Thermally Stable Ag2Se 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 reserv...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2024-12-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400443 |
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| author | Takumi Kotooka Sam Lilak Adam Z. Stieg James K. Gimzewski Naoyuki Sugiyama Yuichiro Tanaka Takuya Kawabata Ahmet Karacali Hakaru Tamukoh Yuki Usami Hirofumi Tanaka |
| author_facet | Takumi Kotooka Sam Lilak Adam Z. Stieg James K. Gimzewski Naoyuki Sugiyama Yuichiro Tanaka Takuya Kawabata Ahmet Karacali Hakaru Tamukoh Yuki Usami Hirofumi Tanaka |
| author_sort | Takumi Kotooka |
| collection | DOAJ |
| description | 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 Ag2Se 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. |
| format | Article |
| id | doaj-art-08d05e4269cd40bc8fe438503c90e264 |
| institution | Kabale University |
| issn | 2199-160X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-08d05e4269cd40bc8fe438503c90e2642025-01-09T11:51:13ZengWiley-VCHAdvanced Electronic Materials2199-160X2024-12-011012n/an/a10.1002/aelm.202400443Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing DeviceTakumi Kotooka0Sam Lilak1Adam Z. Stieg2James K. Gimzewski3Naoyuki Sugiyama4Yuichiro Tanaka5Takuya Kawabata6Ahmet Karacali7Hakaru Tamukoh8Yuki Usami9Hirofumi Tanaka10Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Chemistry and Biochemistry University of California Los Angeles (UCLA) Los Angeles CA 90095 USACalifornia NanoSystems Institute University of California Los Angeles (UCLA) Los Angeles CA 90095 USADepartment of Chemistry and Biochemistry University of California Los Angeles (UCLA) Los Angeles CA 90095 USAToray Research Center Inc. Morphological Research Laboratory 3−2−11 Sonoyama Otsu Shiga 520‐0842 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanDepartment of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology (Kyutech) 2–4 Hibikino, Wakamatsu Kitakyushu 808‐0196 JapanAbstract 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 Ag2Se 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.https://doi.org/10.1002/aelm.202400443reservoir computingsilver selenide nanowire networkvoice classification |
| spellingShingle | Takumi Kotooka Sam Lilak Adam Z. Stieg James K. Gimzewski Naoyuki Sugiyama Yuichiro Tanaka Takuya Kawabata Ahmet Karacali Hakaru Tamukoh Yuki Usami Hirofumi Tanaka Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device Advanced Electronic Materials reservoir computing silver selenide nanowire network voice classification |
| title | Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device |
| title_full | Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device |
| title_fullStr | Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device |
| title_full_unstemmed | Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device |
| title_short | Thermally Stable Ag2Se Nanowire Network as an Effective In‐Materio Physical Reservoir Computing Device |
| title_sort | thermally stable ag2se nanowire network as an effective in materio physical reservoir computing device |
| topic | reservoir computing silver selenide nanowire network voice classification |
| url | https://doi.org/10.1002/aelm.202400443 |
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