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Correlative mapping of local hysteresis properties in VO2

2023/01/10 by Melissa Alzate Banguero, Banguero, Melissa Alzate, Sayan Basak +15
Computer Science · Engineering · Materials Science · #CCD and CMOS Imaging Sensors #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Applications #Strongly Correlated Electrons (cond-mat.str-el) #Transition Metal Oxide Nanomaterials

paper · pdf · doi:10.48550/arxiv.2301.04220

openalex publication_date 2023/01/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We have developed a new optical microscopy technique able to track micron-sized surface clusters as temperature is varied. Potential candidates for study include phase separated metal-insulator materials, ferroelectrics, and porous structures. Several key techniques (including autofocus, step motor/cross correlation alignments, single-pixel thresholding, pair connectivity correlation length and image convolution) were implemented in order to obtain a time series of thresholded images. Here, we apply this new method to probe the archetypal phase separated insulator-metal transition in VO2. A precise time and temperature series of the insulator-metal transition was achieved, allowing us to construct for the first time in this material spatial maps of the transition temperature Tc. These maps reveal multiple interesting features such as fractal electronic patterns on micron scales, regions of the sample with an extremely large or nearly absent local hysteresis, a positive correlation between the Tc value and the hysteresis width ΔTc, and high cycle-to-cycle reproducibility of the transition. These maps also allow for the identification of individual pixels with unique transition characteristics. This unprecedented knowledge of the local properties of each spot along with the behavior of the entire network paves the way to novel electronics applications enabled by, \em e.g., addressing specific regions with desired memory and/or switching characteristics, as well as detailed explorations of open questions in the theory of hysteresis.

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