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Offers : 32

Spectroscopy of AlN colored centers

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Start date : 1 April 2019

offer n° PsD-DRF-19-0062

The study of QD-like emission from deep emission centers in semiconductor has become an important topic in the general framework of quantum information processing and nanoscale sensing, the emblematic emission center being the N-V defect in diamonds. Recently, research has been conducted to evaluate the potential of other defects in various materials, for instance in GaN and BN. Oddly, not much is known on color centers in AlN, despite the many assets of this material : it can be epitaxially deposited, high quality bulk substrates are available, it can be processed as high quality factor microcavities.

We propose in this 12 months post-doc to explore the optical properties of deep luminescing centers in AlN. We will study by microphotoluminescence (either cw or time-resolved) various types of AlN : thin AlN grown on Si (possibly processed as membranes), thick AlN grown on sapphire, ensembles and single AlN nanowires.

  • Keywords : Solid state physics, surfaces and interfaces, INAC, PHELIQS
  • Laboratory : INAC / PHELIQS
  • CEA code : PsD-DRF-19-0062
  • Contact : bruno.gayral@cea.fr

Direct interfacing of bio-inspired NEMS Sensors to bio-inspired RRAM spikingnetworkRKS

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Start date : 1 June 2019

offer n° PsD-DRT-19-0075

Extracting useful and compact information from sensor data is key for future mobile and Internet of Things (IoT) applications. Mining data from raw sensors remains an open problem, so that systems capable of handling large volumes of noisy and incomplete real-life data are required. Today, the most promising approach is deep learning. Despite its benefit, the adoption of deep learning within IoT faces significant barriers due to the constraints imposed by mobile devises (memory, power consumption, and limited transmission range).

One possible approach to tackle these challenges is to rethink and reorganize computer architecture taking inspiration of living organisms. Insects are not able to perform calculations like digital systems but excel in controlling small and agile motor systems based on the fusion of data sparse sensory inputs. Moreover, they operate under severe constrains, of energy conservation and limited communication range, among others. Therefore, they provide highly interesting model systems for neuromorphic embedded computation. Resistive RAM (RRAM) are non-volatile memory elements whose values/conductances change as a function of the applied pulses. Thanks to these properties they are prime candidates for implementing plastic synapses in neuromorphic systems. Arrays of micromechanical pillars mimicking the cricket hairs have been demonstrated to be excellent air flow sensors. The main objective of the project is to develop a bio-inspired RRAM-based spiking neural network directly interfaced with a bio-inspired MEMS sensor for readout and local information processing.

The main research objective is the design, fabrication and test of a RRAM-based spiking neural network for the readout of an already available nanomechanical resonator array. The alleged advantages of the proposed bio-inspired design throughout the whole system will be demonstrated by simulations calibrated on the experimental results.

  • Keywords : Engineering science, Electronics and microelectronics - Optoelectronics, DCOS, Leti
  • Laboratory : DCOS / Leti
  • CEA code : PsD-DRT-19-0075
  • Contact : elisa.vianello@cea.fr

Detection of cyber-attacks in a smart multi-sensor embedded system for soil monitoring

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Start date : 1 April 2019

offer n° PsD-DRT-19-0071

The post-doc is concerned with the application of machine learning methods to detect potential cyber-security attacks on a connected multi-sensor system. The application domain is the agriculture, where CEA Leti has several projects, among which the H2020 project SARMENTI (Smart multi-sensor embedded and secure system for soil nutrient and gaseous emission monitoring). The objective of SARMENTI is to develop and validate a secure, low power multisensor systems connected to the cloud to make in situ soil nutrients analysis and to provide decision support to the farmers by monitoring soil fertility in real-time. Within this topic, the postdoc is concerned with the cyber-security analysis to determine main risks in our multi-sensor case and with the investigation of a attack detection module. The underlying detection algorithm will be based on anomaly detection, e.g., one-class classifier. The work has tree parts, implement the probes that monitor selected events, the communication infrastructure that connects the probes with the detector, and the detector itself.

