Electrothermally Induced Channel Formation in a Spin-Crossover Neuron

There is growing interest in correlated oxides that can switch between volatile resistance states when an electrical bias is applied, functioning as artificial neurons in neuromorphic computing systems. Most devices typically rely on first-order insulator-metal transitions (IMT). However, recent discoveries have shown that devices made of a second-order spin-transition material, such as LaCoO<sub>3</sub> (LCO), can exhibit different or improved functionalities. Despite their significance, the microscopic details surrounding the formation of conductive channels have still been unreported. In this study, the spatiotemporal details of channel formation are revealed by using a combination of infrared (IR) and Raman microscopy. Comparison of LCO and materials such as VO<sub>2</sub> reveals critical differences with important ramifications for computing. First, the findings indicate that LCO channels are narrower and more efficient than VO<sub>2</sub>, but they are also more sensitive to electric fields and disorder. Channels are found to repeatedly hop between different locations under steady-state oscillations, a behavior not previously reported. Additionally, memory effects at high bias are observed. The experiments, along with finite element simulations (FES), suggest that the spin transition in LCO may significantly influence channel nucleation, leading to an increased sensitivity of neuronal devices to disorder and electrode geometry. We discuss how the inherent stochasticity and memory effects could enable functionalities in neuromorphic computing.

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Publication Details

Journal
ACS Nano
Published
2026-02-13
DOI
https://doi.org/10.1021/acsnano.5c17869
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Electrothermally Induced Channel Formation in a Spin-Crossover Neuron

Elliot J. Fuller, Patrick J. Shamberger, Mahnaz Islam, Suhas Kumar et al.
ACS Nano
Advanced Memory and Neural Computing
article

Electrothermally Induced Channel Formation in a Spin-Crossover Neuron

Elliot J. Fuller, Patrick J. Shamberger, Mahnaz Islam, Suhas Kumar, Timothy D. Brown, Fatme Jardali, Elena Salagre, Yeonju Yu, Eric Pop, R. Stanley Williams, A. Alec Talin, Sanjana Goyal, Sangheon Oh
article en

Abstract

There is growing interest in correlated oxides that can switch between volatile resistance states when an electrical bias is applied, functioning as artificial neurons in neuromorphic computing systems. Most devices typically rely on first-order insulator-metal transitions (IMT). However, recent discoveries have shown that devices made of a second-order spin-transition material, such as LaCoO<sub>3</sub> (LCO), can exhibit different or improved functionalities. Despite their significance, the microscopic details surrounding the formation of conductive channels have still been unreported. In this study, the spatiotemporal details of channel formation are revealed by using a combination of infrared (IR) and Raman microscopy. Comparison of LCO and materials such as VO<sub>2</sub> reveals critical differences with important ramifications for computing. First, the findings indicate that LCO channels are narrower and more efficient than VO<sub>2</sub>, but they are also more sensitive to electric fields and disorder. Channels are found to repeatedly hop between different locations under steady-state oscillations, a behavior not previously reported. Additionally, memory effects at high bias are observed. The experiments, along with finite element simulations (FES), suggest that the spin transition in LCO may significantly influence channel nucleation, leading to an increased sensitivity of neuronal devices to disorder and electrode geometry. We discuss how the inherent stochasticity and memory effects could enable functionalities in neuromorphic computing.

ACS NanoVol. 20(8)
Berkeley College (US), Sandia National Laboratories California (US), Mitchell Institute (US), Texas A&M University (US), University of California, Berkeley (US), Stanford University (US)
Openalex Percentile: Top 57%
Advanced Memory and Neural Computing
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