Sihn Sangwook, Chambers William L, Abedin Minhaz, Beckmann Karsten, Cady Nathaniel, Ganguli Sabyasachi, Roy Ajit K
Air Force Research Laboratory, Materials and Manufacturing Directorate, AFRL/RX, Wright-Patterson Air Force Base, Dayton, OH, 45433, USA.
University of Dayton Research Institute, Structural Materials Division, Dayton, OH, 45469, USA.
Small. 2025 Jul;21(28):e2310542. doi: 10.1002/smll.202310542. Epub 2024 Mar 22.
Memristors, non-volatile switching memory platform, has recently attracted significant interest, offering unique potential to enable the realization of human brain-like neuromorphic computing efficiency. Memristors also demonstrate excellent temperature tolerance, long-term durability, and high tunability with nanosecond pulses, making them highly attractive for neuromorphic computing applications. To better understand the material processing, microstructure, and property relationship of switching mechanisms in memristor devices, computational methodologies, and tools are developed to predict the I-V characteristics of memristor devices based on tantalum oxide (TaO) resistive random-access memory (ReRAM) integrated with an n-channel metal-oxide-semiconductor (NMOS) transistor. A multiphysics model based on coupled partial differential equations for electrical and thermal transport phenomena is solved for the high- and low-resistance states during the formation, growth, and destruction of a conducting filament through SET and RESET stages. These stages effectively represent the migration of oxygen vacancies within an oxide exchange layer. A series of parametric studies and energy minimization calculations are conducted to determine probable ranges for key material and model parameters accounting for the experimental data. The computational model successfully predicted the measured I-V curves across various gate voltages applied to the NMOS transistor in the one transistor one resistance (1T1R) configuration.
忆阻器作为一种非易失性开关存储平台，近来引起了广泛关注，它为实现类似人类大脑的神经形态计算效率提供了独特潜力。忆阻器还展现出优异的耐温性、长期耐久性以及对纳秒脉冲的高可调性，这使其在神经形态计算应用中极具吸引力。为了更好地理解忆阻器器件中开关机制的材料加工、微观结构和性能关系，人们开发了计算方法和工具，以基于与n沟道金属氧化物半导体（NMOS）晶体管集成的氧化钽（TaO）电阻式随机存取存储器（ReRAM）来预测忆阻器器件的电流-电压（I-V）特性。通过求解基于耦合偏微分方程的多物理模型，来描述在通过SET和RESET阶段形成、生长和破坏导电细丝期间的高电阻和低电阻状态下的电和热传输现象。这些阶段有效地代表了氧化物交换层内氧空位的迁移。进行了一系列参数研究和能量最小化计算，以确定考虑实验数据的关键材料和模型参数的可能范围。该计算模型成功预测了在单晶体管单电阻（1T1R）配置中施加到NMOS晶体管的各种栅极电压下测得的I-V曲线。
