Resistive switching localization by selective focused ion beam irradiation

被引:5
|
作者
Ghazikhanian, Nareg [1 ,2 ,3 ]
del Valle, Javier [1 ,4 ]
Salev, Pavel [1 ,5 ]
El Hage, Ralph [1 ]
Kalcheim, Yoav [1 ,6 ]
Adda, Coline [1 ]
Schuller, Ivan K. [1 ]
机构
[1] Univ Calif San Diego, Dept Phys, 9500 Gilman Dr, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Program Mat Sci & Engn, 9500 Gilman Dr, La Jolla, CA 92093 USA
[3] Univ Oviedo, Dept Phys, C-Feder Garcia Lorca 18, Oviedo 33007, Spain
[4] Univ Denver, Dept Phys & Astron, 2199 S Univ Blvd, Denver, CO 80208 USA
[5] Univ Denver, Dept Phys & Astron, 2199 S Univ Blvd, Denver, CO 80208 USA
[6] Technion Israel Inst Technol, Dept Mat Sci & Engn, IL-3200003 Haifa, Israel
关键词
METAL-INSULATOR-TRANSITION; VO2; OXIDES;
D O I
10.1063/5.0151823
中图分类号
O59 [应用物理学];
学科分类号
摘要
Materials displaying resistive switching have emerged as promising candidates for implementation as components for neuromorphic computing. Under an applied electric field, certain resistive switching materials undergo an insulator-to-metal transition through the formation of a percolating filament, resulting in large resistance changes. The location and shape of these filaments are strongly influenced by hard-to-control parameters, such as grain boundaries or intrinsic defects, making the switching process susceptible to cycle-to-cycle and device-to-device variation. Using focused Ga+ ion beam irradiation, we selectively engineer defects in VO2 and V2O3 thin films as a case study to control filament formation. Using defect pre-patterning, we can control the position and shape of metallic filaments and reduce the switching power significantly. A greater than three orders of magnitude reduction of switching power was observed in V2O3, and a less than one order of magnitude reduction was observed in VO2. These experiments indicate that selective ion irradiation could be applied to a variety of materials exhibiting resistive switching and could serve as a useful tool for designing scalable, energy efficient circuits for neuromorphic computing.
引用
收藏
页数:6
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