[3PS-046]
Laser-Patterned Vertical Organic Electrochemical Transistor Arrays for Neuromorphic Applications
발표자이인호 (아주대학교)
연구책임자박성준 (아주대학교)
Abstract
Organic electrochemical transistors (OECTs) operate via ion–electron coupling, wherein ionic flux within an electrolyte modulates the conductivity of a semiconducting polymer channel. This mechanism enables time-dependent conductance modulation analogous to biological synaptic plasticity, allowing OECTs to emulate learning and memory functions in hardware. Such characteristics render OECTs promising for neuromorphic signal processing and biointerfacing applications. Here, we present a 6 × 6 multi-electrode array based on vertically structured OECTs fabricated through a laser-patterning process. In contrast to conventional photolithographic or vacuum-based approaches, the proposed method directly defines both channel and electrode geometries with high precision, significantly simplifying fabrication while improving scalability and alignment accuracy. This maskless process enables large-area patterning and uniform device integration in a single step. The vertical device architecture minimizes the channel length to the nanoscale, thereby enhancing ion–electron coupling efficiency and charge injection. As a result, the devices exhibit a high transconductance of ~20 mS and operate at voltages below 1 V. The OECT array demonstrates reproducible, gradual, and reversible conductance modulation under pulsed stimulation, exhibiting both short- and long-term synaptic plasticity essential for neuromorphic learning. Furthermore, the array shows excellent device-to-device uniformity and operational stability over repeated cycling, confirming its reliability for high-density neuromorphic hardware integration. Overall, this work establishes a laser-patterned vertical OECT array architecture that combines high performance, high integration density, and process simplicity, providing a scalable route toward skin-conformal intelligent sensors, bio-signal-driven learning systems, and next-generation human–machine interfaces.