(en anglais) Theoretical study of thermoelectric properties of 2D materials
A partir de février 2021
Effective thermal management and energy harvestingbecome critical issues to improve the sustainability of our electrical energy consumption. Thanks to their unique electrical, mechanical and thermal properties, nanostructuresbased on 2D materials are expectedto increase the energy efficiency of electronic devices.
On one hand, active thermoelectric materials that can recycle wasted energy in electronic circuits must have low thermal conductivity and high electrical conductivity. Unfortunately, due to the Wiedemann Franz law, such behaviors are antonymic in common bulk materials, but should be achieved in nanostructured systems. On the other hand, for thermal management (TM) of heat in excess (to be recycled or discharged), materials having as high thermal conductivities as possible are required. The optimization of TM or TE systems based on 2D nanostructures which are smaller than the mean free path of charge and heat carriers requires an accurateunderstanding of non-equilibrium thermal transport. Thus, specific experimental setup and advanced numerical models must be designed.This internship is focused on the numerical aspects in the framework of the flagship project MACQCAQU belonging tothe NanoSaclay LABEX.However, this work will be performed in strong relationship with the experimentalists involved in the project.
Methodology and Objectives
The objective of this work is to perform a numerical study of the thermal and thermoelectric properties of several 2D materialsand theirheterostructruresby focusing on non-equilibrium transport phenomena, geometric effects and electron-phonon coupling. Among the 2D materials, layered MoS2will be studied first because it is well known in the literature, and then SnS2for its promising TE properties.First, ab-initiocalculations based on the density functional theory (DFT) will be performed to calculate the both the electron and phonon dispersions using the Quantum Espresso software. Then, phonon transport will be studied using ahomemade code based onNon Equilibirum Green’sFunction (NEGF) formalism [3, 4] using a dynamic matrix extracted from DFT. Different types of 2D nanostructures will be evaluated in terms of thermal and thermoelectric properties.
The student will acquire a broad range of skills: in solid state physics (band structure, phonon, electron quantum transport, electron-phonon interaction), technology devices, and scientific computing (DFT software) and programming (Fortran and / or C / C + +, Matlab).Besides, theresults that would be obtained during this internship could be easily published in scientific journals.This work could be a relevant preliminary step for a PhDthesis in our group.
Candidates must have a MSc in Physics, Electronics, Materials Science or related disciplines. We are seeking creative and highly motivated individuals well trained and skilled in scientific research, and available to collaborate in an interdisciplinary team. Programming experience is also desirable, but not mandatory. Please join a CV, a list of courses that you have followed and results of exams in the framework of your master program, and any other information that you judge useful.
Wang, Y.; Xu, N.; Li, D.; Zhu, J. Thermal Properties of Two Dimensional Layered Materials. Adv. Funct. Mater. 2017, 27 (19), 1604134. https://doi.org/10.1002/adfm.201604134
Principi, A.; Vignale, G. Violation of the Wiedemann-Franz Law in Hydrodynamic Electron Liquids. Phys. Rev. Lett. 2015, 115 (5), 056603. https://doi.org/10.1103/PhysRevLett.115.056603
 VT Tran, J Saint-Martin, P Dollfus, S Volz, Optimizing the thermoelectric performance of graphene nano-ribbons without degrading the electronic properties, Scientific reports 7(1), 1-11, 2017 DOI:10.1038/s41598-017-02230-0
 M. Pala, P. Giannozzi, D.Esseni, Unit cell restricted Bloch functions basis for first-principle transport models: Theory and application, Physical Review B 102 (2020), 045410. https://doi.org/10.1103/PhysRevB.102.045410
(en anglais) Theoretical study of thermoelectric properties beyond the linear response of Single Electron Transistor
A partir de février 2021
Specific properties of nanostructures have generated a recent revival of interest in thermoelectric devices. Thanks to their delta-like density of states, devices based on quantum dots are expected to exhibit high Seebeck coefficient, nearly zero electronic thermal conductance and ultra-low phononic thermal conductance if embedded in an oxide matrix. Due to single-electron tunneling across discrete levels in theQuantum Dot(QD), such devices are likelyto behave as quasi-ideal energy filters giving rise to incomparable thermoelectric properties,i.e. with anefficiency very close to the ideal Carnot efficiency.An internship position dedicated to the simulationof such device is available in the COMputational electronICS groupbelonging to Center of Nanosciences and Nanostructures.
