Physical Reservoir Computing Utilizing Ion‐Gating Transistors Operating in Electric Double Layer and Redox Mechanisms
Abstract The enormous energy consumption of modern machine learning technologies, such as deep learning and generative artificial intelligence, is one of the most critical concerns of the time. To solve this problem, physical reservoir computing, which uses the non‐linear dynamics exhibited by mecha...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2024-12-01
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| Series: | Advanced Electronic Materials |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aelm.202400625 |
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| Summary: | Abstract The enormous energy consumption of modern machine learning technologies, such as deep learning and generative artificial intelligence, is one of the most critical concerns of the time. To solve this problem, physical reservoir computing, which uses the non‐linear dynamics exhibited by mechanical systems such as materials and devices as a computational resource for highly efficient information processing, has attracted much attention in recent years. In particular, ion‐gated transistors, a group of devices that control electrical conductivity using electrochemical mechanisms such as electric double layers and redox, show very high computational performance with complex and diverse output properties in contrast to their simple structures, due to the complexity of the physical and chemical processes involved. This research provides an overview of physical reservoir computing using ion‐gating transistors, focusing on the materials used, various computational tasks, and operating mechanisms. |
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| ISSN: | 2199-160X |