Surfing the Growth Parameters in the Quest for Low‐Power, Forming‐Free, and Highly Stable TiO<i><sub>x</sub></i> Memristors for Nanoscale Electronics

Understanding the resistive switching (RS) behavior of oxide-based memory devices at nanoscale is crucial for advancement of high-integration density in-memory computing platforms. This study explores a comprehensive growth parameter space to address the RS behavior of pulsed-laser-deposited substoichiometric TiO<sub>2</sub> (TiO<sub>x</sub>) thin films in search of tailored nanoscale memristors with low-power consumption and high stability. Conductive-atomic-force-microscopy-based measurements facilitate deciphering the switching behavior at nanoscale, providing a direct avenue to understand the microstructure-property relationships. The present investigation reveals that rutile TiO<sub>x</sub> in an optimal stoichiometric configuration exhibits superior RS attributes, enabling forming-free, low-power, and highly stable memory functionalities at nanoscale. By contrast, the expected formation of the Magnéli phase within a highly defective as-grown TiO<sub>x</sub> film hinders the occurrence of switching. Detailed analyses yield a comprehensive parametric phase diagram, providing valuable insights to predict the optimal growth parameters for fabricating on-demand TiO<sub>x</sub>-based switching devices. As bulk RS attributes do not always translate seamlessly to the nanoscale, present study leads the pathway to develop tailored memristors for nanoscale electronics promoting their integration into ultrahigh-density, low-energy-consuming advanced memory technologies across diverse disciplines including robotics, data storage, and sensing.

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Publication Details

Journal
Small
Published
2025-01-16
DOI
https://doi.org/10.1002/smll.202408369
Citations
4
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.92
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article

Surfing the Growth Parameters in the Quest for Low‐Power, Forming‐Free, and Highly Stable TiOx Memristors for Nanoscale Electronics

Aparajita Mandal, S. K. Srivastava, Anirban Mitra, T. Som et al.
4 citations
Small
Advanced Memory and Neural Computing
2.92
article

Surfing the Growth Parameters in the Quest for Low‐Power, Forming‐Free, and Highly Stable TiOx Memristors for Nanoscale Electronics

Aparajita Mandal, S. K. Srivastava, Anirban Mitra, T. Som, Dilruba Hasina
article en
4 citations

Abstract

Understanding the resistive switching (RS) behavior of oxide-based memory devices at nanoscale is crucial for advancement of high-integration density in-memory computing platforms. This study explores a comprehensive growth parameter space to address the RS behavior of pulsed-laser-deposited substoichiometric TiO<sub>2</sub> (TiO<sub>x</sub>) thin films in search of tailored nanoscale memristors with low-power consumption and high stability. Conductive-atomic-force-microscopy-based measurements facilitate deciphering the switching behavior at nanoscale, providing a direct avenue to understand the microstructure-property relationships. The present investigation reveals that rutile TiO<sub>x</sub> in an optimal stoichiometric configuration exhibits superior RS attributes, enabling forming-free, low-power, and highly stable memory functionalities at nanoscale. By contrast, the expected formation of the Magnéli phase within a highly defective as-grown TiO<sub>x</sub> film hinders the occurrence of switching. Detailed analyses yield a comprehensive parametric phase diagram, providing valuable insights to predict the optimal growth parameters for fabricating on-demand TiO<sub>x</sub>-based switching devices. As bulk RS attributes do not always translate seamlessly to the nanoscale, present study leads the pathway to develop tailored memristors for nanoscale electronics promoting their integration into ultrahigh-density, low-energy-consuming advanced memory technologies across diverse disciplines including robotics, data storage, and sensing.

SmallVol. 21(7)
Indian Institute of Technology Kharagpur (IN), Indian Institute of Technology Roorkee (IN), Homi Bhabha National Institute (IN), Institute of Physics, Bhubaneshwar (IN)
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.92
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