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Anthony J. Kenyon
Researcher at University College London
Publications - 174
Citations - 5950
Anthony J. Kenyon is an academic researcher from University College London. The author has contributed to research in topics: Silicon & Photoluminescence. The author has an hindex of 31, co-authored 168 publications receiving 4961 citations. Previous affiliations of Anthony J. Kenyon include University of Sussex.
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Recent developments in rare-earth doped materials for optoelectronics
TL;DR: In this paper, the state of the art in this rapidly growing field is discussed and new classes of materials that open up new possibilities for extended functionality and greater optoelectronic integration are described.
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Recommended Methods to Study Resistive Switching Devices
Mario Lanza,H.-S. Philip Wong,Eric Pop,Daniele Ielmini,Dimitri Strukov,B. C. Regan,Luca Larcher,Marco A. Villena,Jianhua Yang,Ludovic Goux,Attilio Belmonte,Yuchao Yang,Francesco Maria Puglisi,Jinfeng Kang,Blanka Magyari-Köpe,Eilam Yalon,Anthony J. Kenyon,Mark Buckwell,Adnan Mehonic,Alexander L. Shluger,Haitong Li,Tuo-Hung Hou,Boris Hudec,Deji Akinwande,Ruijing Ge,Stefano Ambrogio,Juan Bautista Roldán,Enrique Miranda,Jordi Suñé,Kin Leong Pey,Xing Wu,Nagarajan Raghavan,Ernest Y. Wu,Wei Lu,Gabriele Navarro,Weidong Zhang,Huaqiang Wu,Run-Wei Li,Alexander W. Holleitner,Ursula Wurstbauer,Max C. Lemme,Ming Liu,Shibing Long,Qi Liu,Hangbing Lv,Andrea Padovani,Paolo Pavan,Ilia Valov,Xu Jing,Tingting Han,Kaichen Zhu,Shaochuan Chen,Fei Hui,Yuanyuan Shi +53 more
TL;DR: This manuscript describes the most recommendable methodologies for the fabrication, characterization, and simulation of RS devices, as well as the proper methods to display the data obtained.
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Erbium in silicon
TL;DR: A review of the current state of what remains an active field can be found in this article, summarizing results from a range of studies conducted over the last few years, and pointing to further developments by considering the prospects for successful photonic integration of erbium and silicon.
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Committee machines-a universal method to deal with non-idealities in memristor-based neural networks.
Dovydas Joksas,Pedro Freitas,Chai Zheng,Wing H. Ng,Mark Buckwell,Can Li,Weidong Zhang,Qiangfei Xia,Anthony J. Kenyon,Adnan Mehonic +9 more
TL;DR: A technology-agnostic approach, committee machines, is demonstrated, which increases the inference accuracy of memristive neural networks that suffer from device variability, faulty devices, random telegraph noise and line resistance.
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Resistive switching in silicon suboxide films
Adnan Mehonic,Sébastien Cueff,M Wojdak,Stephen Hudziak,Olivier Jambois,Christophe Labbé,Blas Garrido,Richard Rizk,Anthony J. Kenyon +8 more
TL;DR: In this article, the authors demonstrate that conduction is dominated by trap assisted tunneling through noncontinuous conduction paths consisting of silicon nanoinclusions in a highly nonstoichiometric suboxide phase.