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Offers : 30

Conformal deposition of polymer thin-films in high aspect ratio 3D structures

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Start date : 1 June 2018

offer n° PsD-DRT-18-0078

The deposition of thin films on challenging high aspect ratio structures are of key importance in many different areas of microelectronics and nano-technologies. For polymer thin films, filament-assisted CVD techniques (such as iCVD) have emerged recently as promising method for the conformal deposition of insulating thin films in 3D structures. However, it is still not clear if this CVD method can allow conformal coating inside porous and 3D substrates with acceptable growth rates and what are the limits of utilization. The work proposed here aims to study polymer thin film deposition by iCVD in high aspect ratio 3D structures in order to identify the parameters governing the deposition speed and the accessible degree of conformality. The works will be performed on high aspect ratio Through Silicon Vias and on various porous substrates. The candidate will be in charge of thin films deposition on a 200 mm tool and of the material characterization. The thin films will be characterized using physicochemical analyses (FTIR, X-Ray Reflectometry, Ellipsometry, Porosimetry, Contact angle, AFM). More in depth characterizations (using Electronic Microscopy, ToF-SIMS) will be carried out to study the deposition in 3D structures.

The main objective of the work will be to identify the key parameters that play a role in the conformal deposition inside 3D structures and porous substrates as a function of the feature shape and size. The work will be done in the LETI/DTSi division. The material deposition and characterizations will be performed in the LETI clean room in close collaboration with an industrial partner. Part of the work will be done in collaboration with experts of materials characterization (CEA nanocharacterization platform), and specialists in charge of 3D integration.

  • Keywords : Engineering science, Materials and applications, Solid state physics, surfaces and interfaces, DTSI, Leti
  • Laboratory : DTSI / Leti
  • CEA code : PsD-DRT-18-0078
  • Contact :

Modeling silicon-on-insulator quantum bits

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Start date : 1 September 2018

offer n° PsD-DRF-18-0073

Quantum information technologies on silicon have raised an increasing interest over the last five years. CEA is pushing forward its own original platform based on the “silicon-on-insulator” (SOI) technology. The information is stored in the spin of carrier(s) trapped in quantum dots, which are etched in a thin silicon film and are controlled by metal gates. SOI has many assets: the patterning of the thin film can produce smaller, hence more scalable qubits; also, the use of the silicon substrate beneath as a back gate provides extra control over the quantum bits (qubits).

Many aspects of the physics of silicon spin qubits are still poorly understood. It is, therefore, essential to complement the experimental activity with state-of-the-art modeling. For that purpose, CEA is actively developing the “TB_Sim” code. The aims of this 2-year post-doctoral position are to model spin manipulation and readout in SOI qubits, and to model decoherence and relaxation at the atomistic scale using TB_Sim. This modeling work will be strongly coupled to the experimental activity in Grenoble. The candidate will have access to experimental data on state-of-the-art devices.

  • Keywords : Theoretical physics, Solid state physics, surfaces and interfaces, Theoretical Physics, INAC, MEM
  • Laboratory : INAC / MEM
  • CEA code : PsD-DRF-18-0073
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AlGaN/GaN HEMTs transfert for enhanced electrical and thermal performances

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Start date : 1 April 2018

offer n° PsD-DRT-18-0060

Due to their large critical electric field and high electron mobility, gallium nitride (GaN) based devices emerge as credible candidates for power electronic applications. In order to face the large market needs and benefit from available silicon manufacturing facilities, the current trend is to fabricate those devices, such as aluminum gallium nitride (AlGaN)/GaN high electron mobility transistors (HEMTs), directly on (111) silicon substrates. However, this pursuit of economic sustainability negatively affects device performances mainly because of self-heating effect inherent to silicon substrate use. New substrates with better thermal properties than silicon are desirable to improve thermal dissipation and enlarge the operating range at high performance.

