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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (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.

Original languageEnglish
Article numberSCCF02
JournalJapanese journal of applied physics
Issue numberSC
Publication statusPublished - 2021 Jun
Externally publishedYes

ASJC Scopus subject areas

  • General Engineering
  • General Physics and Astronomy


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