在多铁路YMnO3单晶中的内在无序网络,用于通过可调节的域墙结构进行内在物理储存器计算
Muzhen Xu1, Kyoka Furuta2, Ahmet Karacali3
1Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, 2-4 Hibikino, Wakamatsu, Kitakyushu, 808-0196, Japan.
Small (Weinheim an der Bergstrasse, Germany)
|September 12, 2025
概括
由于其固有的非线性,多铁基YMnO3单晶显示出对物理储库计算 (PRC) 的希望. 这些材料在像语音识别这样的复杂任务中实现了高精度和低功耗.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 物理储库计算 (PRC) 利用物理系统的非线性进行高效的计算.
- 多铁基YMnO3从其无序的域结构中表现出内在的非线性,这表明PRC的潜力.
- 高温功能和可调性是先进的PRC系统的关键要求.
研究的目的:
- 探索YMnO3单晶的潜力,作为物理储备计算的平台.
- 基于其非线性性质,系统地评估YMnO3的PRC性能.
主要方法:
- 对YMnO3.3的非线性反应,相位移和高维度的分析.
- 基准测试任务包括波形生成 (WG),内存容量 (MC) 和NARMA2时间序列预测.
- 对低功率语音识别能力的评估.
主要成果:
- YMnO3单晶在基准PRC任务中表现出卓越的性能.
- 在显著低功耗 (≈1.77 μW 和 ≈0.02 nW/域) 的情况下实现了高精度.
- 成功应用于低功率语音识别任务.
结论:
- YMnO3单晶是下一代物理储备计算的可行和有前途的材料.
- 该材料的性能解决了中国的关键挑战,包括效率和高温操作.
- 这项工作为开发先进,功能化的PRC设备奠定了基础.
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