Caractérisation des ions d'oxygène dans les mémoires résistives soumises à polarisation électrique par techniques de TEM avancées (original) (raw)

2020, Université Grenoble Alpes [2020-....]

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References (248)

  1. A.2. Principe .......................................................................................................................................................................
  2. II.A.3. Les modes d'observation possibles ........................................................................................................................ II.A.3.a. Mode TEM : imagerie conventionnelle ......................................................................................................... II.A.3.a.i. Mode image .................................................................................................................................................. II.A.3.a.ii. Mode diffraction par sélection d'aire ...................................................................................................... II.A.3.a.iii. Champ clair et champ sombre ................................................................................................................
  3. II.A.3.b. Mode STEM ....................................................................................................................................................... II.A.3.b.i. Champ clair et champ sombre..................................................................................................................
  4. II.A.4. Instruments et conditions d'analyses utilisées ......................................................................................................
  5. II.A.5. Contraste et résolution spatiale ...............................................................................................................................
  6. II.A.6. Interactions entre le faisceau et l'échantillon ........................................................................................................ II.A.6.a. Ionisation des niveaux électroniques par le faisceau incident ....................................................................
  7. II.A.6.b. Désexcitation des atomes .................................................................................................................................
  8. II.A.7. Spectroscopie EDX .................................................................................................................................................. II.A.7.a. Instrument de mesure : les détecteurs ............................................................................................................ II.A.7.b. Le spectre EDX .................................................................................................................................................
  9. II.A.8. Spectroscopie EELS ................................................................................................................................................. II.A.8.a. Instrument de mesure : Spectromètre ............................................................................................................ II.A.8.b. Le spectre EELS ................................................................................................................................................ II.A.8.b.i. Les pertes faibles d'énergie ....................................................................................................................... II.A.8.b.ii. Pertes d'énergie de coeur ........................................................................................................................... II.A.8.b.iii. Structure Fine ............................................................................................................................................ II.A.8.b.iv. Format des données récoltées ................................................................................................................
  10. II.A.8.c. Protocole expérimental .....................................................................................................................................
  11. II.B. Traitements et analyse de spectres images ............................................................... II.B.1. Analyse conventionnelle GMS3 ..............................................................................................................................
  12. II.B.1.a. Principe ................................................................................................................................................................
  13. II.B.2. Estimation de l'épaisseur via le libre parcours moyen ........................................................................................
  14. II.B.3. Analyse manuelle structure fine .............................................................................................................................. II.C. Préparation d'échantillon par Sonde Ionique Focalisée .............................................
  15. II.C.1. Principe de fonctionnement .................................................................................................................................... II.C.1.a. Généralités ........................................................................................................................................................... II.C.1.b. Propriétés du faisceau d'ions ........................................................................................................................... II.C.1.c. Imagerie Electronique ....................................................................................................................................... II.C.1.d. Dépôts de matière ............................................................................................................................................. II.C.1.d.i. Dépôts sous faisceau d'ions ...................................................................................................................... II.C.1.d.ii. Dépôts sous faisceau d'électrons ............................................................................................................ II.C.1.d.iii. Nature des dépôts utilisés........................................................................................................................
  16. II.C.2. Préparation d'une lame TEM conventionnelle ....................................................................................................
  17. II.D. Conclusion ................................................................................................................ III. Développement des techniques expérimentales pour le TEM en operando électrique ................................................................................................................... III.A. Porte-objet dédiés à la commutation électrique en operando dans le TEM ...............
  18. III.A.1. Porte-objet à pointe dédié à la commutation électrique en operando ...............................................................
  19. III.A.1.a. Présentation du porte-objet de Nanofactory .............................................................................................. III.A.1.b. Présentation du porte-objet de Hummingbird ...........................................................................................
  20. III.A.1.c. Difficultés induites et artefacts ...................................................................................................................... III.A.1.c.i. Mouvements grossiers de la pointe ........................................................................................................ III.A.1.c.ii. Contraintes mécaniques et thermiques ................................................................................................. III.A.1.c.iii. Effets de pointe .......................................................................................................................................
  21. III.A.1.d. Adaptation des techniques de préparation d'échantillon à l'utilisation de porte-objet à pointe pour la caractérisation operando .................................................................................................................................................. III.A.1.d.i. Préparation d'échantillon avec électrode localisée .............................................................................. III.A.1.d.ii. Préparation d'échantillon avec électrode localisée et contact conducteur déporté ......................
  22. III.A.1.d.iii. Préparation d'échantillon pleine plaque ..............................................................................................
  23. III.A.1.e. Protocole d'approche de la pointe utilisé lors de commutations en operando ........................................
  24. III.A.2. Porte-objet à puce dédié à la commutation électrique en operando .................................................................. III.A.2.a. Présentation....................................................................................................................................................... III.A.2.b. Adaptation des techniques de préparation d'échantillon à l'utilisation de porte-objet à puce pour la caractérisation operando ...................................................................................................................................................... III.A.2.b.i. Préparation en Z ....................................................................................................................................... III.A.2.b.ii. Autre piste de préparation d'échantillon avec époxy conductrice................................................... III.A.2.b.iii. Technique de préparation supplémentaire .......................................................................................
  25. III.A.2.c. Difficultés induites et artefacts ....................................................................................................................
  26. III.A.3. Bilan ......................................................................................................................................................................... III.B. Algorithme de démélange hyperspectral basé sur VCA .......................................... III.B.1. Traitements conventionnels des données EELS et problématiques associées ...........................................
  27. III.B.1.a. Dépendance à l'utilisateur ............................................................................................................................. III.B.1.b. Manque de reproductibilité et de fiabilité des cartographies ..................................................................
  28. III.B.1.c. Absence d'information sur l'environnement direct des ions analysés ..................................................
  29. III.B.2. Algorithmes de démélange hyperspectral ..........................................................................................................
  30. III.B.3. Étapes dans le fonctionnement de l'algorithme développé et utilisé ............................................................ III.B.3.a. Réécriture des données dans un espace minimisant la variance.............................................................
  31. III.B.3.b. Optimisation des références et démélanges ..............................................................................................
  32. III.B.3.c. Calcul des cartographies d'abondances des composantes spectrales .................................................... operando ............................................................................................................................................................... III.B.3.d.i. Préparation des datacubes en amont du démélange par VCA ........................................................
  33. III.B.3.d.ii. Adaptation du traitement de données aux datacubes acquis en operando ......................................
  34. III.B.
  35. Bilan ..........................................................................................................................................................................
  36. III.C. Conclusion .............................................................................................................
  37. IV.A. Introduction ............................................................................................................ IV.B. Présentation de l'empilement mémoire à base de SrTiO3 : en couche épitaxiale ...
  38. IV.B.1. Croissance du matériau mémoire ........................................................................................................................ IV.B.2. Intérêts portés à cet empilement mémoire ........................................................................................................ IV.B.3. Application de la méthode de démélange sur des données déjà publiées ....................................................
  39. IV.B.3.a. Étude du seuil L du titane par algorithme de démélange ........................................................................
  40. IV.B.3.b. Étude du seuil K de l'oxygène par algorithme de démélange ................................................................
  41. IV.B.3.c. Confrontation de nos résultats avec le modèle précédemment proposé ..............................................
  42. IV.C. Caractérisation STEM EELS en operando des ions oxygène dans un échantillon mémoire polycristallin ..................................................................................................... IV.C.1. Croissance de cet échantillon « granulaire »....................................................................................................... IV.C.2. Préparation d'échantillon ...................................................................................................................................... IV.C.3. Caractérisations structures et électriques ........................................................................................................... IV.C.4. Traitement conventionnel des images hyperspectrales EELS ....................................................................... IV.C.5. Traitements par algorithme de démélange hyperspectral ................................................................................