A Ph.D. student in the lab. has developed a method to replace the original silicon material with copper, starting from AlGaN/GaN HEMTs fabricated on silicon substrates. He has demonstrated the interest of the postponement of a GaN power HEMT on a copper metal base with respect to self heating without degrading the voltage resistance of the component. But there are still many points to study to improve the power components.

Post-doc objectives : We propose to understand what is the best integration to eliminate self-heating and increase the voltage resistance of the initial AlGaN/GaN HEMT. The impact of the component transfer on the quality of the 2D gas will be analyzed.

The same approach can be made if necessary on RF components.

Different stacks will be made by the post-doc and he will be in charge of the electrical and thermal characterizations. Understanding the role of each part of the structure will be critical in choosing the final stack.

This process will also be brought in larger dimensions.

This post-doc will work if necessary in collaboration with different thesis students on power components.

  • Keywords : Engineering science, Materials and applications, Thermal energy, combustion, flows, DCOS, Leti
  • Laboratory : DCOS / Leti
  • CEA code : PsD-DRT-18-0060
  • Contact :


Mail Sélection

Start date : 4 June 2018

offer n° IMEPLaHC-03082018-CMNE



Nanonets2Sense is an H2020 Research and Innovation Action which is developing integrated sensing devices for health and well-being applications, with the objective of providing a low-cost, highly sensitive and robust solution for Point-of-Care applications. This is a field of intense research, with strong innovation potential and real opportunities for future industrial development.
The devices under study are taking advantage of the sensitivity of nanowires to changes in their surface charge by means of the well-known field-effect.
The originality of the project lies in the fact that we are using nanowire based structures called nanonets which can be stacked above a CMOS readout circuit using a “System-on-chip” 3D integration scheme. The device is meant to detect DNA sequences by prior functionalization of the nanonet surface by proper DNA probes, complementary to the target sequence.
The aim of this post-doc is to optimize the sensing device structure and the functionalization process for optimum sensitivity. This will be conducted using pseudo MOSFET measurements made on SOI structures in order to be able to screen numerous functionalization schemes before transfer to nanonet-based structures.

The Post Doc candidate should have a PhD in electrical characterization and modelling of semiconductor devices.
The post doc’s work will require competences in device electrical characterization at wafer level as well as use of elementary chemical processes for device bio functionalization.
Fluent oral and written communication skills in English are required. French speaking skills are welcome.

Net salary: 1920€/month.

Contract duration: one year renewable once.

Contact: Mireille MOUIS, project coordinator, Tel: +33456529535

  • Keywords : Engineering science, Electronics and microelectronics - Optoelectronics, FMNT, IMEP-LaHc
  • Laboratory : FMNT / IMEP-LaHc
  • CEA code : IMEPLaHC-03082018-CMNE
  • Contact :

3D occupancy grid analysis with a deep learning approach

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Start date : 1 February 2018

offer n° PsD-DRT-18-0042

The context of this subject is the development of autonomous vehicles / drones / robots.

The vehicle environment is represented by a 3D occupancy grid, in which each cell contains the probability of presence of an object. This grid is refreshed over time, thanks to sensor data (Lidar, Radar, Camera).

Higher-level algorithms, like path planning or collision avoidance, think in terms of objects described by their path, speed, and nature. It is thus mandatory to get these objects from individual grid cells, with clustering, classification, and tracking.

Many previous publications on this topic comes from the context of vision processing, many of them using deep learning. They show a big computational complexity, and do not benefit from occupancy grids specific characteristics (lack of textures, a priori knowledge of areas of interest…). We want to explore new techniques, tailored to occupation grids, and more compatible with embedded and low cost implementation.

The objective of the subject is to determine, from a series of 3D occupation grids, the number and the nature of the different objects, their position and velocity vector, exploiting the recent advances of deep learning on unstrucured 3D data.

  • Keywords : Engineering science, Computer science and software, DACLE, Leti
  • Laboratory : DACLE / Leti
  • CEA code : PsD-DRT-18-0042
  • Contact :
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