An Artificial Intelligence-Assisted Digital Microfluidic System for Multistate Droplet Control

Abstract

Digital microfluidics (DMF) is a versatile technique for parallel and field-programmable control of individual droplets. Given the high freedom in droplet manipulation, it is essential to establish self-adaptive and intelligent control methods for DMF systems with informed of the transient state of droplets and their interactions. However, most related studies focus on the localization and shape recognition of droplets. Here, we develop an AI-assisted DMF framework named "µDropAI" for multistate droplet control based on droplet morphology. Semantic segmentation model is integrated into our custom-designed DMF system to recognize the droplet states and their interactions for feedback control with a state machine. The proposed model has a strong flexibility and can recognize droplets of different colors and shapes with an error rate of less than 0.63%. It enables control of droplets without user intervention. The coefficient variation (CV) of the volumes of split droplets can be limited to below 2.8%, which is lower than the CV of traditional dispense, contributing to an improvement in the precision of droplet split. The proposed system will inspire the development of semantic-driven DMF systems which can interface with multimodal large language models (MLLM) for fully automatic control.