Dec 5, 2024
8:00pm - 10:00pm
Hynes, Level 1, Hall A
Ziyi Yuan1,Babak Bakhit1,Zhuotong Sun1,Markus Hellenbrand1,Xinjuan Li1,Simon Fairclough1,Caterina Ducati1,Haiyan Wang2,Judith Driscoll1
University of Cambridge1,Purdue University2
Ziyi Yuan1,Babak Bakhit1,Zhuotong Sun1,Markus Hellenbrand1,Xinjuan Li1,Simon Fairclough1,Caterina Ducati1,Haiyan Wang2,Judith Driscoll1
University of Cambridge1,Purdue University2
In today’s data-hungry world, data storage and computing technologies consume a huge share of the world’s electricity and have become one of the main global issues. To meet the net zero objectives, high-performance, energy-efficient memory and computing devices are urgently needed. Resistive switching non-volatile memory devices offer great promise, yet face major challenges, particularly in the endurance of filamentary-based devices, which limits their broader application. Here, we propose a novel thin-film design strategy to enhance endurance in resistive switching devices through nanocomposite engineering. Specifically, we developed a vertically aligned WO<sub>x</sub>:CeO<sub>x</sub> nanocomposite thin film structure. This structure gives excellent endurance performance of at least 10<sup>6</sup> cycles—an improvement of over ten times compared to existing similar devices in literatures. Furthermore, the devices exhibit robust data retention of at least 10<sup>4</sup> s, multi-level conductance states, and reliable device-to-device uniformity. These performance improvements are mainly attributed to the formation of vertically aligned nano-scale grains that provide uniformly distributed defective paths for conducting filament formation, unlike the random filament formation seen in traditional designs. The linear scaling of current with electrode area is found, indicating good scaling potential linked of the controlled spacing between the filaments of around 80 nm. We believe this carefully engineered vertically aligned nanocomposite structure offers a promising thin-film design for the next generation of non-volatile memory and neuromorphic computing applications.