Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Murat, Onen"'
Autor:
Andrew Dane, Jason Allmaras, Di Zhu, Murat Onen, Marco Colangelo, Reza Baghdadi, Jean-Luc Tambasco, Yukimi Morimoto, Ignacio Estay Forno, Ilya Charaev, Qingyuan Zhao, Mikhail Skvortsov, Alexander Kozorezov, Karl K. Berggren
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-8 (2022)
Unlocking the ultimate potential of superconducting nanowire single photon detectors requires engineering their thermal properties. Here, the authors improve our understanding of heat flow in these devices and suggest routes to improved performance.
Externí odkaz:
https://doaj.org/article/802db61884cf4b3289bbd3f1515fc0cd
Autor:
Murat Onen, Tayfun Gokmen, Teodor K. Todorov, Tomasz Nowicki, Jesús A. del Alamo, John Rozen, Wilfried Haensch, Seyoung Kim
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Analog crossbar arrays comprising programmable non-volatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices criticall
Externí odkaz:
https://doaj.org/article/1af78532c4534d7194bbf6db0d93a1e1
Autor:
Xiahui Yao, Konstantin Klyukin, Wenjie Lu, Murat Onen, Seungchan Ryu, Dongha Kim, Nicolas Emond, Iradwikanari Waluyo, Adrian Hunt, Jesús A. del Alamo, Ju Li, Bilge Yildiz
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
Designing energy efficient neural networks based on synaptic memristor devices remains a challenge. Here, the authors propose the development of a 3-terminal WO3 synaptic device based on proton intercalation in inorganic materials by leveraging a sol
Externí odkaz:
https://doaj.org/article/0856e109046d4101a3dd9ec0156e6679
Publikováno v:
2023 7th IEEE Electron Devices Technology & Manufacturing Conference (EDTM).
Publikováno v:
Frontiers in Neuroscience, Vol 11 (2017)
In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistiv
Externí odkaz:
https://doaj.org/article/a709e92b51ee41cab02c0f0591b7ee0c
Publikováno v:
Advanced materials (Deerfield Beach, Fla.).
Artificial neural networks based on crossbar arrays of analog programmable resistors could address the high energy challenge of conventional hardware in artificial intelligence applications. However, state-of-the-art two-terminal resistive switching
Autor:
Murat Onen, Nicolas Emond, Baoming Wang, Difei Zhang, Frances M. Ross, Ju Li, Bilge Yildiz, Jesús A. del Alamo
Publikováno v:
Science (New York, N.Y.). 377(6605)
Nanoscale ionic programmable resistors for analog deep learning are 1000 times smaller than biological cells, but it is not yet clear how much faster they can be relative to neurons and synapses. Scaling analyses of ionic transport and charge-transfe
Autor:
Murat, Onen, Tayfun, Gokmen, Teodor K, Todorov, Tomasz, Nowicki, Jesús A, Del Alamo, John, Rozen, Wilfried, Haensch, Seyoung, Kim
Analog crossbar arrays comprising programmable non-volatile resistors are under intense investigation for acceleration of deep neural network training. However, the ubiquitous asymmetric conductance modulation of practical resistive devices criticall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe9770806be38e81a3190e77d9ca21b3
Publikováno v:
MIT web domain
Ion intercalation based programmable resistors have emerged as a potential next-generation technology for analog deep-learning applications. Proton, being the smallest ion, is a very promising candidate to enable devices with high modulation speed, l
Autor:
Andrew E. Dane, Qing-Yuan Zhao, Jason P. Allmaras, Marco Colangelo, Murat Onen, A. G. Kozorezov, Reza Baghdadi, Ignacio Estay Forno, Mikhail A. Skvortsov, Jean-Luc J. Tambasco, Yukimi Morimoto, Karl K. Berggren, Ilya Charaev, Di Zhu
Controlling thermal transport is important for a range of devices and technologies, from phase change memories to next-generation electronics. This is especially true in nano-scale devices where thermal transport is altered by the influence of surfac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::952d03b26bb24a5f44c45b7bfc3be9cc
http://arxiv.org/abs/2104.04652
http://arxiv.org/abs/2104.04652