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.

With the increasing demand for fast, accurate, and reliable biological sensor systems, miniaturized systems have been aimed at droplet-based sensor systems and have been promising. A micro-electrode dot array (MEDA) biochip, which is one kind of the miniaturized systems for biochemical protocols such as dispensing, dilutions, mixing, and so on, has become widespread due to enabling dynamical control of the droplets in microfluidic manipulations. In MEDA biochips, the electrowetting-on-dielectric (EWOD) technique stands out since it can actuate droplets with nano/picoliter volumes. Microelectrode cells on MEDA actuate multiple droplets simultaneously to route locations for the purpose of the biochemical operations. Taking advantage of the feature, droplets are often routed in parallel to achieve high-throughput outcomes. Regarding parallel manipulation of multiple droplets, however, the droplets are known to be initially placed at a distant position to avoid undesirable mixing. The droplets thus result in traveling a long way for a manipulation, and the required biochip size for routing is also enlarged. This paper proposes a routing method for droplets to reduce the biochip size on a MEDA biochip with the allowance of splitting during routing operations. We mathematically derive the routing problem, and the experiments demonstrate that our proposal can significantly reduce the biochip size by 70.8% on average, compared to the state-of-the-art method.

Digital Microfluidic Biochips (DMFBs), used in various kinds of fields like DNA analysis, clinical diagnosis, and PCR testing, have made biochemical experiments more compact, efficient, and user-friendly than previous ways. However, their reliability is often compromised by their inability to adapt to all kinds of errors. All 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 through sensors or cameras. Unknown errors, in contrast, become apparent only during the routing process and remain undetected by sensors or cameras, which is the biggest issue to unexpectedly stop the routing process and diminishes 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 demonstrate that our algorithm outperforms previous ones in terms of the success rate of the routing in the scenario including both known errors and unknown errors. Additionally, our algorithm contributes to detecting unknown errors during the routing process and identifying the most efficient routing path with high probability.

In recent years, digital microfluidic biochips (DMFBs), based on microfluidic technology, have attracted attention as compact and flexible experimental devices. DMFBs are widely applied in biochemistry and medical fields, including point-of-care clinical diagnostics and PCR testing. Among them, micro electrode dot array (MEDA) biochips, composed of numerous microelectrodes, have overcome the limitations of conventional chips by enabling finer droplet manipulation and real-time sensing, thus significantly improving experimental efficiency. While various studies have been conducted to enhance the utilization of MEDA biochips, few have considered the shape-dependent velocity characteristics of droplets in routing. Moreover, methods that do take such characteristics into account often face significant challenges in solving time. This study proposes a fast droplet routing method for MEDA biochips that incorporates shape-dependent velocity characteristics by utilizing the distance information to the target cell. The experimental results demonstrate that the proposed method achieves approximately a 67.5% reduction in solving time compared to existing methods, without compromising solution quality.

Micro Electrode Dot Array (MEDA) biochips have recently attracted considerable attention in the biochemical and medical industries. MEDA biochips manipulate micro droplets for biochemical experiments such as DNA analysis. Droplets on MEDA biochips are moved using the Electrowetting on Dielectric (EWOD) effect, but a portion of a droplet may remain on a cell after passing through, contaminating the cell. Other droplets cannot pass through a contaminated cell. In previous studies, contaminated cells were considered unavailable for droplet routing. As the number of contaminated cells increases, droplets may be prevented from moving to the desired position. Therefore, we propose a method for simultaneous routing of target functional and washing droplets based on a shape-dependent velocity model. In a simulation, the proposed method reduced the routing time by about 10% compared with an existing method.

Digital microfluidic biochips are widely used for automated biochemical and diagnostic applications such as molecular analysis, immunoassays, and point-of-care testing. Among them, micro-electrode-dot-array (MEDA) biochips enable fine-grained droplet manipulation with shape-dependent velocity, which significantly increases routing flexibility but also computational complexity. This work proposes an efficient droplet routing method for MEDA biochips that reduces computational time while maintaining or improving solution quality. The proposed method introduces a guidance constraint that consistently directs droplets toward the target cell and integrates a solver-friendly model with a tight lower-bound design. Experimental results demonstrate that, for instances solvable by the existing method, the proposed method achieves comparable or better Routing time while reducing Solution-finding time by 72.3%. Moreover, the Solution-finding rate improves from 33.3% to 85.3% over all problem instances. Notably, for instances that cannot be solved within the time limit by the existing method, the proposed method successfully finds optimal solutions in 72.7% of the cases. These results indicate that the proposed method enables faster solution discovery, and extends solvability to previously intractable routing problems.