  43. IV.D. Résultats ................................................................................................................ IV.D.1. Vérification avec la littérature .............................................................................................................................. IV.D.2. Démélange à quatre composantes ...................................................................................................................... IV.D.3. Vérification avec cinq composantes ................................................................................................................... IV.D.4. Proposition d'un modèle ...................................................................................................................................... IV.D.5. Perspective d'étude via l'algorithme de démélange développé ......................................................................
  44. IV.E. Conclusion .............................................................................................................
  45. V. Étude d'un empilement mémoire de La2NiO4 .....................................................
  46. V.A. Introduction .............................................................................................................
  47. V.B. Présentation de l'empilement mémoire à base de La2NiO4 ..................................... utilisant les ions secondaires, l'imagerie électronique est habituellement préférée, étant donné que cette méthode est non-abrasive, et que la qualité d'image est bien meilleure [116,117].
  48. II
  49. D. Cooper, C. Baeumer, N. Bernier, A. Marchewka, C. La Torre, R.E. Dunin-Borkowski, S. Menzel, R. Wa- ser, R. Dittmann, Anomalous Resistance Hysteresis in Oxide ReRAM: Oxygen Evolution and Reincorporation Re- vealed by In Situ TEM, Adv. Mater. 29 (2017) 1700212. https://doi.org/10.1002/adma.201700212.
  50. M. Lamkimel, The Internet of Things, LTE Magazine. (2016). https://ltemagazine.com/the-internet-of- things.
  51. B. Heslop, By 2030, Each Person Will Own 15 Connected Devices, MarTechAdvisor. (2019). https://www.martechadvisor.com/articles/iot/by-2030-each-person-will-own-15-connected-devices-heres-what- that-means-for-your-business-and-content/#.
  52. WeAreSocial, Hootsuite, Digital 2019, 2019.
  53. Digital data storage is undergoing mind-boggling growth, EeTimes. (2018). http://www.eetimes.com/au- thor.asp?section_id=36&doc_id=1330462.
  54. M. LaPedus, Week In Review: Manufacturing, Test, (2020). https://semiengineering.com/week-in-review- manufacturing-test-78/.
  55. Storage in big data market research report global forecast 2022, Market Research Future. (2020). https://www.marketresearchfuture.com/reports/storage-in-big-data-market-2651.
  56. Share of big data and business analytics revenues worldwide in 2019, by industry, Statista. (2019). https://www.statista.com/statistics/616225/worldwide-big-data-business-analytics-revenue/.
  57. 2015 International Technology Roadmap for Semiconductors (ITRS), Semiconductor Industry Association.
  58. Solid State Drive (SSD) Requirements and endurance test method, JEDEC. (2016). https://www.jedec.org/standards-documents/docs/jesd218b01.
  59. ClarivateAnalytics, Results Analysis : Publications per Years, WebOfKnowledge. (2019). http://wcs.webof- knowledge.com/RA/analyze.do?product=WOS&SID=F3wvW2f9kzQMmyWF7Mp&field=PY_Publica- tionYear_PublicationYear_en&yearSort=true.
  60. Y. de Charentenay, Emerging Non-Volatile Memory (NVM) Technologies & Markets by YOLE Develope- ment, in: 2017.
  61. B. de Salvo, Silicon Non-Volatile Memories: Paths of Innovation, John Wiley & Sons, 2013.
  62. Memristive Phenomena -From Fundamental Physics to Neuromorphic Computing (lectures notes), Rainer
  63. Waser&Matthias Wuttig, Forschungszentrum Jülich GmbH, 2016.
  64. M.M. McGibbon, N.D. Browning, M.F. Chisholm, A.J. McGibbon, S.J. Pennycook, V. Ravikumar, V.P. Dravid, Direct Determination of Grain Boundary Atomic Structure in SrTiO3, Science. 266 (1994) 102-104. https://doi.org/10.1126/science.266.5182.102.
  65. T.W. Hickmott, Low-Frequency Negative Resistance in Thin Anodic Oxide Films, Journal of Applied Phy- sics. 33 (1962) 2669-2682. https://doi.org/10.1063/1.1702530.
  66. J.H. Simmons, P.B. Macedo, Analysis of Viscous Relaxation in Critical Oxide Mixtures, J. Chem. Phys. 54 (1971) 1325-1331. https://doi.org/10.1063/1.1674972.
  67. ReRAM Advantages, Crossbar. (2018). https://www.crossbar-inc.com/technology/reram-advantages/.
  68. H.-S.P. Wong, H.-Y. Lee, S. Yu, Y.-S. Chen, Y. Wu, P.-S. Chen, B. Lee, F.T. Chen, M.-J. Tsai, Metal-Oxide RRAM, Proceedings of the IEEE. 100 (2012) 1951-1970. https://doi.org/10.1109/JPROC.2012.2190369.
  69. F. Pan, S. Gao, C. Chen, C. Song, F. Zeng, Recent progress in resistive random access memories: Materials, switching mechanisms, and performance, Materials Science and Engineering: R: Reports. 83 (2014) 1-59. https://doi.org/10.1016/j.mser.2014.06.002.
  70. M. Prezioso, F. Merrikh-Bayat, B.D. Hoskins, G.C. Adam, K.K. Likharev, D.B. Strukov, Training and ope- ration of an integrated neuromorphic network based on metal-oxide memristors, Nature. 521 (2015) 61-64. https://doi.org/10.1038/nature14441.
  71. S. Yu, Y. Wu, R. Jeyasingh, D. Kuzum, H.-S.P. Wong, An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation, IEEE Trans. Electron Devices. 58 (2011) 2729-2737. https://doi.org/10.1109/TED.2011.2147791.
  72. B. Ku, B. Koo, A.S. Sokolov, M.J. Ko, C. Choi, Two-terminal artificial synapse with hybrid organic-inorga- nic perovskite (CH3NH3)PbI3 and low operating power energy (∼47 fJ/μm2), Journal of Alloys and Compounds. 833 (2020) 155064. https://doi.org/10.1016/j.jallcom.2020.155064.
  73. Z. Wang, S. Joshi, S.E. Savel'ev, H. Jiang, R. Midya, P. Lin, M. Hu, N. Ge, J.P. Strachan, Z. Li, Q. Wu, M.
  74. Barnell, G.-L. Li, H.L. Xin, R.S. Williams, Q. Xia, J.J. Yang, Memristors with diffusive dynamics as synaptic emula- tors for neuromorphic computing, Nature Mater. 16 (2017) 101-108. https://doi.org/10.1038/nmat4756.
  75. J.J. Yang, D.B. Strukov, D.R. Stewart, Memristive devices for computing, Nature Nanotech. 8 (2013) 13- 24. https://doi.org/10.1038/nnano.2012.240.
  76. T. Ohno, T. Hasegawa, T. Tsuruoka, K. Terabe, J.K. Gimzewski, M. Aono, Short-term plasticity and long- term potentiation mimicked in single inorganic synapses, Nature Mater. 10 (2011) 591-595. https://doi.org/10.1038/nmat3054.
  77. K. Maas, La2NiO4+δ, un Conducteur Mixte Ionique-Electronique pour les Mémoires à Changement de Valence, Université Grenoble Alpes, 2019.
  78. C. Moreno, C. Munuera, S. Valencia, F. Kronast, X. Obradors, C. Ocal, Reversible Resistive Switching and Multilevel Recording in La0.7Sr0.3MnO3 Thin Films for Low Cost Nonvolatile Memories, Nano Lett. 10 (2010) 3828-3835. https://doi.org/10.1021/nl1008162.
  79. G.W. Burr, R.M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M. Ishii, P. Narayanan, A. Fu- marola, L.L. Sanches, I. Boybat, M. Le Gallo, K. Moon, J. Woo, H. Hwang, Y. Leblebici, Neuromorphic computing using non-volatile memory, Advances in Physics: X. 2 (2017) 89-124. https://doi.org/10.1080/23746149.2016.1259585.