  • Keywords : Engineering science, Computer science and software, Mathematics - Numerical analysis - Simulation, DACLE, Leti
  • Laboratory : DACLE / Leti
  • CEA code : PsD-DRT-19-0071
  • Contact : anca.molnos@cea.fr

Feasability study and development of models towards SPICE-simulation of silicon Qubit quantum circuits

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Start date : 1 August 2019

offer n° PsD-DRT-18-0056

The Compact / SPICE model is the link between the development of technological bricks and circuit design. The model purpose is to accurately reproduce the experimental characteristics essential to digital, analog and mixed circuit design. But today we need deeper investigation to set up the specifications of models for such device, in order to provide adequate tools to help circuit designers building up quantum circuits.

The main challenge is to be able to describe the quantum behavior of this architecture. It will also be necessary to study if this behavior must be described via the physical quantities (eg electronic spin, energy level …) or by logical quantities (quantum state, matrix of transformation, …). It will also be necessary to take into account the compatibility between the mathematical formalism and the standard tools of compact modeling (through Verilog-A description).

Following recent experimental research activities (between CEA and CNRS) concerning the first demonstration of hole spin qubit on SOI, we propose first to investigate how to model such device through macro modeling approach where SET compact model, inclusion of magnetic spin degeneracy and management of RF excitation are main steps.

The challenges in regards to literature are inclusion of magnetic field in SET model, description of resonant tunneling, RF excitation of SET and reproduction of Rabi oscillations.

  • Keywords : Engineering science, Electronics and microelectronics - Optoelectronics, DCOS, Leti
  • Laboratory : DCOS / Leti
  • CEA code : PsD-DRT-18-0056
  • Contact : sebastien.martinie@cea.fr

Nano-silicon/graphene composites for high energy density lithium-ion batteries

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Start date : 1 May 2018

offer n° PsD-DRF-18-0052

This postdoctoral fellowship is part of the Graphene Flagship Core 2 H2020 european project (2018-2020) on the energy storage applications of graphene. In lithium-ion batteries, graphene associated to nanostructured silicon in a proper composite helps increase the energy capacity. Indeed graphene wraps silicon, reducing its reactivity with electrolyte and the formation of the SEI passivation layer. It also maintains a high electrical conductivity within the electrode.

The study will compare two technologies: graphene-silicon nanoparticles and graphene-silicon nanowires. The former composite, already explored in the above mentioned project, will be optimized in the present study. The latter is a new kind of composite, using a large scale silicon nanowire synthesis process recently patented in the lab. The postdoc will work within two laboratories: a technological research lab (LITEN) with expertise in batteries for transportation, and a fundamental research lab (INAC) with expertise in nanomaterial synthesis.

The postdoc will synthesize silicon nanowires for his/her composites at INAC. Following LITEN know-how, she/he will be in charge of composite formulation, battery fabrication and electrochemical cycling. He/she will systematically compare the electrochemical behavior of the nanoparticle and nanowire based silicon-graphene composites. Comparison will extend to the mechanism of capacity fading and SEI formation, thanks to the characterization means available at CEA Grenoble and in the European consortium: X-ray diffraction, electronic microscopy, XPS, FTIR, NMR spectroscopies. She/he will report her/his work within the international consortium (Cambride UK, Genova Italy, Graz Austria) meetings.

A 2-year post-doctoral position is open.

PhD in materials science is requested. Experience in nanocharacterization, nanochemistry and/or electrochemistry is welcome.

Applications are expected before May 31st, 2018.

  • Keywords : Engineering science, Materials and applications, Ultra-divided matter, Physical sciences for materials, INAC, SyMMES
  • Laboratory : INAC / SyMMES
  • CEA code : PsD-DRF-18-0052
  • Contact : pascale.chenevier@cea.fr
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