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用于低温内存计算的铜酸盐超导体中的场致相变

Field-Induced Phase Transitions in Cuprate Superconductors for Cryogenic in-Memory Computing.

作者信息

Günkel Thomas, Alcalà Jordi, Fernández Alejandro, Barrera Aleix, Balcells Lluís, Mestres Narcís, Miranda Enrique, Suñé Jordi, Palau Anna

机构信息

Insititut de Ciència de Materials de Barcelona, ICMAB-CSIC, Campus de la UAB, Bellaterra, 08193, Spain.

Departament d'Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, 08193, Spain.

出版信息

Small. 2025 Apr;21(14):e2411908. doi: 10.1002/smll.202411908. Epub 2025 Mar 3.

Abstract

Energy-efficient cryogenic memory systems play a critical role in a wide spectrum of applications focused on ultra-energy-efficient information and communication technologies, such as quantum computing or superconducting electronics. Neuromorphic systems, known for their superior energy efficiency, have emerged as a promising approach for in-memory computing. Specifically, strongly correlated oxides that exhibit Mott metal-insulator transitions through field-induced oxygen movement are of great interest for analog memory and neuromorphic computing. Yet, optimizing their performance at low temperatures may prove challenging due to their reliance on ionic motion. In this study, superconducting structures composed of strongly correlated YBaCuO (YBCO) combined with ferromagnetic (LSMO) are investigated to obtain non-volatile multilevel memristive switching effects with high performance at cryogenic temperatures. This research reveals the presence of two competing switching mechanisms, which are attributed to the movement of oxygen vacancies and electric carriers within these structures. It is determined that a phase transition induced by the movement of holes is the primary factor influencing the switching dynamics at low temperatures. Additionally, a physics-based compact model is proposed that accurately replicates the experimental findings and provides a tool for circuit-level design.

摘要

节能低温存储系统在一系列专注于超节能信息和通信技术的应用中发挥着关键作用,如量子计算或超导电子学。以其卓越的能源效率而闻名的神经形态系统,已成为内存计算的一种有前途的方法。具体而言,通过场致氧迁移表现出莫特金属-绝缘体转变的强关联氧化物,对于模拟存储和神经形态计算具有极大的吸引力。然而,由于它们依赖离子运动,在低温下优化其性能可能具有挑战性。在本研究中,对由强关联的钇钡铜氧(YBCO)与铁磁体(LSMO)组成的超导结构进行了研究,以在低温下获得具有高性能的非易失性多电平忆阻开关效应。这项研究揭示了两种相互竞争的开关机制的存在,这归因于这些结构中氧空位和载流子的移动。确定由空穴移动引起的相变是影响低温下开关动力学的主要因素。此外,还提出了一个基于物理的紧凑模型,该模型准确地复制了实验结果,并为电路级设计提供了一个工具。

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