  80. R. Waser, R. Dittmann, G. Staikov, K. Szot, Redox-Based Resistive Switching Memories -Nanoionic Me- chanisms, Prospects, and Challenges, Adv. Mater. 21 (2009) 2632-2663. https://doi.org/10.1002/adma.200900375.
  81. J.E. Brewer, M. Gill, Nonvolatile Memory Technologies With Emphasis on Flash, IEEE Press Series on MicroElectronic Systems, IEEE Press Series, n.d.
  82. Z. Wei, Y. Kanzawa, K. Arita, Y. Katoh, K. Kawai, S. Muraoka, S. Mitani, S. Fujii, K. Katayama, M. Iijima, T. Mikawa, T. Ninomiya, R. Miyanaga, Y. Kawashima, K. Tsuji, A. Himeno, T. Okada, R. Azuma, K. Shimakawa, H. Sugaya, T. Takagi, R. Yasuhara, K. Horiba, H. Kumigashira, M. Oshima, Highly reliable TaOx ReRAM and direct evidence of redox reaction mechanism, in: 2008 IEEE International Electron Devices Meeting, IEEE, San Fran- cisco, CA, USA, 2008: pp. 1-4. https://doi.org/10.1109/IEDM.2008.4796676.
  83. A. Sawa, Resistive switching in transition metal oxides, Materials Today. 11 (2008) 28-36. https://doi.org/10.1016/S1369-7021(08)70119-6.
  84. S. Tappertzhofen, G. Di Martino, S. Hofmann, Nanoparticle Dynamics in Oxide-Based Memristive De- vices, Phys. Status Solidi A. 217 (2020) 1900587. https://doi.org/10.1002/pssa.201900587.
  85. R.A. De Souza, M.S. Islam, E. Ivers-Tiffée, Formation and migration of cation defects in the perovskite oxide LaMnO3, J. Mater. Chem. 9 (1999) 1621-1627. https://doi.org/10.1039/a901512d.
  86. Y. Yang, P. Gao, S. Gaba, T. Chang, X. Pan, W. Lu, Observation of conducting filament growth in nanos- cale resistive memories, Nat Commun. 3 (2012) 732. https://doi.org/10.1038/ncomms1737.
  87. P. Calka, Nanocaractérisation d'oxydes à changement de résistance pour les mémoires résistives, Université Grenoble Alpes, 2012.
  88. L.W. Song, S.S. Niu, Y.C. Sun, L.F. Hua, X. Zhao, W. Chen, Crystallinity dependence of resistive switching in Ti/Pr(Sr0.1Ca0.9)2Mn2O7Pt: Filamentary versus interfacial mechanisms, Appl. Phys. Lett. 104 (2014) 093502. https://doi.org/10.1063/1.4867483.
  89. S. Stille, C. Baeumer, S. Krannich, C. Lenser, R. Dittmann, J. Perlich, S.V. Roth, R. Waser, U. Klemradt, Feasibility studies for filament detection in resistively switching SrTiO3 devices by employing grazing incidence small angle X-ray scattering, Journal of Applied Physics. 113 (2013) 064509. https://doi.org/10.1063/1.4792035.
  90. W. Lee, J. Park, S. Kim, J. Woo, J. Shin, G. Choi, S. Park, D. Lee, E. Cha, B.H. Lee, H. Hwang, High Cur- rent Density and Nonlinearity Combination of Selection Device Based on TaOx/TiO2/TaOx Structure for One Se- lector-One Resistor Arrays, ACS Nano. 6 (2012) 8166-8172. https://doi.org/10.1021/nn3028776.
  91. Yu.F. Zhukovskii, E.A. Kotomin, S. Piskunov, D.E. Ellis, A comparative ab initio study of bulk and surface oxygen vacancies in PbTiO3, PbZrO3 and SrTiO3 perovskites, Solid State Communications. 149 (2009) 1359-1362. https://doi.org/10.1016/j.ssc.2009.05.023.
  92. A.R. Genreith-Schriever, P. Hebbeker, J. Hinterberg, T. Zacherle, R.A. De Souza, Understanding Oxygen- Vacancy Migration in the Fluorite Oxide CeO2 : An Ab Initio Study of Impurity-Anion Migration, J. Phys. Chem. C. 119 (2015) 28269-28275. https://doi.org/10.1021/acs.jpcc.5b07813.
  93. A. Schönhals, C.M.M. Rosário, S. Hoffmann-Eifert, R. Waser, S. Menzel, D.J. Wouters, Role of the Elec- trode Material on the RESET Limitation in Oxide ReRAM Devices, Adv. Electron. Mater. 4 (2018) 1700243. https://doi.org/10.1002/aelm.201700243.
  94. S. Menzel, P. Kaupmann, R. Waser, Understanding filamentary growth in electrochemical metallization me- mory cells using kinetic Monte Carlo simulations, Nanoscale. 7 (2015) 12673-12681. https://doi.org/10.1039/C5NR02258D.
  95. S. Menzel, M. Waters, A. Marchewka, U. Böttger, R. Dittmann, R. Waser, Origin of the Ultra-nonlinear Switching Kinetics in Oxide-Based Resistive Switches, Adv. Funct. Mater. 21 (2011) 4487-4492. https://doi.org/10.1002/adfm.201101117.
  96. G.-S. Park, Y.B. Kim, S.Y. Park, X.S. Li, S. Heo, M.-J. Lee, M. Chang, J.H. Kwon, M. Kim, U.-I. Chung, R.
  97. Dittmann, R. Waser, K. Kim, In situ observation of filamentary conducting channels in an asymmetric Ta2O5-x/TaO2-x bilayer structure, Nat Commun. 4 (2013) 2382. https://doi.org/10.1038/ncomms3382.
  98. B. Arndt, F. Borgatti, F. Offi, M. Phillips, P. Parreira, T. Meiners, S. Menzel, K. Skaja, G. Panaccione, D.A.
  99. MacLaren, R. Waser, R. Dittmann, Spectroscopic Indications of Tunnel Barrier Charging as the Switching Me- chanism in Memristive Devices, Adv. Funct. Mater. 27 (2017) 1702282. https://doi.org/10.1002/adfm.201702282.
  100. T. Ahmed, S. Walia, E.L.H. Mayes, R. Ramanathan, P. Guagliardo, V. Bansal, M. Bhaskaran, J.J. Yang, S. Sriram, Inducing tunable switching behavior in a single memristor, Applied Materials Today. 11 (2018) 280-290. https://doi.org/10.1016/j.apmt.2018.03.003.
  101. T. Ahmed, S. Walia, E.L.H. Mayes, R. Ramanathan, P. Guagliardo, V. Bansal, M. Bhaskaran, J.J. Yang, S. Sriram, Data related to the nanoscale structural and compositional evolution in resistance change memories, Data in Brief. 21 (2018) 18-24. https://doi.org/10.1016/j.dib.2018.09.087.
  102. H. Du, C.-L. Jia, A. Koehl, J. Barthel, R. Dittmann, R. Waser, J. Mayer, Nanosized Conducting Filaments Formed by Atomic-Scale Defects in Redox-Based Resistive Switching Memories, Chem. Mater. 29 (2017) 3164- 3173. https://doi.org/10.1021/acs.chemmater.7b00220.
  103. P. Gao, Z. Wang, W. Fu, Z. Liao, K. Liu, W. Wang, X. Bai, E. Wang, In situ TEM studies of oxygen va- cancy migration for electrically induced resistance change effect in cerium oxides, Micron. 41 (2010) 301-305. https://doi.org/10.1016/j.micron.2009.11.010.
