Physical reservoir operation utilizing polarization change in a liquid crystal film

Abstract In recent years, there has been growing interest in physical reservoir computing, which operates with low power consumption and low computational cost, utilizing various phenomena for their operations. In this study, we developed a liquid crystal-based reservoir that utilizes dielectric polarization as a new state variable for the physical reservoir operation. Nonlinear polarization change in a liquid crystal film was confirmed both by observation using a polarized optical microscope and by electrical measurements. We also evaluated the performance of the liquid crystal reservoir by basic methods such as short-term memory tasks. In the MNIST classification task, we achieved an accuracy of approximately 88%, suggesting the potential of the liquid crystal reservoir for future applications.

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Publication Details

Journal
Japanese Journal of Applied Physics
Published
2025-02-18
DOI
https://doi.org/10.35848/1347-4065/adb757
Citations
1
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
2.48
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article

Physical reservoir operation utilizing polarization change in a liquid crystal film

Tsuyoshi Hasegawa, Hirofumi Tanaka, Shusaku Nagano, Atsuhiro Mizuno
1 citations
Japanese Journal of Applied Physics
Neural Networks and Reservoir Computing
2.48
article

Physical reservoir operation utilizing polarization change in a liquid crystal film

Tsuyoshi Hasegawa, Hirofumi Tanaka, Shusaku Nagano, Atsuhiro Mizuno
article en
1 citations

Abstract

Abstract In recent years, there has been growing interest in physical reservoir computing, which operates with low power consumption and low computational cost, utilizing various phenomena for their operations. In this study, we developed a liquid crystal-based reservoir that utilizes dielectric polarization as a new state variable for the physical reservoir operation. Nonlinear polarization change in a liquid crystal film was confirmed both by observation using a polarized optical microscope and by electrical measurements. We also evaluated the performance of the liquid crystal reservoir by basic methods such as short-term memory tasks. In the MNIST classification task, we achieved an accuracy of approximately 88%, suggesting the potential of the liquid crystal reservoir for future applications.

Japanese Journal of Applied PhysicsVol. 64(3)
Rikkyo University (JP), Waseda University (JP), Kyushu Institute of Technology (JP)
Affordable and clean energy
Openalex Percentile: Top 12%
Neural Networks and Reservoir Computing
2.48
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Physical reservoir operation utilizing polarization change in a liquid crystal film — Tsuyoshi Hasegawa, Hirofumi Tanaka, et al. · Japanese Journal of Applied Physics (2025) | TGRS Research Map | TGRS