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Parallel Droplet Control in MEDA Biochips using Multi-Agent Reinforcement Learning
Tung-Che Liang · Jin Zhou · Yun-Sheng Chan · Tsung-Yi Ho · Krishnendu Chakrabarty · Cy Lee

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @

Microfluidic biochips are being utilized for clinical diagnostics, including COVID-19 testing, because of they provide sample-to-result turnaround at low cost. Recently, microelectrode-dot-array (MEDA) biochips have been proposed to advance microfluidics technology. A MEDA biochip manipulates droplets of nano/picoliter volumes to automatically execute biochemical protocols. During bioassay execution, droplets are transported in parallel to achieve high-throughput outcomes. However, a major concern associated with the use of MEDA biochips is microelectrode degradation over time. Recent work has shown that formulating droplet transportation as a reinforcement-learning (RL) problem enables the training of policies to capture the underlying health conditions of microelectrodes and ensure reliable fluidic operations. However, the above RL-based approach suffers from two key limitations: 1) it cannot be used for concurrent transportation of multiple droplets; 2) it requires the availability of CCD cameras for monitoring droplet movement. To overcome these problems, we present a multi-agent reinforcement learning (MARL) droplet-routing solution that can be used for various sizes of MEDA biochips with integrated sensors, and we demonstrate the reliable execution of a serial-dilution bioassay with the MARL droplet router on a fabricated MEDA biochip. To facilitate further research, we also present a simulation environment based on the PettingZoo Gym Interface for MARL-guided droplet-routing problems on MEDA biochips.

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.

Jin Zhou (Duke University)
Yun-Sheng Chan (National Chiao Tung University)
Tsung-Yi Ho (National Tsing Hua University)
Krishnendu Chakrabarty (Duke University)
Cy Lee (National Chiao Tung University)

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