Performance of Ag-Ag2S core-shell nanoparticle-based random network reservoir computing device

Hadiyawarman, Yuki Usami*, Takumi Kotooka, Saman Azhari, Masanori Eguchi, Hirofumi Tanaka*


研究成果: Article査読

10 被引用数 (Scopus)


Reservoir computing (RC), a low-power computational framework derived from recurrent neural networks, is suitable for temporal/sequential data processing. Here, we report the development of RC devices utilizing Ag-Ag2S core-shell nanoparticles (NPs), synthesized by a simple wet chemical protocol, as the reservoir layer. We examined the NP-based reservoir layer for the required properties of RC hardware, such as echo state property, and then performed the benchmark tasks. Our study on NP-based reservoirs highlighted the importance of the dynamics between the NPs as indicated by the rich high dimensionality due to the echo state property. These dynamics affected the accuracy (up to 99%) of the target waveforms that were generated with a low number of readout channels. Our study demonstrates the great potential of Ag-Ag2S NPs for the development of next-generation RC hardware.

ジャーナルJapanese journal of applied physics
出版ステータスPublished - 2021 6月

ASJC Scopus subject areas

  • 工学一般
  • 物理学および天文学一般


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