Quantum Resistance in Multilayer Graphene-BiFeO3 Memristor for Brain-Inspired Computing
Institute of Physics（印度物理研究所）；Homi Bhabha National Institute（霍米·巴巴国立研究所）；Sambalpur University（桑巴尔普尔大学）；Chalmers University of Technology（查尔姆斯理工大学）；Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Saclay（巴黎萨克雷大学纳米科学和纳米技术中心，法国国家科学研究中心）；Institute of Nano Science and Technology（纳米科学与技术研究所）；Indian Institute of Technology Madras（马德拉斯印度理工学院）
Suman Roy, Priyanka Sahu, Subhabrata Das, Sameer Kumar Mallik, Susmita Jana, Alok Kumar, Himadri Nandan Mohanty, Kaushik Ghosh, B. R. K. Nanda, Satyaprakash Sahoo
In the era of big data and the Internet of Things, quantum-level control of conductance states offers a promising route toward high-density data storage and brain-inspired neuromorphic computing. Although quantum conductance (QC) phenomena have been demonstrated in various metal oxide memristors, achieving reliable and precise control over quantized states remains in its infancy. Here, we demonstrate bidirectional quantum conductance states in multifunctional BiFeO3 (BFO) perovskite memristors integrated with multilayer-graphene contacts, enabling higher-order tunability and revealing the potential of perovskite-2D heterostructures for quantum-engineered memory and computing devices. XPS analysis provides detailed insights into oxygen vacancy dynamics in BFO, whereas first-principles density functional theory calculations clearly reveal a strong localized electric field at the graphene-BFO interface. Our devices exhibit current-controlled higher-order QC transitions facilitated by quantum point contact formation, giving rise to quantized conductance states during both SET and RESET processes. Time-lag correlation maps quantify the stochastic evolution of QC states under dynamic voltage-pulse tuning schemes. Notably, the quantized conductance states effectively emulate synaptic potentiation and depression, enabling precise weight modulation for high-accuracy image and digit recognition in convolutional neural networks. These findings establish perovskite-2D heterostructures as promising candidates for QC-driven resistive switching and demonstrate their potential for developing controllable quantum memristors.
Dynamic Behaviors and Training Effects in TiN/Ti/HfO$_x$/TiN Nanolayered Memristors with Controllable Quantized Conductance States: Implications for Quantum and Neuromorphic Computing Devices
Low-Power Control of Resistance Switching Transitions in First-Order Memristors
Institute of Physics, Opole University（奥波莱大学物理研究所）；Department of Electronics and Telecommunications Politecnico di Torino（都灵理工大学电子与电信系）；Department of Physics and Astronomy, University of South Carolina（南卡罗来纳大学物理与天文学系）
Hysteretic magnetic pinning and reversible resistance switching in High-Tc superconductor/ferromagnet multilayers
我们研究了一个高TC超导（YBa 2Cu 3 O 7-d）/铁磁（Co/Pt多层）的混合体，它表现出由磁历史驱动的电阻开关：取决于外部磁场的方向，观察到的混合态电阻的显着减少或增加作为磁化反转发生在Co/Pt多层。我们证明，杂散磁场通过i）创建的涡旋/反涡旋和ii）由外部磁场引起的涡旋的静磁钉扎引起这些效应。
