Thermally Stable Ag<sub>2</sub>Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device
Abstract The artificial intelligence (AI) paradigm shifts from software to implementing general‐purpose or application‐specific hardware systems with lower power requirements. This study explored a material physical reservoir consisting of a material random network, called in‐materio physical reservoir computing (RC) to achieve efficient hardware systems. The device, made up of a random, highly interconnected network of nonlinear Ag 2 Se nanojunctions as reservoir nodes, demonstrated the requisite characteristics of an in‐materio physical reservoir, including but not limited to nonlinear switching, memory, and higher harmonic generation. The power consumption of the in‐materio physical reservoir is 0.07 nW per nanojunctions, confirming its highly efficient information processing system. As a hardware reservoir, the devices successfully performed waveform generation tasks. Finally, a voice classification by an in‐materio physical reservoir is achieved over 80%, comparable to an RC software simulation. In‐materio physical RC with rich nonlinear dynamics has huge potential for next‐generation hardware‐based AI.
Authors
- Takumi Kotooka (ORCID: https://orcid.org/0000-0002-4513-1878)
- Adam Z. Stieg (ORCID: https://orcid.org/0000-0001-7312-9364)
- Hakaru Tamukoh (ORCID: https://orcid.org/0000-0002-3669-1371)
- Yuki Usami (ORCID: https://orcid.org/0000-0002-8583-325X)
- Hirofumi Tanaka (ORCID: https://orcid.org/0000-0002-4378-5747)
- Sam Lilak (ORCID: https://orcid.org/0000-0003-4021-2895)
- Yuichiro Tanaka (ORCID: https://orcid.org/0000-0001-6974-070X)
- James K. Gimzewski (ORCID: https://orcid.org/0000-0003-4333-6957)
- Ahmet Karacali (ORCID: https://orcid.org/0009-0000-4695-2814)
- Naoyuki Sugiyama (ORCID: https://orcid.org/0000-0002-4549-7407)
- Takuya Kawabata
Institutions
- California NanoSystems Institute (US)
- University of California, Los Angeles (US)
- National Institute for Materials Science (JP)
- Kyushu Institute of Technology (JP)
- Toray Industries, Inc. (Japan) (JP)
Publication Details
- Journal
- Advanced Electronic Materials
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1002/aelm.202400443
- Citations
- 14
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 4.86
Funders
- Foundation for the Advancement of Mesoamerican Studies
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Society for the Promotion of Science
- Core Research for Evolutional Science and Technology
- Advanced Low Carbon Technology Research and Development Program
- ACT-X