With an increase in the demand for smart wearable systems,
artificial
synapse arrays for flexible neural networks have received considerable
attention. A synaptic device with a two-terminal configuration is
promising for complex neural networks because of its ability to scale
to a crossbar array architecture. To realize practical crossbar arrays
with a high density, it is essential to achieve reliable electrode
lines that act as signal terminals. However, an effective method to
develop intrinsically flexible signal lines in artificial neural networks
has not been developed. In this study, we achieved reliable polymer
signal lines for flexible neural networks using coffee ring-free micromolding
in capillaries (MIMIC). In a typical MIMIC, the outward convective
flow of the polymer solution inherently deteriorates the pattern fidelity.
To achieve reliable conducting polymer (CP) lines, we precisely controlled
the flow of the polymer solution in the MIMIC by inducing the Marangoni
force. When the convective and Marangoni flows for the solution were
balanced in the MIMIC, the CP line patterns were reliably produced
with high fidelity. The developed CP lines exhibited superior conductivity
and high mechanical flexibility. Moreover, flexible memristor arrays
consisting of CP signal lines demonstrated a high potential for realizing
practical neuromorphic systems linked to artificial intelligence.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.