A Deep Reinforcement Learning Approach to Droplet Routing for Erroneous Digital Microfluidic Biochips

Abstract

Digital microfluidic biochips (DMFBs), which are used in various fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than the previous methods. However, their reliability is often compromised by their inability to adapt to all kinds of errors. Errors in biochips can be categorized into two types: known errors, and unknown errors. Known errors are detectable before the start of the routing process using sensors or cameras. Unknown errors, in contrast, only become apparent during the routing process and remain undetected by sensors or cameras, which can unexpectedly stop the routing process and diminish the reliability of biochips. This paper introduces a deep reinforcement learning-based routing algorithm, designed to manage not only known errors but also unknown errors. Our experiments demonstrated that our algorithm outperformed the previous ones in terms of the success rate of the routing, in the scenarios including both known errors and unknown errors. Additionally, our algorithm contributed to detecting unknown errors during the routing process, identifying the most efficient routing path with a high probability.