  104. Q. Zhang, X. He, J. Shi, N. Lu, H. Li, Q. Yu, Z. Zhang, L.-Q. Chen, B. Morris, Q. Xu, P. Yu, L. Gu, K. Jin, C.-W. Nan, Atomic-resolution imaging of electrically induced oxygen vacancy migration and phase transformation in SrCoO2.5-σ, Nat Commun. 8 (2017) 104. https://doi.org/10.1038/s41467-017-00121-6.
  105. T. Kramer, D. Mierwaldt, M. Scherff, M. Kanbach, C. Jooss, Developing an in situ environmental TEM set up for investigations of resistive switching mechanisms in Pt-Pr1-xCaxMnO3-δ-Pt sandwich structures, Ultramicros- copy. (2017). https://doi.org/10.1016/j.ultramic.2017.08.012.
  106. N. Dobigeon, N. Brun, Spectral mixture analysis of EELS spectrum-images, Ultramicroscopy. 120 (2012) 25-34. https://doi.org/10.1016/j.ultramic.2012.05.006.
  107. M. Shiga, K. Tatsumi, S. Muto, K. Tsuda, Y. Yamamoto, T. Mori, T. Tanji, Sparse modeling of EELS and EDX spectral imaging data by nonnegative matrix factorization, Ultramicroscopy. 170 (2016) 43-59. https://doi.org/10.1016/j.ultramic.2016.08.006.
  108. M. Duchamp, M. Lachmann, C.B. Boothroyd, A. Kovács, F.-J. Haug, C. Ballif, R.E. Dunin-Borkowski, Compositional study of defects in microcrystalline silicon solar cells using spectral decomposition in the scanning transmission electron microscope, Appl. Phys. Lett. 102 (2013) 133902. https://doi.org/10.1063/1.4800569.
  109. N. Bonnet, N. Brun, C. Colliex, Extracting information from sequences of spatially resolved EELS spectra using multivariate statistical analysis, Ultramicroscopy. 77 (1999) 97-112. https://doi.org/10.1016/S0304- 3991(99)00042-X.
  110. G.H. Golub, C. Reinsch, Singular value decomposition and least squares solutions, Numer. Math. 14 (1970) 403-420.
  111. V. Perrier, R. Mohr, La Décomposition en Valeurs Singulières, (2011).
  112. M. Türler, Principal component analysis: comme outils d'analyse de la variabilité spectrale, in: Wiley Inter- disciplinary Reviews: Computational Statistics, 2006: pp. 433-459.
  113. M. Richardson, Principal Component Analysis, in: 2009: p. 23.
  114. A. Martin, L'analyse de données, in: ENSIETA, 2004: p. 119.
  115. Q. Guo, C. Zhang, Y. Zhang, H. Liu, An Efficient SVD-Based Method for Image Denoising, IEEE Trans.
  116. Circuits Syst. Video Technol. 26 (2016) 868-880. https://doi.org/10.1109/TCSVT.2015.2416631.
  117. J.E. Ball, S. Kari, N.H. Younan, Hyperspectral pixel unmixing using singular value decomposition, in: IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, IEEE, Anchorage, AK, USA, 2004: pp. 3253-3256. https://doi.org/10.1109/IGARSS.2004.1370395.
  118. J.M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, J. Chanussot, Hyperspectral Un- mixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing. 5 (2012) 354-379. https://doi.org/10.1109/JSTARS.2012.2194696.
  119. N. Dobigeon, Y. Altmann, N. Brun, S. Moussaoui, Linear and Nonlinear Unmixing in Hyperspectral Ima- ging, in: Data Handling in Science and Technology, Elsevier, 2016: pp. 185-224. https://doi.org/10.1016/B978-0- 444-63638-6.00006-1.
  120. M.-D. Iordache, J.M. Bioucas-Dias, A. Plaza, Sparse Unmixing of Hyperspectral Data, IEEE Trans. Geosci. Remote Sensing. 49 (2011) 2014-2039. https://doi.org/10.1109/TGRS.2010.2098413.
  121. P.M. Voyles, Informatics and data science in materials microscopy, Current Opinion in Solid State and Ma- terials Science. 21 (2017) 141-158. https://doi.org/10.1016/j.cossms.2016.10.001.
  122. N. Dobigeon, J.-Y. Tourneret, C.-I. Chang, Semi-Supervised Linear Spectral Unmixing Using a Hierarchical Bayesian Model for Hyperspectral Imagery, IEEE Trans. Signal Process. 56 (2008) 2684-2695. https://doi.org/10.1109/TSP.2008.917851.
  123. Y.M. Masalmah, Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization, University Of Puerto Rico Mayaguez Campus, 2007.
  124. A. Lagrange, M. Fauvel, S. May, J. Bioucas-Dias, N. Dobigeon, Matrix cofactorization for joint representa- tion learning and supervised classification -Application to hyperspectral image analysis, Neurocomputing. 385 (2020) 132-147. https://doi.org/10.1016/j.neucom.2019.12.068.
  125. M. Karoui, F. Benhalouche, Y. Deville, K. Djerriri, X. Briottet, T. Houet, A. Le Bris, C. Weber, Partial Li- near NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hypers- pectral Remote Sensing Data, Remote Sensing. 11 (2019) 2164. https://doi.org/10.3390/rs11182164.
  126. N. Keshava, J.F. Mustard, Spectral unmixing, IEEE Signal Process. Mag. 19 (2002) 44-57. https://doi.org/10.1109/79.974727.
  127. O. Besson, N. Dobigeon, J.-Y. Tourneret, Bayesian estimation of a subspace, in: 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, Pacific Grove, CA, USA, 2011: pp. 629-633. https://doi.org/10.1109/ACSSC.2011.6190078.
  128. J.M. Bioucas-Dias, M.A.T. Figueiredo, Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing, in: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evo- lution in Remote Sensing, IEEE, Reykjavik, Iceland, 2010: pp. 1-4. https://doi.org/10.1109/WHIS- PERS.2010.5594963.
  129. S. Jia, Y. Qian, Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing, IEEE Trans.
  130. Geosci. Remote Sensing. 47 (2009) 161-173. https://doi.org/10.1109/TGRS.2008.2002882.
  131. Y. Qian, S. Jia, J. Zhou, A. Robles-Kelly, Hyperspectral Unmixing via L1/2L_{1/2}L1/2 Sparsity-Constrained Nonnegative Matrix Factorization, IEEE Trans. Geosci. Remote Sensing. 49 (2011) 4282-4297. https://doi.org/10.1109/TGRS.2011.2144605.
  132. S.A. Robila, L.G. Maciak, A parallel unmixing algorithm for hyperspectral images, in: D.P. Casasent, E.L.
  133. Hall, J. Röning (Eds.), Boston, MA, 2006: p. 63840F. https://doi.org/10.1117/12.685655.
  134. Z.-L. Sun, L. Shang, An improved constrained ICA with reference based unmixing matrix initialization, Neurocomputing. 73 (2010) 1013-1017. https://doi.org/10.1016/j.neucom.2009.12.016.
  135. B.A. Pearlmutter, A Context-Sensitive Generalization of ICA, (n.d.) 7.
  136. V. Vigneron, V. Zarzoso, E. Moreau, R. Gribonval, E. Vincent, Latent Variable Analysis and Signal Separa- tion, in: 2010: p. 672.
  137. J.M.P. Nascimento, J.M.B. Dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sensing. 43 (2005) 898-910. https://doi.org/10.1109/TGRS.2005.844293.
  138. F. Kowkabi, A. Keshavarz, Using spectral Geodesic and spatial Euclidean weights of neighbourhood pixels for hyperspectral Endmember Extraction preprocessing, ISPRS Journal of Photogrammetry and Remote Sensing. 158 (2019) 201-218. https://doi.org/10.1016/j.isprsjprs.2019.10.005.
  139. J.M.P. Nascimento, J.M. Bioucas-Dias, Hyperspectral Unmixing Based on Mixtures of Dirichlet Compo- nents, IEEE Trans. Geosci. Remote Sensing. 50 (2012) 863-878. https://doi.org/10.1109/TGRS.2011.2163941.
