Site-Selective Nanowire\nSynthesis and Fabrication\nof Printed Memristor Arrays with Ultralow Switching Voltages on Flexible\nSubstrate
Large area electronics (LAE) with the capability to sense\nand retain\ninformation are crucial for advances in applications such as wearables,\ndigital healthcare, and robotics. The big data generated by these\nsensor-laden systems need to be scaled down or processed locally.\nIn this regard, brain-inspired computing and in-memory computing have\nattracted considerable interest. However, suitable architectures have\nmainly been developed using costly and resource-intensive conventional\nlithography-based methods. There is a need for the development of\ninnovative, resource-efficient fabrication routes that enable such\ndevices and concepts. Herein, we present ZnO nanowire (NW)-based memristors\non a polyimide substrate fabricated by a LAE-compatible and resource-efficient\nroute comprising solution processing and printing technologies. High-resolution\n“drop-on-demand” and “direct ink write”\nprinters are employed to deposit metallic layers (silver and gold)\nand a ZnO seed layer, needed for the site-selective growth of ZnO\nNWs via a low-cost hydrothermal method. The printed memristors show\nhigh bipolar resistance switching (ON/OFF ratio >10<sup>3</sup>) between\ntwo nonvolatile states and consistent switching at ultralow voltages\n(all devices showed switching at amplitudes <200 mV), with the\nbest performing device showing consistent cycled resistance switching\nover 4 orders of magnitude with SET and RESET voltages of about 71\nand −57 mV, respectively. Thus, the presented devices offer\nreliable high resistance switching at the lowest reported voltage\nfor printed memristors and prove to be competitive with many conventional\nnanofabrication-based devices. The presented results show the potential\nprinted memristors technology holds for large-area, low-voltage sensing\napplications such as electronic skin.
Publication Details
- Journal
- Figshare
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1021/acsami.4c07172.s001
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00