Shin Wonjun, Min Kyung Kyu, Bae Jong-Ho, Yim Jiyong, Kwon Dongseok, Kim Yeonwoo, Yu Junsu, Hwang Joon, Park Byung-Gook, Kwon Daewoong, Lee Jong-Ho
Department of Electrical and Computer Engineering and Inter-university Semiconductor Research Center, Seoul National University, Seoul 08826, Republic of Korea.
SK Hynix Inc., Icheon 17336, Korea.
Nanoscale. 2022 Feb 10;14(6):2177-2185. doi: 10.1039/d1nr06525d.
Recently, ferroelectric tunnel junctions (FTJs) have gained extensive attention as possible candidates for emerging memory and synaptic devices for neuromorphic computing. However, the working principles of FTJs remain controversial despite the importance of understanding them. In this study, we demonstrate a comprehensive and accurate analysis of the working principles of a metal-ferroelectric-dielectric-semiconductor stacked FTJ using low-frequency noise (LFN) spectroscopy. In contrast to resistive random access memory, the 1/ noise of the FTJ in the low-resistance state (LRS) is approximately two orders of magnitude larger than that in the high-resistance state (HRS), indicating that the conduction mechanism in each state differs significantly. Furthermore, the factors determining the conduction of the FTJ in each state are revealed through a systematic investigation under various conditions, such as varying the electrical bias, temperature, and bias stress. In addition, we propose an efficient method to decrease the LFN of the FTJ in both the LRS and HRS using high-pressure forming gas annealing.
最近，铁电隧道结（FTJs）作为新兴的用于神经形态计算的存储器和突触器件的潜在候选者受到了广泛关注。然而，尽管理解铁电隧道结的工作原理很重要，但其工作原理仍存在争议。在本研究中，我们使用低频噪声（LFN）光谱对金属-铁电体-电介质-半导体堆叠的铁电隧道结的工作原理进行了全面而准确的分析。与电阻式随机存取存储器不同，铁电隧道结在低电阻状态（LRS）下的1/噪声比在高电阻状态（HRS）下大约大两个数量级，这表明每种状态下的传导机制有显著差异。此外，通过在各种条件下进行系统研究，如改变电偏压、温度和偏压应力，揭示了决定铁电隧道结在每种状态下传导的因素。此外，我们提出了一种使用高压形成气体退火来降低铁电隧道结在低电阻状态和高电阻状态下低频噪声的有效方法。