  140. R. Feng, L. Wang, Y. Zhong, Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery, Remote Sensing. 10 (2018) 1546. https://doi.org/10.3390/rs10101546.
  141. J. Krmar, M. Vukićević, A. Kovačević, A. Protić, M. Zečević, B. Otašević, Performance comparison of non- linear and linear regression algorithms coupled with different attribute selection methods for quantitative structure - retention relationships modelling in micellar liquid chromatography, Journal of Chromatography A. 1623 (2020) 461146. https://doi.org/10.1016/j.chroma.2020.461146.
  142. B. Prats Mateu, M.T. Hauser, A. Heredia, N. Gierlinger, Waterproofing in Arabidopsis: Following Pheno- lics and Lipids In situ by Confocal Raman Microscopy, Front. Chem. 4 (2016). https://doi.org/10.3389/fchem.2016.00010.
  143. J. Spiegelberg, J. Rusz, T. Thersleff, K. Pelckmans, Analysis of electron energy loss spectroscopy data using geometric extraction methods, Ultramicroscopy. 174 (2017) 14-26. https://doi.org/10.1016/j.ultramic.2016.12.014.
  144. E. Monier, T. Oberlin, N. Brun, X. Li, M. Tencé, N. Dobigeon, Fast reconstruction of atomic-scale STEM- EELS images from sparse sampling, ArXiv:2002.01225 [Cond-Mat]. (2020). http://arxiv.org/abs/2002.01225 (acces- sed July 1, 2020).
  145. E. Monier, T. Oberlin, N. Brun, X. Li, M. Tence, N. Dobigeon, Fast reconstruction of atomic-scale STEM- EELS images from sparse sampling -Supplementary materials, Ultramicroscopy. (n.d.) 13.
  146. E. Monier, T. Oberlin, N. Brun, M. Tence, M. de Frutos, N. Dobigeon, Reconstruction of Partially Sampled Multiband Images-Application to STEM-EELS Imaging, IEEE Trans. Comput. Imaging. 4 (2018) 585-598. https://doi.org/10.1109/TCI.2018.2866961.
  147. D.B. Williams, C.B. Carter, Transmission electron microscopy: a textbook for materials science, 2nd ed, Springer, New York, 2008.
  148. J.M. Zuo, J.C.H. Spence, Advanced Transmission Electron Microscopy, Springer New York, New York, NY, 2017. https://doi.org/10.1007/978-1-4939-6607-3.
  149. R.F. Egerton, Electron Energy-Loss Spectroscopy in the Electron Microscope, Springer US, Boston, MA, 2011. https://doi.org/10.1007/978-1-4419-9583-4.
  150. L. Reimer, H. Kohl, Transmission electron microscopy: physics of image formation, 5. ed, Springer, New
  151. York, NY, 2008. [96] M. Schaffer, B. Schaffer, Q. Ramasse, Sample preparation for atomic-resolution STEM at low voltages by FIB, Ultramicroscopy. 114 (2012) 62-71. https://doi.org/10.1016/j.ultramic.2012.01.005.
  152. F. Yi, P.M. Voyles, Analytical and computational modeling of fluctuation electron microscopy from a nano- crystal/amorphous composite, Ultramicroscopy. 122 (2012) 37-47. https://doi.org/10.1016/j.ultramic.2012.07.022.
  153. M. KARLÍK, B. JOUFFREY, Étude des métaux par microscopie électronique en transmission (MET) - Formation des images, Techniques de l'Ingénieur. (2008). https://www.techniques-ingenieur.fr/base-documen- taire/materiaux-th11/essais-metallographiques-des-metaux-et-alliages-42343210/etude-des-metaux-par-microscopie- electronique-en-transmission-met-m4135/ (accessed July 2, 2020).
  154. J. Verbeeck, P. Schattschneider, A. Rosenauer, Image simulation of high resolution energy filtered TEM images, Ultramicroscopy. 109 (2009) 350-360. https://doi.org/10.1016/j.ultramic.2009.01.003.
  155. T. Ohsuna, Y. Sakamoto, O. Terasaki, K. Kuroda, TEM image simulation of mesoporous crystals for struc- ture type identification, Solid State Sciences. 13 (2011) 736-744. https://doi.org/10.1016/j.solidstates- ciences.2010.04.028.
  156. A. Maltsi, T. Niermann, T. Streckenbach, K. Tabelow, T. Koprucki, Numerical simulation of TEM images for In(Ga)As/GaAs quantum dots with various shapes, Opt Quant Electron. 52 (2020) 257. https://doi.org/10.1007/s11082-020-02356-y.
  157. D.E. Newbury, The New X-ray Mapping: X-ray Spectrum Imaging above 100 kHz Output Count Rate with the Silicon Drift Detector, Microsc. Microanal. 12 (2006) 26-35. https://doi.org/10.1017/S143192760606020X.
  158. P. Statham, Digital Pulse Processing and Pile Up Correction for Accurate Interpretation of High Rate SDD Spectrum Images, Microscopy and Microanalysis. 13 (2007) 1428-1429. https://doi.org/10.1017/S1431927607073242.
  159. H. von Harrach, P. Dona, B. Freitag, H. Soltau, A. Niculae, M. Rohde, An integrated Silicon Drift Detector System for FEI Schottky Field Emission Transmission Electron Microscopes, Microsc Microanal. 15 (2009) 208- 209. https://doi.org/10.1017/S1431927609094288.
  160. J.I. Goldstein, D.E. Newbury, P. Echlin, D.C. Joy, C.E. Lyman, E. Lifshin, L. Sawyer, J.R. Michael, Scan- ning Electron Microscopy and X-ray Microanalysis, Springer US, Boston, MA, 2003. https://doi.org/10.1007/978- 1-4615-0215-9.
  161. C. Grigis, S. Schamm, Element and phase identification via fine structure analysis in EELS: application to MOCVD-Y1Ba2Cu3O7-δ thin films, Ultramicroscopy. 74 (1998) 159-167. https://doi.org/10.1016/S0304- 3991(98)00040-0.
  162. EELS atlas, EELS.Info. (2020). https://eels.info/atlas.
  163. R.F. Egerton, Electron energy-loss spectroscopy in the TEM, Rep. Prog. Phys. 72 (2009) 016502. https://doi.org/10.1088/0034-4885/72/1/016502.
  164. W.J. MoberlyChan, D.P. Adams, M.J. Aziz, G. Hobler, T. Schenkel, Fundamentals of Focused Ion Beam Nanostructural Processing: Below, At, and Above the Surface, MRS Bulletin. 32 (2007) 424-432. https://doi.org/10.1557/mrs2007.66.
  165. G. Sir Taylor, Disintegration of water drops in an electric field, Royal Society. (1964) 383-397. https://doi.org/10.1098/rspa.1964.0151.
  166. L.A. Giannuzzi, N.C.S. University, Introduction to Focused Ion Beams: Instrumentation, Theory, Tech- niques and Practice, Springer Science & Business Media, 2006.
  167. A. Claverie, Transmission Electron Microscopy in Micro-nanoelectronics, John Wiley & Sons, 2013.
  168. A.J. Steckl, H.C. Mogul, S.M. Mogren, Ultrashallow Si p+-n junction fabrication by low energy Ga+ fo- cused ion beam implantation, Journal of Vacuum Science & Technology B: Microelectronics Processing and Pheno- mena. 8 (1990) 1937-1940. https://doi.org/10.1116/1.584878.
  169. S. Rajsiri, B.W. Kempshall, S.M. Schwarz, L.A. Giannuzzi, FIB Damage in Silicon: Amorphization or Rede- position?, Microsc Microanal. 8 (2002) 50-51. https://doi.org/10.1017/S1431927602101577.
