elements […] >>
08 octobre 2019
Simulation and modelling of interconnect networks for CMOS quantum bit systems
Context : Because it may revolutionize the high performance computing systems, nowadays, silicon quantum computing technologies receive an increasing interest. Based on quantum bit (Qubit), the large potential of those technologies stems from the use of CMOS know-how to adapt the semiconductor qubit in large scale. To achieve efficient control and read-out of qubit with […] >>
08 octobre 2019
Overcoming catastrophic inference in neural networks through accurate overlapping representations
Context: Catastrophic forgetting is the fact that a neural network formed on a first set of elements can forget them when it learns a second set. Therefore, there can be no incremental learning. This is now becoming extremely limiting if we want to develop autonomous systems capable of dealing with situations that could not have […] >>