Falcone Donato Francesco, Menzel Stephan, Stecconi Tommaso, Galetta Matteo, La Porta Antonio, Offrein Bert Jan, Bragaglia Valeria
IBM Research Europe - Zürich, 8803 Rüschlikon, Switzerland.
Peter Gruenberg Institute 7, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany.
Nanoscale Horiz. 2024 Apr 29;9(5):775-784. doi: 10.1039/d4nh00072b.
The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems. In this context, novel analog filamentary conductive-metal-oxide (CMO)/HfO redox-based resistive switching memory (ReRAM) represents a key technology. Despite device performance enhancements reported in literature, the underlying mechanism behind resistive switching is not fully understood. This work presents the first physics-based analytical model of the current transport and of the resistive switching in these devices. As a case study, analog TaO/HfO ReRAM devices are considered. The current transport is explained by a trap-to-trap tunneling process, and the resistive switching by a modulation of the defect density within the sub-band of the TaO that behaves as electric field and temperature confinement layer. The local temperature and electric field distributions are derived from the solution of the electric and heat transport equations in a 3D finite element ReRAM model. The intermediate resistive states are described as a gradual modulation of the TaO defect density, which results in a variation of its electrical conductivity. The drift-dynamics of ions during the resistive switching is analytically described, allowing the estimation of defect migration energies in the TaO layer. Moreover, the role of the electro-thermal properties of the CMO layer is unveiled. The proposed analytical model accurately describes the experimental switching characteristic of analog TaO/HfO ReRAM devices, increasing the physical understanding and providing the equations necessary for circuit simulations incorporating this technology.
忆阻器技术与编程算法的近期协同优化，使得利用内存计算系统进行神经网络训练成为可能。在此背景下，新型的基于氧化还原的丝状导电金属氧化物（CMO）/HfO电阻式开关存储器（ReRAM）成为一项关键技术。尽管文献中报道了器件性能的提升，但电阻式开关背后的潜在机制仍未完全理解。这项工作提出了首个基于物理的这些器件中电流输运和电阻式开关的分析模型。作为案例研究，考虑了模拟TaO/HfO ReRAM器件。电流输运由陷阱到陷阱的隧穿过程来解释，电阻式开关则由TaO子带内缺陷密度的调制来解释，TaO子带起到电场和温度限制层的作用。局部温度和电场分布由三维有限元ReRAM模型中的电输运和热输运方程的解得出。中间电阻状态被描述为TaO缺陷密度的逐渐调制，这导致其电导率发生变化。分析描述了电阻式开关过程中离子的漂移动力学，从而能够估计TaO层中的缺陷迁移能量。此外，还揭示了CMO层的电热特性的作用。所提出的分析模型准确地描述了模拟TaO/HfO ReRAM器件的实验开关特性，增进了物理理解，并提供了将该技术纳入电路模拟所需的方程。