  170. K.A. Unocic, M.J. Mills, G.S. Daehn, Effect of gallium focused ion beam milling on preparation of alumi- nium thin foils, Journal of Microscopy. 240 (2010) 227-238. https://doi.org/10.1111/j.1365-2818.2010.03401.x.
  171. L.A. Giannuzzi, N.C.S. University, Introduction to Focused Ion Beams: Instrumentation, Theory, Tech- niques and Practice, Springer Science & Business Media, 2004.
  172. R.G. Elliman, J.S. Williams, Advances in ion beam modification of semiconductors, Current Opinion in Solid State and Materials Science. 19 (2015) 49-67. https://doi.org/10.1016/j.cossms.2014.11.007.
  173. I. Utke, P. Hoffmann, J. Melngailis, Gas-assisted focused electron beam and ion beam processing and fabri- cation, J. Vac. Sci. Technol. B. 26 (2008) 1197. https://doi.org/10.1116/1.2955728.
  174. J. Goldstein, D.E. Newbury, D.C. Joy, C.E. Lyman, P. Echlin, E. Lifshin, L. Sawyer, J.R. Michael, Scanning Electron Microscopy and X-Ray Microanalysis: Third Edition, Springer Science & Business Media, 2012.
  175. M. Ishida, J. Fujita, T. Ichihashi, Y. Ochiai, T. Kaito, S. Matsui, Focused ion beam-induced fabrication of tungsten structures, Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures Pro- cessing, Measurement, and Phenomena. 21 (2003) 2728-2731. https://doi.org/10.1116/1.1627806.
  176. S. Matsui, T. Kaito, J. Fujita, M. Komuro, K. Kanda, Y. Haruyama, Three-dimensional nanostructure fabri- cation by focused-ion-beam chemical vapor deposition, Journal of Vacuum Science & Technology B: Microelectro- nics and Nanometer Structures Processing, Measurement, and Phenomena. 18 (2000) 3181. https://doi.org/10.1116/1.1319689.
  177. J.D. Jr, D.P. Nackashi, S.E. Mick, Electron microscope sample holder for forming a gas or liquid cell with two semiconductor devices, US8829469B2, 2014. https://patents.google.com/patent/US8829469B2/en (accessed July 3, 2020).
  178. L.F. Allard, W.C. Bigelow, M. Jose-Yacaman, D.P. Nackashi, J. Damiano, S.E. Mick, A new MEMS-based system for ultra-high-resolution imaging at elevated temperatures, Microscopy Research and Technique. 72 (2009) 208-215. https://doi.org/10.1002/jemt.20673.
  179. TT10 -Bruker AFM Probes, (n.d.). https://www.brukerafmprobes.com/p-3442-tt10.aspx (accessed July 3, 2020).
  180. R.J. Kamaladasa, A.A. Sharma, Y.-T. Lai, W. Chen, P.A. Salvador, J.A. Bain, M. Skowronski, Y.N. Picard, In Situ TEM Imaging of Defect Dynamics under Electrical Bias in Resistive Switching Rutile-TiO2, Microsc Microa- nal. 21 (2015) 140-153. https://doi.org/10.1017/S1431927614013555.
  181. R.J. Kamaladasa, A.A. Sharma, Y.-T. Lai, W. Chen, P.A. Salvador, J.A. Bain, M. Skowronski, Y.N. Picard, In Situ TEM Imaging of Defect Dynamics under Electrical Bias in Resistive Switching Rutile-TiO2, Microscopy and Microanalysis. 21 (2015) 140-153. https://doi.org/10.1017/S1431927614013555.
  182. J.S. Lee, S. Lee, T.W. Noh, Resistive switching phenomena: A review of statistical physics approaches, Appl. Phys. Rev. 2 (2015) 031303. https://doi.org/10.1063/1.4929512.
  183. J. Kwon, A.A. Sharma, J.A. Bain, Y.N. Picard, M. Skowronski, Oxygen Vacancy Creation, Drift, and Aggre- gation in TiO2 -Based Resistive Switches at Low Temperature and Voltage, Adv. Funct. Mater. 25 (2015) 2876- 2883. https://doi.org/10.1002/adfm.201500444.
  184. S. Ambrogio, S. Balatti, Z.Q. Wang, Y.-S. Chen, H.-Y. Lee, F.T. Chen, D. Ielmini, Data retention statistics and modelling in HfO2 resistive switching memories, in: 2015 IEEE International Reliability Physics Symposium, 2015: p. MY.7.1-MY.7.6. https://doi.org/10.1109/IRPS.2015.7112810.
  185. M. Wu, A. Tafel, P. Hommelhoff, E. Spiecker, Determination of 3D electrostatic field at an electron nano- emitter, Appl. Phys. Lett. 114 (2019) 013101. https://doi.org/10.1063/1.5055227.
  186. S. Rubanov, P.R. Munroe, FIB-induced damage in silicon, J Microsc. 214 (2004) 213-221. https://doi.org/10.1111/j.0022-2720.2004.01327.x.
  187. J.-Y. Chen, C.-L. Hsin, C.-W. Huang, C.-H. Chiu, Y.-T. Huang, S.-J. Lin, W.-W. Wu, L.-J. Chen, Dynamic Evolution of Conducting Nanofilament in Resistive Switching Memories, (2013). https://doi.org/10.1021/nl4015638.
  188. A.S.M. International, Istfa '98: Proceedings of the 24Th International Symposium for Testing and Failure Analysis, ASM International, 1998.
  189. Y.H. Lee, R. Biswas, C.M. Soukoulis, C.Z. Wang, C.T. Chan, K.M. Ho, Molecular-dynamics simulation of thermal conductivity in amorphous silicon, Phys. Rev. B. 43 (1991) 6573-6580. https://doi.org/10.1103/Phy- sRevB.43.6573.
  190. A. Zintler, U. Kunz, Y. Pivak, S.U. Sharath, S. Vogel, E. Hildebrandt, H.-J. Kleebe, L. Alff, L. Molina-Luna, FIB based fabrication of an operative Pt/HfO2/TiN device for resistive switching inside a transmission electron mi- croscope, Ultramicroscopy. 181 (2017) 144-149. https://doi.org/10.1016/j.ultramic.2017.04.008.
  191. P.A. Crozier, Quantitative elemental mapping of materials by energy-filtered imaging, Ultramicroscopy. 58 (1995) 157-174. https://doi.org/10.1016/0304-3991(94)00201-W.
  192. M. Dalla Mura, J. Chanussot, A. Plaza, An Overview on Hyperspectral Unmixing, in: gipsa-lab, 2014: p. 83.
  193. J.C. Harsanyi, C.-I. Chang, Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach, IEEE Transactions on Geoscience and Remote Sensing. 32 (1994) 779-785. https://doi.org/10.1109/36.298007.
  194. D.P. Bertsekas, S. Pallottino, M.G. Scutell�, Polynomial auction algorithms for shortest paths, Comput Optim Applic. 4 (1995) 99-125. https://doi.org/10.1007/BF01302891.
  195. D.P. Bertsekas, Nonlinear Programming, Journal of the Operational Research Society. 48 (1997) 334-334. https://doi.org/10.1057/palgrave.jors.2600425.
  196. M.A.T. Figueiredo, J.M. Bioucas-Dias, Restoration of Poissonian Images Using Alternating Direction Op- timization, IEEE Trans. on Image Process. 19 (2010) 3133-3145. https://doi.org/10.1109/TIP.2010.2053941.
  197. T. Heisig, C. Baeumer, U.N. Gries, M.P. Mueller, C. La Torre, M. Luebben, N. Raab, H. Du, S. Menzel, D.N. Mueller, C.-L. Jia, J. Mayer, R. Waser, I. Valov, R.A. De Souza, R. Dittmann, Oxygen Exchange Processes bet- ween Oxide Memristive Devices and Water Molecules, Adv. Mater. 30 (2018) 1800957. https://doi.org/10.1002/adma.201800957.
