Room-temperature solution-phase graphoepitaxial growth of in-plane nanowire arrays on flexible films for bendable synaptic devices
Recent advancements in artificial intelligence have spurred growing interest in developing innovative architectures for artificial synapses. Among these, nanowires have emerged as promising candidates for creating lightweight, flexible, and energy-efficient synapses. However, achieving in-plane aligned growth of nanowires on flexible substrates poses a substantial challenge for their integration into bendable synapses. This study introduces a room-temperature solution-phase graphoepitaxial growth technique that facilitates the in-plane aligned growth of nanowires along hydrophilic nanogrooves on flexible polyvinyl alcohol films. This scalable method obviates the need for complex vacuum systems and bypasses the constraints of traditional lattice-matching epitaxy by leveraging surface topography to guide nanowire alignment. Devices incorporating aligned tri-isopropylsilylethynyl pentacene nanowires exhibit a wavelength-sensitive photoresponse and mimic fundamental biological synaptic behaviors, including paired pulse facilitation and spike-number-dependent plasticity. Furthermore, these devices demonstrate exceptional bending stability, maintaining consistent synaptic response even after 2000 bends at a curvature radius of 0.4 cm. The approach's versatility is further highlighted by its applicability to the in-plane aligned growth of diverse organic nanowire arrays. By seamlessly integrating these aligned nanowires into devices without requiring post-growth transfer and assembly, this approach simplifies fabrication processes and improves device durability. This study underscores the transformative potential of solution-phase graphoepitaxial growth as a scalable and efficient strategy for advancing flexible and conformable nanowire-based devices and technologies.
Authors
- Hanyu Liu (ORCID: https://orcid.org/0009-0001-7479-0178)
- Jinyou Xu (ORCID: https://orcid.org/0000-0002-2405-6346)
- Xiangtao Chen (ORCID: https://orcid.org/0000-0002-5102-1472)
- Daquan Zhang (ORCID: https://orcid.org/0000-0002-4464-0099)
- Yanbin Chen (ORCID: https://orcid.org/0000-0001-6259-595X)
- Pingyang Huang
- Zhanhao Liang (ORCID: https://orcid.org/0009-0007-9822-9346)
- Guofu Zhou (ORCID: https://orcid.org/0000-0003-1101-1947)
- Shubin Yi
- Wei Zhou (ORCID: https://orcid.org/0000-0002-4901-242X)
- Qiming Yang (ORCID: https://orcid.org/0009-0006-0813-3934)
- W. Mao (ORCID: https://orcid.org/0009-0006-6862-8146)
Institutions
- South China Normal University (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1063/5.0258019
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- Guangdong Provincial Pearl River Talents Program
- Basic and Applied Basic Research Foundation of Guangdong Province