Analysis of “on” and “off” times for thermally driven VO2 metal-insulator transition nanoscale switching devices

2011 ◽  
Vol 62 (1) ◽  
pp. 161-164 ◽  
Author(s):  
Yan Zhang ◽  
Shriram Ramanathan
Keyword(s):  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Thermally Driven ◽  
Switching Devices

scholarly journals Thermally driven analog of the Barkhausen effect at the metal-insulator transition in vanadium dioxide

2014 ◽  
Vol 105 (13) ◽  
pp. 131902 ◽  
Author(s):  
Benjamin Huber-Rodriguez ◽  
Siu Yi Kwang ◽  
Will J. Hardy ◽  
Heng Ji ◽  
Chih-Wei Chen ◽  
...  
Keyword(s):  
Vanadium Dioxide ◽  
Insulator Transition ◽  
Barkhausen Effect ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Thermally Driven

W Doping and Voltage Driven Metal–Insulator Transition in VO2 Nano-Films for Smart Switching Devices

2019 ◽  
Vol 2 (10) ◽  
pp. 6738-6746 ◽  
Author(s):  
Chen Ling ◽  
Zhengjing Zhao ◽  
Xinyuan Hu ◽  
Jingbo Li ◽  
Xushan Zhao ◽  
...  
Keyword(s):  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
W Doping ◽  
Switching Devices

scholarly journals Machines for Materials and Materials for Machines: Metal-Insulator Transitions and Artificial Intelligence

2021 ◽  
Vol 9 ◽  
Author(s):  
Jennifer Fowlie ◽  
Alexandru Bogdan Georgescu ◽  
Bernat Mundet ◽  
Javier del Valle ◽  
Philippe Tückmantel
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Rare Earth ◽  
Phase Transitions ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Network Analyses ◽  
Correlated Electron ◽  
Thermally Driven

In this perspective, we discuss the current and future impact of artificial intelligence and machine learning for the purposes of better understanding phase transitions, particularly in correlated electron materials. We take as a model system the rare-earth nickelates, famous for their thermally-driven metal-insulator transition, and describe various complementary approaches in which machine learning can contribute to the scientific process. In particular, we focus on electron microscopy as a bottom-up approach and metascale statistical analyses of classes of metal-insulator transition materials as a bottom-down approach. Finally, we outline how this improved understanding will lead to better control of phase transitions and present as an example the implementation of rare-earth nickelates in resistive switching devices. These devices could see a future as part of a neuromorphic computing architecture, providing a more efficient platform for neural network analyses – a key area of machine learning.

Fermi-surface reconstruction close to a pressure-induced metal-insulator transition

2004 ◽  
Vol 114 ◽  
pp. 277-281 ◽  
Author(s):  
J. Wosnitza ◽  
J. Hagel ◽  
O. Stockert ◽  
C. Pfleiderer ◽  
J. A. Schlueter ◽  
...  
Keyword(s):  
Fermi Surface ◽  
Surface Reconstruction ◽  
Insulator Transition ◽  
Metal Insulator Transition ◽  
Metal Insulator ◽  
Fermi Surface Reconstruction

Crystal growth and metal-insulator transition in two-dimensional layered rare-earth palladates

2018 ◽  
Vol 2 (8) ◽  
Author(s):  
Yoshiko Nanao ◽  
Yoshiharu Krockenberger ◽  
Ai Ikeda ◽  
Yoshitaka Taniyasu ◽  
Michio Naito ◽  
...  
Keyword(s):  
Rare Earth ◽  
Crystal Growth ◽  
Insulator Transition ◽  
Two Dimensional ◽  
Metal Insulator Transition ◽  
Metal Insulator
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