Electrohydrodynamic jet printing enables Micro-OLEDs multilayer structure preparation

Abstract

The application of advanced technologies such as virtual reality (VR) and augmented reality (AR) is driving the innovation of ultra-high-resolution display panels, such as Micro-Organic Light-Emitting Diodes (Micro-OLEDs). Micro-OLEDs typically consist of multilayer structures, and some researchers have opted for electrofluidic inkjet printing technology over the traditional vapor deposition process. This preference is due to its advantages in achieving high resolution, enabling additive manufacturing. However, when printing multilayer structures, the deposition charge from the bottom layer, along with electric field crosstalk, can cause printing defects. This paper introduces two innovative modules into the conventional printing process: a deep reinforcement learning framework for dynamic height adjustment and a ’run-to-run’ data control strategy. The Soft Actor–Critic (SAC) deep reinforcement learning algorithm is employed to develop a strategy for regulating process parameters and the height of the printed structure. This approach allows for the precise control of the multilayer structure height, compensating for the impact of accumulated charges in complex electric fields. Using the electrofluidic printing platform, a three-layer structure was printed on pixel pits with HIL, HTL, and EML inks. This printing process yielded OLED devices with 1200 ppi resolution, adjustable volume, and a stable structure. The uniformity of the printed layer height achieved 96.3%. Furthermore, Micro-OLEDs devices with a resolution of 3600 ppi were successfully fabricated, meeting the resolution requirements for most current display panels.