Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing

Physical reservoir computing (PRC) using synaptic devices has attracted attention as a promising edge artificial intelligence device. To handle time-series data on various time scales, it is necessary to fabricate devices with the desired time scale. In this study, we fabricated a dye-sensitized solar-cell-based synaptic device with controllable time constants by changing the light intensity. This device showed synaptic features, such as paired-pulse facilitation and paired-pulse depression, in response to light intensity. Moreover, we found that the high computational performance of the time-series data processing task was achieved by changing the light intensity, even when the input pulse width was varied. In addition, the fabricated device can be used for motion recognition tasks. This study paves the way for realizing multiple time-scale PRC.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-10-28
DOI
https://doi.org/10.1021/acsami.4c11061
Citations
5
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.74

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article

Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing

Takashi Ikuno, Hiroaki Komatsu, Norika Hosoda
5 citations
ACS Applied Materials & Interfaces
Neural Networks and Reservoir Computing
1.74
article

Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing

Takashi Ikuno, Hiroaki Komatsu, Norika Hosoda
article en
5 citations

Abstract

Physical reservoir computing (PRC) using synaptic devices has attracted attention as a promising edge artificial intelligence device. To handle time-series data on various time scales, it is necessary to fabricate devices with the desired time scale. In this study, we fabricated a dye-sensitized solar-cell-based synaptic device with controllable time constants by changing the light intensity. This device showed synaptic features, such as paired-pulse facilitation and paired-pulse depression, in response to light intensity. Moreover, we found that the high computational performance of the time-series data processing task was achieved by changing the light intensity, even when the input pulse width was varied. In addition, the fabricated device can be used for motion recognition tasks. This study paves the way for realizing multiple time-scale PRC.

ACS Applied Materials & InterfacesVol. 17(3)
Tokyo University of Science (JP)
Japan Science and Technology Agency
Affordable and clean energy
Openalex Percentile: Top 13%
Neural Networks and Reservoir Computing
1.74
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Self-Powered Dye-Sensitized Solar-Cell-Based Synaptic Devices for Multi-Scale Time-Series Data Processing in Physical Reservoir Computing — Takashi Ikuno, Hiroaki Komatsu, et al. · ACS Applied Materials & Interfaces (2024) | TGRS Research Map | TGRS