Intrinsic Disordered Network in Multiferroic YMnO 3 Single Crystals for In‐Materio Physical Reservoir Computing Through Tuneable Domain‐Wall Structure (Small 48/2025)
Source abstract
Abstract
Physical Reservoir Computing Multiferroic YMnO3 features cloverleaf-like domain structures forming an intrinsically disordered yet functional network within the single crystal. The resulting nonlinearity enables complex electrical dynamics suitable for in-materio physical reservoir computing. This self-organized network realizes low-power operation while preserving high computational accuracy, highlighting YMnO3 as a promising material for next-generation neuromorphic and AI computing. More in article number 2506397, Hirofumi Tanaka, Yoichi Horibe, and co-workers.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Neural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Center for Neuromorphic AI Hardware Kyushu Institute of Technology 2–4 Hibikino, Wakamatsu Kitakyushu 808–0196 Japan
Department of Materials Science and Engineering Kyushu Institute of Technology 1–1 Sensui, Tobata Kitakyushu 804–8550 Japan
Department of Human Intelligence Systems Kyushu Institute of Technology 2–4 Hibikino, Wakamatsu Kitakyushu 808–0196 Japan
References
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