Double DQN for Chip-Level Synthesis of Paper-Based Digital Microfluidic Biochips
Fang-Chi Wu1, Jian-De Li2, Katherine Shu-Min Li1, Sying-Jyan Wang2 and Tsung-Yi Ho3
1Department of Computer Science and Engineering, National Sun Yat-Sen University
2Department of Computer Science and Engineering, National Chung Hsing University
3Department of Computer Science, National Tsing Hua University
ABSTRACT
Paper-based digital microfluidic biochip (PBDMFB) technology is one of the most promising solutions in biochemical applications due to the paper substrate. The paper substrate makes PB-DMFBs more portable, cost-effective, and less dependent on manufacturing equipment. However, the single-layer paper substrate, which entangles electrodes, conductive wires, and droplet routing in the same layer, raises challenges to chip-level synthesis of PB-DMFBs. Furthermore, current design automation tools have to address various design issues including manufacturing cost, reliability, and security. Therefore, a more flexible chip-level synthesis method is necessary. In this paper, we propose the first reinforcement learning based chip-level synthesis for PB-DMFBs. Double deep Q-learning networks are adapted for the agent to select and estimate actions, and then we obtain the optimized synthesis results. Experimental results show that the proposed method is not only effective and efficient for chip-level synthesis but also scalable to reliability and security–oriented schemes.
Keywords: Paper-Based Digital Microfluidic Biochips, Chiplevel Synthesis, Reinforcement Learning, Double DQN.
