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Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning
Tung-Che Liang · Zhanwei Zhong · Yaas Bigdeli · Tsung-Yi Ho · Krishnendu Chakrabarty · Richard Fair

Wed Jul 15 05:00 AM -- 05:45 AM & Wed Jul 15 04:00 PM -- 04:45 PM (PDT) @

We present and investigate a novel application domain for deep reinforcement learning (RL): droplet routing on digital microfluidic biochips (DMFBs). A DMFB, composed of a two-dimensional electrode array, manipulates discrete fluid droplets to automatically execute biochemical protocols such as high-throughput DNA sequencing and point-of-care clinical diagnosis. However, a major concern associated with the use of DMFBs is that electrodes in a biochip can degrade over time. Droplet-transportation operations associated with the degraded electrodes can fail, thereby compromising the integrity of the bioassay outcome. While it is not feasible to detect the degradation of an electrode by simply examining its appearance, we show that casting droplet transportation as an RL problem enables the training of deep network policies to capture the underlying health conditions of electrodes and to provide reliable fluidic operations. We propose a new RL-based droplet-routing flow that can be used for various sizes of DMFBs, and demonstrate reliable execution of an epigenetic bioassay with the RL droplet router on a fabricated DMFB. To facilitate further research, we also present a simulation environment based on the OpenAI Gym Interface for RL-guided droplet-routing problems on DMFBs.

Author Information

Tung-Che Liang (Duke University)

Tung-Che Liang received his B.S. degree in Electronics Engineering from National Chiao Tung University, Hsinchu, Taiwan, in 2014, and the M.S.E degree from the Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA, in 2020, where he is currently working toward the Ph.D. degree. He was with Synopsys Inc., Hsinchu, Taiwan, as an R&D engineer. He was a yield & diagnosis intern at Intel, Santa Clara, CA, a DFT intern at NVIDIA Inc., Santa Clara, CA, and a DFT intern at Apple Inc., Cupertino, CA. His current research interests include deep reinforcement learning, design automation, and security for microfluidic systems.

Zhanwei Zhong (Duke University)
Yaas Bigdeli (Duke Univsersity)
Tsung-Yi Ho (National Tsing Hua University)
Krishnendu Chakrabarty (Duke University)
Richard Fair (Duke University)

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