Adaptive Droplet Routing for MEDA Biochips via Deep Reinforcement Learning

Abstract

Digital microfluidic biochips (DMFBs) based on a micro-electrode-dot-array (MEDA) architecture provide fine-grained control and sensing of droplets in real-time. However, excessive actuation of microelectrodes in MEDA biochips can lead to charge trapping during bioassay execution, causing the failure of microelectrodes and erroneous bioassay outcomes. A recently proposed enhancement to MEDA allows run-time measurement of microelectrode health information, thereby enabling synthesis of adaptive routing strategies for droplets. However, existing synthesis solutions are computationally infeasible for large MEDA biochips that have been commercialized. In this paper, we propose a synthesis framework for adaptive droplet routing in MEDA biochips via deep reinforcement learning (DRL). The framework utilizes the real-time microelectrode health feedback to synthesize droplet routes that proactively minimize the likelihood of charge trapping. We show how the adaptive routing strategies can be synthesized using DRL. We implement the DRL agent, the MEDA simulation environment, and the bioassay scheduler using the OpenAI Gym environment. Our framework obtains adaptive routing policies efficiently for COVID-19 testing protocols on large arrays that reflect the sizes of commercial MEDA biochips available in the marketplace, significantly increasing probabilities of successful bioassay completion compared to existing methods.