Master Mold Assisted Printing Enables Morphology Controlled Conjugated Polymer Transistors

Efficient charge transport in conjugated polymer-based thin-film transistors (PTFTs) is governed by the interplay between interchain hopping and intrachain transport. Because intrachain transport enables mobility significantly faster than interchain hopping, precise backbone alignment and intramolecular ordering are essential. However, conventional techniques such as spin-coating and dip-coating often induce structural disorder with limited morphological control, while inkjet printing suffers from insufficient chain alignment and low patterning resolution.

Here, we propose a master mold-assisted printing (MAP) technique that enables unidirectional alignment of conjugated polymer backbones with high structural regularity. Through guided confinement, MAP facilitates controlled polymer assembly, yielding enhanced crystallographic ordering and reduced energetic disorder. Chain alignment and anisotropic morphology were characterized by atomic force microscopy (AFM) and polarized UV-Vis spectroscopy. PTFTs fabricated via MAP exhibited significantly improved electrical characteristics compared with spin-coated counterparts, and temperature-dependent measurements revealed reduced activation energy and improved charge transport efficiency. Furthermore, MAP enables monolithic integration of complementary p- and n-type transistors on a single substrate in a single fabrication step. The resulting complementary inverter demonstrated ultra-high voltage gain and low static power consumption, achieved through precise control of the number of aligned backbones, enabling balanced channel conductance and optimized complementary operation.

Acknowledgments
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS2022NR071808). Also, this research was supported by Korea Basic Science Institute (National research Facilities and Equipment Center) grant funded by Ministry of Education (grant No. 2022R1A6C101B738).