  198. V.E. Alexandrov, E.A. Kotomin, J. Maier, R.A. Evarestov, First-principles study of bulk and surface oxygen vacancies in SrTiO3 crystal, Eur. Phys. J. B. 72 (2009) 53-57. https://doi.org/10.1140/epjb/e2009-00339-4.
  199. T.J. Bailey, R. Jha, Understanding Synaptic Mechanisms in SrTiO3 RRAM Devices, IEEE Trans. Electron Devices. 65 (2018) 3514-3520. https://doi.org/10.1109/TED.2018.2847413.
  200. N. Bickel, G. Schmidt, K. Heinz, K. MOiler, Structure determination of the SrTiO3(100) surface, Vacuum. 41 (1990) 46-48.
  201. T. Wolfram, E.A. Kraut, F.J. Morin, d -Band Surface States on Transition-Metal Perovskite Crystals: I. Qualitative Features and Application to SrTiO3, Phys. Rev. B. 7 (1973) 1677-1694. https://doi.org/10.1103/Phy- sRevB.7.1677.
  202. S. Stille, Ch. Lenser, R. Dittmann, A. Koehl, I. Krug, R. Muenstermann, J. Perlich, C.M. Schneider, U. Kle- mradt, R. Waser, Detection of filament formation in forming-free resistive switching SrTiO3 devices with Ti top electrodes, Appl. Phys. Lett. 100 (2012) 223503. https://doi.org/10.1063/1.4724108.
  203. K. van Benthem, C. Elsässer, M. Rühle, Core-hole effects on the ELNES of absorption edges in SrTiO3, Ultramicroscopy. 96 (2003) 509-522. https://doi.org/10.1016/S0304-3991(03)00112-8.
  204. D.A. Muller, N. Nakagawa, A. Ohtomo, J.L. Grazul, H.Y. Hwang, Atomic-scale imaging of nanoengineered oxygen vacancy profiles in SrTiO3, Nature. 430 (2004) 657-661. https://doi.org/10.1038/nature02756.
  205. J.P. Buban, M. Chi, D.J. Masiel, J.P. Bradley, B. Jiang, H. Stahlberg, N.D. Browning, Structural variability of edge dislocations in a SrTiO3 low-angle [001] tilt grain boundary, J. Mater. Res. 24 (2009) 2191-2199. https://doi.org/10.1557/jmr.2009.0259.
  206. K. Fleck, U. Bottger, R. Waser, S. Menzel, Interrelation of Sweep and Pulse Analysis of the SET Process in SrTiO3 Resistive Switching Memories, IEEE Electron Device Lett. 35 (2014) 924-926. https://doi.org/10.1109/LED.2014.2340016.
  207. T. Mizoguchi, Y. Sato, J.P. Buban, K. Matsunaga, T. Yamamoto, Y. Ikuhara, Sr vacancy segregation by heat treatment at SrTiO3 grain boundary, Appl. Phys. Lett. 87 (2005) 241920. https://doi.org/10.1063/1.2146051.
  208. S.-D. Mo, W.Y. Ching, M.F. Chisholm, G. Duscher, Electronic structure of a grain-boundary model in
  209. SrTiO3, Phys. Rev. B. 60 (1999) 2416-2424. https://doi.org/10.1103/PhysRevB.60.2416.
  210. M. Abbate, F.M.F. de Groot, J.C. Fuggle, A. Fujimori, Y. Tokura, Y. Fujishima, O. Strebel, M. Domke, G.
  211. Kaindl, J. van Elp, B.T. Thole, G.A. Sawatzky, M. Sacchi, N. Tsuda, Soft-x-ray-absorption studies of the location of extra charges induced by substitution in controlled-valence materials, Phys. Rev. B. 44 (1991) 5419-5422. https://doi.org/10.1103/PhysRevB.44.5419.
  212. C. Baeumer, C. Schmitz, A. Marchewka, D.N. Mueller, R. Valenta, J. Hackl, N. Raab, S.P. Rogers, M.I.
  213. Khan, S. Nemsak, M. Shim, S. Menzel, C.M. Schneider, R. Waser, R. Dittmann, Quantifying redox-induced Schottky barrier variations in memristive devices via in operando spectromicroscopy with graphene electrodes, Nature Com- munications. 7 (2016) 12398. https://doi.org/10.1038/ncomms12398.
  214. R. Muenstermann, T. Menke, R. Dittmann, R. Waser, Coexistence of Filamentary and Homogeneous Resis- tive Switching in Fe-Doped SrTiO3 Thin-Film Memristive Devices, Advanced Materials. 22 (2010) 4819-4822. https://doi.org/10.1002/adma.201001872.
  215. H. Du, C.-L. Jia, L. Houben, V. Metlenko, R.A. De Souza, R. Waser, J. Mayer, Atomic structure and che- mistry of dislocation cores at low-angle tilt grain boundary in SrTiO3 bicrystals, Acta Materialia. 89 (2015) 344-351. https://doi.org/10.1016/j.actamat.2015.02.016.
  216. L.F. Kourkoutis, H.L. Xin, T. Higuchi, Y. Hotta, J.H. Lee, Y. Hikita, D.G. Schlom, H.Y. Hwang, D.A. Mul- ler, Atomic-resolution spectroscopic imaging of oxide interfaces, Philosophical Magazine. 90 (2010) 4731-4749. https://doi.org/10.1080/14786435.2010.518983.
  217. A.S. Sefat, G. Amow, M.-Y. Wu, G.A. Botton, J.E. Greedan, High-resolution EELS study of the vacancy- doped metal/insulator system, Nd1-xTiO3, to 0.33., Journal of Solid State Chemistry. 178 (2005) 1008-1016. https://doi.org/10.1016/j.jssc.2004.12.027.
  218. F.M.F. de Groot, J. Faber, J.J.M. Michiels, M.T. Czyżyk, M. Abbate, J.C. Fuggle, Oxygen 1s x-ray absorp- tion of tetravalent titanium oxides: A comparison with single-particle calculations, Phys. Rev. B. 48 (1993) 2074- 2080. https://doi.org/10.1103/PhysRevB.48.2074.
  219. V. Metlenko, A.H.H. Ramadan, F. Gunkel, H. Du, H. Schraknepper, S. Hoffmann-Eifert, R. Dittmann, R.
  220. Waser, R.A.D. Souza, Do dislocations act as atomic autobahns for oxygen in the perovskite oxide SrTiO3?, Nanos- cale. 6 (2014) 12864-12876. https://doi.org/10.1039/C4NR04083J.
  221. G. Bertoni, E. Beyers, J. Verbeeck, M. Mertens, P. Cool, E.F. Vansant, G. Van Tendeloo, Quantification of crystalline and amorphous content in porous TiO2 samples from electron energy loss spectroscopy, Ultramicros- copy. 106 (2006) 630-635. https://doi.org/10.1016/j.ultramic.2006.03.006.
  222. I. Tanaka, T. Nakajima, J. Kawai, H. Adachi, H. Gu, M. Ruhle, Dopant-modified local chemical bonding at a grain, Philosophical Magazine Letters. 75 (1997) 21-28. https://doi.org/10.1080/095008397179877.
  223. H. Seo, A.B. Posadas, C. Mitra, A.V. Kvit, J. Ramdani, A.A. Demkov, Band alignment and electronic struc- ture of the anatase TiO 2 /SrTiO 3 (001) heterostructure integrated on Si(001), Phys. Rev. B. 86 (2012) 075301. https://doi.org/10.1103/PhysRevB.86.075301.
