Ponis John, Jerla Nicholas, Agbeworvi George, Perez-Beltran Saul, Kumar Nitin, Ashen Kenna, Li Jialu, Wang Edrick, Smeaton Michelle A, Jardali Fatme, Chakraborty Sarbajeet, Shamberger Patrick J, Jungjohann Katherine L, Weiland Conan, Jaye Cherno, Ma Lu, Fischer Daniel, Guo Jinghua, Sambandamurthy G, Qian Xiaofeng, Banerjee Sarbajit
Department of Chemistry, Texas A&M University, College Station, Texas 77843, United States.
Department of Physics, University at Buffalo, State University of New York, Buffalo, New York 14260, United States.
J Am Chem Soc. 2024 Dec 18;146(50):34536-34550. doi: 10.1021/jacs.4c11968. Epub 2024 Dec 4.
Building artificial neurons and synapses is key to achieving the promise of energy efficiency and acceleration envisioned for brain-inspired information processing. Emulating the spiking behavior of biological neurons in physical materials requires precise programming of conductance nonlinearities. Strong correlated solid-state compounds exhibit pronounced nonlinearities such as metal-insulator transitions arising from dynamic electron-electron and electron-lattice interactions. However, a detailed understanding of atomic rearrangements and their implications for electronic structure remains obscure. In this work, we unveil discontinuous conductance switching from an antiferromagnetic insulator to a paramagnetic metal in ε-CuVO. Distinctively, fashioning nonlinear dynamical oscillators from entire millimeter-sized crystals allows us to map the structural transformations underpinning conductance switching at an atomistic scale using single-crystal X-ray diffraction. We observe superlattice ordering of Cu ions between [VO] layers at low temperatures, a direct result of interchain Cu-ion migration and intrachain reorganization. The resulting charge and spin ordering along the vanadium oxide framework stabilizes an insulating state. Using X-ray absorption and emission spectroscopies, assigned with the aid of electronic structure calculations and measurements of partially and completely decuprated samples, we find that Cu 3 and V 3 orbitals are closely overlapped near the Fermi level. The filling and overlap of these states, specifically the narrowing/broadening of V 3 states near the Fermi level, mediate conductance switching upon Cu-ion rearrangement. Understanding the mechanisms of conductance nonlinearities in terms of ion motion along specific trajectories can enable the atomistic design of neuromorphic active elements through strategies such as cointercalation and site-selective modification.
构建人工神经元和突触是实现脑启发式信息处理所设想的能源效率和加速潜力的关键。在物理材料中模拟生物神经元的尖峰行为需要对电导非线性进行精确编程。强关联固态化合物表现出明显的非线性，如由动态电子-电子和电子-晶格相互作用引起的金属-绝缘体转变。然而，对原子重排及其对电子结构的影响的详细理解仍然模糊不清。在这项工作中，我们揭示了ε-CuVO中从反铁磁绝缘体到顺磁金属的不连续电导切换。独特的是，用整个毫米大小的晶体构建非线性动态振荡器，使我们能够使用单晶X射线衍射在原子尺度上绘制出支撑电导切换的结构转变。我们观察到低温下[VO]层之间Cu离子的超晶格有序排列，这是链间Cu离子迁移和链内重组的直接结果。沿氧化钒框架产生的电荷和自旋有序稳定了绝缘状态。通过借助电子结构计算以及对部分和完全脱铜样品的测量来进行X射线吸收和发射光谱分析，我们发现Cu 3和V 3轨道在费米能级附近紧密重叠。这些状态的填充和重叠，特别是费米能级附近V 3状态的变窄/变宽，在Cu离子重排时介导电导切换。从沿着特定轨迹的离子运动角度理解电导非线性机制，可通过共嵌入和位点选择性修饰等策略实现神经形态活性元件的原子设计。
