| Autor: |
Saha, Basudev, Das, Bidyut, Majumder, Mukta |
| Předmět: |
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| Zdroj: |
Nanotechnology & Precision Engineering; Jun2023, Vol. 6 Issue 2, p1-12, 12p |
| Abstrakt: |
Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. However, for complex bioassays, finding routes for the transportation of droplets in an electrowetting-on-dielectric digital biochip while maintaining their discreteness is a challenging task. In this study, we propose a deep reinforcement learning-based droplet routing technique for digital microfluidic biochips. The technique is implemented on a distributed architecture to optimize the possible paths for predefined source–target pairs of droplets. The actors of the technique calculate the possible routes of the source–target pairs and store the experience in a replay buffer, and the learner fetches the experiences and updates the routing paths. The proposed algorithm was applied to benchmark suites I and III as two different test benches, and it achieved significant improvements over state-of-the-art techniques. HIGHLIGHTS: • Deep-reinforcement learning approach in a distributed framework for droplet transportation on digital microfluidic biochips. • Optimizing the droplet transportation time for homogeneous multiple droplets by sharing the common electrodes. • A sequence of intelligent trade-offs to handle the fluidic constraints for parallel droplet transportation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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