  224. L.H. Karlsson, J. Birch, J. Halim, M.W. Barsoum, P.O.Å. Persson, Atomically Resolved Structural and Che- mical Investigation of Single MXene Sheets, Nano Lett. 15 (2015) 4955-4960. https://doi.org/10.1021/acs.nano- lett.5b00737.
  225. F. de la Peña, M.-H. Berger, J.-F. Hochepied, F. Dynys, O. Stephan, M. Walls, Mapping titanium and tin oxide phases using EELS: An application of independent component analysis, Ultramicroscopy. 111 (2011) 169-176. https://doi.org/10.1016/j.ultramic.2010.10.001.
  226. L. Fitting, S. Thiel, A. Schmehl, J. Mannhart, D.A. Muller, Subtleties in ADF imaging and spatially resolved EELS: A case study of low-angle twist boundaries in SrTiO3, Ultramicroscopy. 106 (2006) 1053-1061. https://doi.org/10.1016/j.ultramic.2006.04.019.
  227. T. Leichtweiss, R.A. Henning, J. Koettgen, R.M. Schmidt, B. Holländer, M. Martin, M. Wuttig, J. Janek, Amorphous and highly nonstoichiometric titania (TiOx) thin films close to metal-like conductivity, J. Mater. Chem. A. 2 (2014) 6631. https://doi.org/10.1039/c3ta14816e.
  228. W. Hu, L. Li, W. Tong, G. Li, T. Yan, Tailoring the nanoscale boundary cavities in rutile TiO2 hierarchical microspheres for giant dielectric performance, J. Mater. Chem. 20 (2010) 8659-8667. https://doi.org/10.1039/C0JM01232G.
  229. X. Hu, H. Shen, Y. Cheng, X. Xiong, S. Wang, J. Fang, S. Wei, One-step modification of nano-hydroxyapa- tite coating on titanium surface by hydrothermal method, Surface and Coatings Technology. 205 (2010) 2000-2006. https://doi.org/10.1016/j.surfcoat.2010.08.088.
  230. S.C. Oh, H.Y. Jung, H. Lee, Effect of the top electrode materials on the resistive switching characteristics of TiO2 thin film, Journal of Applied Physics. 109 (2011) 124511. https://doi.org/10.1063/1.3596576.
  231. T. Dewolf, Nano-caractérisation des mécanismes de commutation dans les mémoires résistives à base d'HfO2, Université de Toulouse 3, 2018.
  232. K. Maas, E. Villepreux, D. Cooper, C. Jiménez, H. Roussel, L. Rapenne, X. Mescot, Q. Rafhay, M. Bou- dard, M. Burriel, Using a mixed ionic electronic conductor to build an analog memristive device with neuromorphic programming capabilities, J. Mater. Chem. C. 8 (2020) 464-472. https://doi.org/10.1039/C9TC03972D.
  233. K. Maas, E. Villepreux, D. Cooper, E. Salas-Colera, J. Rubio-Zuazo, G.R. Castro, O. Renault, C. Jimenez, H. Roussel, X. Mescot, Q. Rafhay, M. Boudard, M. Burriel, Tuning Memristivity by Varying the Oxygen Content in a Mixed Ionic-Electronic Conductor, Adv. Funct. Mater. 30 (2020) 1909942. https://doi.org/10.1002/adfm.201909942.
  234. I.K. Schuller, R. Stevens, R. Pino, M. Pechan, Neuromorphic Computing -From Materials Research to Systems Architecture Roundtable, USDOE Office of Science (SC) (United States), 2015. https://doi.org/10.2172/1283147.
  235. K. Watari, B. Brahmaroutu, G.L. Messing, S. Trolier-McKinstry, S.-C. Cheng, Epitaxial Growth of Aniso- tropically Shaped, Single-crystal Particles of Cubic SrTiO3, Journal of Materials Research. 15 (2000) 846-849. https://doi.org/10.1557/JMR.2000.0121.
  236. M.A. Bin Adnan, K. Arifin, L.J. Minggu, M.B. Kassim, Titanate-based perovskites for photochemical and photoelectrochemical water splitting applications: A review, International Journal of Hydrogen Energy. 43 (2018) 23209-23220. https://doi.org/10.1016/j.ijhydene.2018.10.173.
  237. X.-D. Wang, W.-G. Li, J.-F. Liao, D.-B. Kuang, Recent Advances in Halide Perovskite Single-Crystal Thin Films: Fabrication Methods and Optoelectronic Applications, Solar RRL. 3 (2019) 1800294. https://doi.org/10.1002/solr.201800294.
  238. H.H. Wu, D.R. Trinkle, Direct Diffusion through Interpenetrating Networks: Oxygen in Titanium, Phys.
  239. Rev. Lett. 107 (2011) 045504. https://doi.org/10.1103/PhysRevLett.107.045504.
  240. F.M.F. de Groot, M. Grioni, J.C. Fuggle, J. Ghijsen, G.A. Sawatzky, H. Petersen, Oxygen 1s x-ray-absorp- tion edges of transition-metal oxides, Phys. Rev. B. 40 (1989) 5715-5723. https://doi.org/10.1103/Phy- sRevB.40.5715.
  241. C. Di Valentin, G. Pacchioni, A. Selloni, Electronic Structure of Defect States in Hydroxylated and Redu- ced Rutile mathrmTiO2(110){\mathrm{TiO}}_{2}(110)mathrmTiO2(110) Surfaces, Phys. Rev. Lett. 97 (2006) 166803. https://doi.org/10.1103/Phy- sRevLett.97.166803.
  242. M. Abbate, G. Zampieri, F. Prado, A. Caneiro, J.M. Gonzalez-Calbet, M. Vallet-Regi, Electronic structure and metal-insulator transition in LaNiO 3 -δ, Phys. Rev. B. 65 (2002) 155101. https://doi.org/10.1103/Phy- sRevB.65.155101.
  243. Y. Wang, M.R.S. Huang, U. Salzberger, K. Hahn, W. Sigle, P.A. van Aken, Towards atomically resolved EELS elemental and fine structure mapping via multi-frame and energy-offset correction spectroscopy, Ultramicros- copy. 184 (2018) 98-105. https://doi.org/10.1016/j.ultramic.2017.10.014.
  244. L.A. Grunes, R.D. Leapman, C.N. Wilker, R. Hoffmann, A.B. Kunz, Oxygen K near-edge fine structure: An electron-energy-loss investigation with comparisons to new theory for selected 3 d Transition-metal oxides, Phys. Rev. B. 25 (1982) 7157-7173. https://doi.org/10.1103/PhysRevB.25.7157.
  245. J. Bassat, Anisotropic ionic transport properties in La2NiO4+δ single crystals, Solid State Ionics. 167 (2004) 341-347. https://doi.org/10.1016/j.ssi.2003.12.012.
  246. M. Burriel, G. Garcia, J. Santiso, J.A. Kilner, R.J. Chater, S.J. Skinner, Anisotropic oxygen diffusion proper- ties in epitaxial thin films of La 2 NiO 4+δ, J. Mater. Chem. 18 (2008) 416-422. https://doi.org/10.1039/B711341B.
  247. M. Burriel, H. Téllez, R.J. Chater, R. Castaing, P. Veber, M. Zaghrioui, T. Ishihara, J.A. Kilner, J.-M. Bassat, Influence of Crystal Orientation and Annealing on the Oxygen Diffusion and Surface Exchange of La 2 NiO 4+δ, J. Phys. Chem. C. 120 (2016) 17927-17938. https://doi.org/10.1021/acs.jpcc.6b05666\. EELS » -JEELS 2018 -11 ème Journées d'EELS 2018 (du 11 au 14 Juin), Porquerolles (FRANCE)
  248. • Auteur : Édouard Villepreux. « In-situ characterisation of Oxygen ions in memristive by advanced TEM » -EMC 2016 -16 ème European Microscopy Congress 2016 (du 28 Aout au 2 Septembre) Lyon, (FRANCE)