Bae Jongmin, Kwon Choah, Park See-On, Jeong Hakcheon, Park Taehoon, Jang Taehwan, Cho Yoonho, Kim Sangtae, Choi Shinhyun
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
Department of Nuclear Engineering, Hanyang University, Seoul 04763, Republic of Korea.
Sci Adv. 2024 Jun 7;10(23):eadm7221. doi: 10.1126/sciadv.adm7221.
Memristive neuromorphic computing has emerged as a promising computing paradigm for the upcoming artificial intelligence era, offering low power consumption and high speed. However, its commercialization remains challenging due to reliability issues from stochastic ion movements. Here, we propose an innovative method to enhance the memristive uniformity and performance through aliovalent halide doping. By introducing fluorine concentration into dynamic TiO memristors, we experimentally demonstrate reduced device variations, improved switching speeds, and enhanced switching windows. Atomistic simulations of amorphous TiO reveal that fluoride ions attract oxygen vacancies, improving the reversible redistribution and uniformity. A number of migration barrier calculations statistically show that fluoride ions also reduce the migration energies of nearby oxygen vacancies, facilitating ionic diffusion and high-speed switching. The detailed Voronoi volume analysis further suggests design principles in terms of the migrating species' electrostatic repulsion and migration barriers. This work presents an innovative methodology for the fabrication of reliable memristor devices, contributing to the realization of hardware-based neuromorphic systems.
忆阻神经形态计算已成为即将到来的人工智能时代一种很有前景的计算范式，具有低功耗和高速的特点。然而，由于随机离子运动导致的可靠性问题，其商业化仍然具有挑战性。在此，我们提出一种创新方法，通过异价卤化物掺杂来提高忆阻的均匀性和性能。通过将氟浓度引入动态TiO忆阻器中，我们通过实验证明了器件变化的减少、开关速度的提高和开关窗口的增强。非晶TiO的原子模拟表明，氟离子吸引氧空位，改善了可逆再分布和均匀性。大量迁移势垒计算从统计学上表明，氟离子还降低了附近氧空位的迁移能，促进了离子扩散和高速开关。详细的Voronoi体积分析进一步提出了关于迁移物种静电排斥和迁移势垒的设计原则。这项工作提出了一种制造可靠忆阻器器件的创新方法，有助于基于硬件的神经形态系统的实现。
