材料科学
矫顽力
电极
电容器
铁电性
极化(电化学)
钪
光电子学
凝聚态物理
薄膜
晶格常数
铁电RAM
切换时间
电压
铁电电容器
缩放比例
氮化物
电介质
应变工程
复合材料
作者
Yinuo Zhang,Rajeev Kumar,Giovanni Esteves,Yubo Wang,Deep Jariwala,Eric A. Stach,Roy H. Olsson
标识
DOI:10.1021/acsami.6c08411
摘要
Aluminum scandium nitride (AlScN) ferroelectrics are promising for next-generation non-volatile memory applications due to their high remnant polarization when compared to Pb(ZrxTi1-x)O3 and doped-HfO2 material systems, as well as their fast switching and scalability to nanometer thicknesses. For AlScN films at a 10 nm thickness, their coercive field substantially increases, which hinders low voltage operation. We demonstrate that interfacial engineering through bottom electrode selection and strain management reduces this coercive field increase with scaling and improves ferroelectric performance. We report robust ferroelectricity in ultra-thin AlScN capacitors deposited on a Sc bottom electrode under both alternating current and direct current conditions. The coercive field is reduced by over 20% compared to capacitors with an Al bottom electrode. We evaluated the difference in dynamic switching behavior across a decade of frequency by applying the frequency-scaling power law. At frequencies <16.7 kHz, the capacitors with Sc and Al bottom electrodes exhibit comparable frequency-scaling exponents of 0.030 and 0.028, respectively, indicating similar switching kinetics. However, at higher frequencies, the capacitor with an Al bottom electrode shows a significantly higher exponent value of 0.063, indicating a stronger frequency dependence, whereas the capacitor with a Sc bottom electrode maintains a stable exponent of 0.030, suggesting a lower frequency dependence during faster switching scenarios. We employed scanning electron nanobeam diffraction to measure the strain difference in AlScN thin films grown on templates with different lattice mismatches, providing a correlation between lattice mismatch, film strain, and switching behavior in ultra-thin film systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI