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Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VOsub.2
by
Salev, Pavel
, Zimmers, Alexandre
, Simmons, Forrest
, Carlson, Erica W
, Raymond, Nicolas
, Schuller, Ivan K
, Aigouy, Lionel
, Alzate Banguero, Melissa
, Basak, Sayan
in
Dielectric films
/ Magnetic properties
/ Network switches
/ Neural networks
/ Thin films
2025
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Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VOsub.2
by
Salev, Pavel
, Zimmers, Alexandre
, Simmons, Forrest
, Carlson, Erica W
, Raymond, Nicolas
, Schuller, Ivan K
, Aigouy, Lionel
, Alzate Banguero, Melissa
, Basak, Sayan
in
Dielectric films
/ Magnetic properties
/ Network switches
/ Neural networks
/ Thin films
2025
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Do you wish to request the book?
Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VOsub.2
by
Salev, Pavel
, Zimmers, Alexandre
, Simmons, Forrest
, Carlson, Erica W
, Raymond, Nicolas
, Schuller, Ivan K
, Aigouy, Lionel
, Alzate Banguero, Melissa
, Basak, Sayan
in
Dielectric films
/ Magnetic properties
/ Network switches
/ Neural networks
/ Thin films
2025
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Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VOsub.2
Journal Article
Optical Mapping and On-Demand Selection of Local Hysteresis Properties in VOsub.2
2025
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Overview
Quantum materials have tremendous potential for disruptive applications. However, scaling devices down has been challenging due to electronic inhomogeneities in many of these materials. Understanding and controlling these electronic patterns on a local scale has thus become crucial to further new applications. To address this issue, we have developed a new optical microscopy method that allows for the precise quasi-continuous filming of the insulator-to-metal transition in VO[sub.2] with fine temperature steps. This enables us to track metal and insulator domains over thousands of images and quantify, for the first time, the local hysteresis properties of VO[sub.2] thin films. The analysis of the maps has allowed us to quantify cycle-to-cycle reproducibility of the local transitions and reveals a positive correlation between the local insulator–metal transition temperatures T[sub.c] and the local hysteresis widths ΔT[sub.c]. These maps also enable the optical selection of regions of high or low transition temperature in combination with large or nearly absent local hysteresis. These maps pave the way to understand and use stochasticity to advantage in these materials by picking on-demand transition properties, allowing the scaling down of devices such as optical switches, infrared microbolometers and spiking neural networks.
Publisher
MDPI AG
Subject
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