 Zhi-Gang Chen, Guang Han, Lei Yang, Lina Cheng, Jin Zou, In Progress in Natural Science: Materials International, Volume 22, Issue 6, Pages 535-549(2012)https://doi.org/10.1016/j.pnsc.2012.11.011. Mahan, G. & Sofo, J. The best thermoelectric. Proc. Natl. Acad. Sci. 93, 7436–7439 (1996). DOI 10.1073/pnas.93.15.7436Talbo, V., Galdin-Retailleau, S., Valentin, A. & Dollfus, P. IEEE Transactions on Electron Devices 58, 3286–3293 (2011). DOI 10.1109/TED.2011.2161611 Vincent Talbo, Jérome Saint-Martin, Sylvie Retailleau, and Philippe Dollfus, Scientific Reports, 7, 14783 (2017).https://www.nature.com/articles/s41598-017-14009-4 G. Benenti, G. Casati, K. Saito, et R. S. Whitney, Physics Reports, vol. 694, p. 1‑124(2017).
Methods and techniques
By using our homemade code consisting in a 3D Poisson-Schrödinger solver and the resolution of the Master equation[3,4], the thermoelectric properties of a Si-quantum dot-based single-electron transistor operating in sequential tunneling regime are investigated in terms of thermoelectric figure of meritZT, efficiency and power(cf. Fig 1 and Fig 2.). By taking into account the phonon-induced collisional broadening of energy levels in the quantum dot, bothheat and electrical currents are computed in a voltage and temperature rangesbeyond the linear response.
poursuite en thèse envisageable
(en anglais) Monte Carlo Simulation of static and dynamic thermal properties of nanostructures
A partir de février 2021
Nanostructures and in particular nanowireshave acquired in the last years a prominent role in several cutting-edge researchesand could be used in particular as a material for renewable energy. As the Fourier heat equation does not rigorouslydescribe the thermal transport at the nanoscaledue to the occurrence of out of equilibrium phenomena, we have developed a unique home-made Monte Carlo simulatorbased on the Boltzmann’s transport equation for phonons. Our advanced simulatorspecifically developed to the nanoscale includes a Full-band description of the material properties (dispersioncf. Fig. 2 and scattering rates) that are parametrized by using ab-initio calculations . An internship position is available in theCOMputationnal electronICS groupwhich aims toinvestigate the nanoscale heat transfer by using our ab-initio Monte Carlo Simulator.
Methodology and objectives
The internship has 3 objectives:
(i) Using the available code to study the thermal transportacross single interfacesbetween different materials/phase,
(ii) Computing the static and transient thermal properties of polytype nanowires (cf. fig 1) with a realistic geometry,
(iii) Making comparison between theoretical and experimental results.
Skills learned during the thesis
The student will acquire a broad range of skills: in solid state physics (phonon transport, band structure, phonon spectrum, electron-phonon interaction and phonon-phonon interaction), technology devices, and scientific programming (Fortran and/or C/C++, Matlab).Besides, theresults that would be obtained during this internshipcould be easily published in scientific journals. This internship could be a relevant preliminary work for pursuing aPhd thesis in our group.
Candidates must be at least in the first year of the Master programin Physics, Electronics, Materials Science or related disciplines. We are seeking creative and highly motivated individuals well trained and skilled in scientific research. Programming experience is also desirable, but not mandatory. Please join a CV, a list of courses that you have followed and results of exams in the framework of your master program, and any other information that you judge useful.
 Vineis, C. J., Shakouri, A. , Majumdar, A. and Kanatzidis, M. G. (2010), Nanostructured Thermoelectrics: Big Efficiency Gains from Small Features. Adv. Mater., 22: 3970-3980. doi:10.1002/adma.201000839  L. Vincent, et al., Novel Heterostructured Ge Nanowires Based on Polytype Transformation, Nano Lett., 14 (8), pp 4828–4836 (2014) Davier, B., Larroque, J., Dollfus, P., Chaput, L., Volz, S., Lacroix, D., & Saint-Martin, J. Heat transfer in rough nanofilms and nanowires using Full Band Ab Initio Monte Carlo simulation. Journal of Physics: Condensed Matter,30(49), 495902 (2018) Chaput, L., Larroque, J., Dollfus, P., Saint-Martin, J., & Lacroix, D. (2018). Ab initio based calculations of the thermal conductivity at the micron scale. Applied Physics Letters, 112(3), 033104.  Larroque, J., Dollfus, P.,& Saint-Martin, J. (2018). Phonon transmission at Si/Ge and polytypic Ge interfaces using full-band mismatch based models. Journal of Applied Physics, 123(2), 025702
ELABORATION et CARACTERISATION de couches minces de Ge/Si épitaxiées sur substrat de Silicium
A partir de janvier 2021
Ce stage qui s’inscrit dans une collaboration entre le C2N (centre de Nanosciences et de Nanotechnologies) et l’ IRFU (Institut de recherche sur les lois Fondamentales de l’univers), fait partie d’un projet interne du CEA intitulé : Nanostructures Si/Ge/Si pour détecteurs pixels.
