Presentation
DRL-based Voltage Optimization for Multiple Droplet Routing in DMFBs
DescriptionThis study presents a Deep Reinforcement Learning (DRL) framework for optimizing the routing of multiple droplets in Digital Microfluidic Biochips (DMFBs). Our approach significantly reduces computational costs by optimizing voltage-applied cell positions, enabling the parallel movement of multiple droplets with a single neural network pass. Experimental results on various DMFB sizes and droplet counts demonstrate a reduction of up to 98% in neural network parameters and 95% in memory usage for a 10x10 grid with two droplets. Additionally, the proposed method enhances success rates of routing as the number of droplets increases, surpassing existing multi-agent DRL techniques.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
AI
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