Ces détecteurs pixels permettent de reconstruire les trajectoires des particules chargées au plus près du point de collision. Le projet DOTPIX (Development Of a the Technology needed for a large micrometric resolution PIXel position sensitive detector) consiste donc à développer ces détecteurs sensibles à la position des pixels de grande résolution micrométrique puis de les caractériser en vue de leur utilisation sur des expériences de physique des particules auprès de futurs collisionneurs e+e-.
Le stage intervient dans l’étape de développement de la technologie Ge On Si nécessaire au fonctionnement du dispositif DOTPiX. Le/la candidat(e) effectuera ce stage au sein de l’équipe Seeds du C2N qui a une longue expérience sur la croissance par épitaxie de matériaux désaccordés. Il/elle sera en charge de l’épitaxie de couches d’épaisseur nanométrique de Si, SiGe et Ge sur des substrats de Si (texturé ou non). La technique de croissance cristalline utilisée est l’UHV-CVD (Ultra High Vacuum Chemical Vapor Deposition) et le travail s’effectuera principalement dans la salle blanche du C2N. Il/elle travaillera aussi aux caractérisations in et ex-situ de ces couches avec les moyens disponibles au C2N (RHEED, MEB, AFM, XPS, ellipsométrie, caractérisations électriques). Il/elle sera également amener à collaborer avec d’autres laboratoires comme le GEMAC (Groupe d'Étude de la Matière Condensée) pour les analyses SIMS et l’IRFU pour des caractérisations électriques complémentaire et des simulations numériques si nécessaire.
Laboratoire et équipe d'accueil
C2N, CNRS / Université Paris Saclay, 10 Boulevard Thomas Gobert, 91120 Palaiseau FRANCE Département Matériaux / Equipe SEEDs
Contact: Géraldine Hallais
Téléphone : 01 70 27 03 49 / E-mail geraldine.hallais @c2n.upsaclay.fr
Dates : janvier à septembre 2021
(en anglais) Artificial intelligence models for opto-mechanical silicon metamaterials
A partir de janvier 2021
Stimulated Brillouin scattering (SBS), mediating (THz) photons and (GHz) acoustic phonons, has an immense potential for opto-acoustic signal processing which have no analogue in conventional electro-optic or all-optic approaches. Namely, SBS has shown ultra-high-resolution filtering and remarkable low noise signal regeneration. Furthermore, the narrow linewidth and low phase noise of Brillouin lasers, make them an ideal solution for high-performance micro-wave signal generation.
SBS has been extensively developed in optical fibers, however the phonon leakage towards the cladding in conventional silicon-on-insulator (SOI) waveguides precluded the observation of SBS in Si photonics. However, the recent development of a new generation of Si optomechanic waveguides (see Fig. 1) has removed this barrier, revolutionizing the field and allowing the experimental demonstration of SBS nonlinearities surpassing Kerr and Raman effects (in 2013 ), complete phononic bandgap (in 2014 ), net amplification (in 2016 ), and Si Brillouin laser (in 2017 ). Still, fully exploiting the potential of these Brillouin optomechanical interactions in silicon requires of novel design strategies and, and advanced design tools allowing their optimization.
The goal of this internship is to develop artificial intelligence models to harness the unique degrees of freedom of subwavelength Si nanostructures to independently tailor photonic and phononic modes, providing simultaneous tight confinement and strong overlap, thus maximizing the efficiency of the SBS effect.
poursuite en thèse envisageable
(en anglais) Deep learning models for miniaturized silicon photonics sensors
A partir de janvier 2021
Lab-on-chip sensors are miniaturized circuits that integrate all key functionalities within a single chip, including target preparation, detection and data analysis. Ideally, these devices should be compact, robust and low cost, allowing large-volume production. This would allow the widespread deployment of a wide range of high-impact applications such as invasive medical diagnostics, food quality control and air pollution monitoring. In addition, lab-on-chip sensors should comprise sophisticated and intelligence enough (data monitoring, processing and analysis) to be used by non-skilled personal.
Deep learning methods rely on a training process and different abstraction levels to yield models able to automatically discover the representations needed for detection or classification. Deep learning models can perform precise detection regardless the changes in operation parameters within a training range, thus obviating the need for tight calibration processes. The different abstraction levels are learned from data training, and not from engineering design. This provides deep learning methods with a unique flexibility and outstanding processing capabilities that are already being exploited in image or speech recognition or predicting DNA mutations. We experimentally demonstrate for the first time that the use of machine learning substantially improves the tolerances of on-chip SHFT spectrometers against temperature variations, thus opening a new route for their use in realistic applications outside the controlled laboratory environment.
The goal of this internship will be to explore the use of deep learning algorithms to improve the performance, robustness and flexibility of miniaturized silicon photonics sensors. Simplified models of Si sensors and deep learning algorithms will be combined to alleviate performance degradations due to non-idealities and to develop advanced detection and processing functionalities.