One-pass Architectural Synthesis for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

LIU Genggeng; JIAO Xinyue; PAN Youlin; HUANG Xing

doi:10.11999/JEIT251058

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LIU Genggeng, JIAO Xinyue, PAN Youlin, HUANG Xing. One-pass Architectural Synthesis for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251058

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LIU Genggeng, JIAO Xinyue, PAN Youlin, HUANG Xing. One-pass Architectural Synthesis for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251058

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One-pass Architectural Synthesis for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

doi: 10.11999/JEIT251058 cstr: 32379.14.JEIT251058

LIU Genggeng^{1, 2, 3},
JIAO Xinyue^{1, 2, 3},
PAN Youlin^{1, 2, 3},
HUANG Xing^{4
,
,}

1.
College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China
2.
Engineering Research Center of Big Data Intelligence, Ministry of Education, Fuzhou 350116, China
3.
Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 350116, China
4.
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The National Natural Science Foundation of China (62372109, 62572396), Fujian Science Fund for Distinguished Young Scholars (2023J06017)

Received Date: 2025-10-09
Accepted Date: 2025-12-22
Rev Recd Date: 2025-12-20

Available Online: 2026-01-04

Abstract

Abstract

Continuous-Flow Microfluidic Biochips (CFMBs) are widely applied in biomedical research because of miniaturization, high reliability, and low sample consumption. As integration density increases, design complexity significantly rises. Conventional stepwise design methods treat binding, scheduling, layout, and routing as separate stages, with limited information exchange across stages, which leads to reduced solution quality and extended design cycles. To address this limitation, a one-pass architectural synthesis method for CFMBs is proposed based on Deep Reinforcement Learning (DRL). Graph Convolutional Neural networks (GCNs) are used to extract state features, capturing structural characteristics of operations and their relationships. Proximal Policy Optimization (PPO), combined with the A* algorithm and list scheduling, ensures rational layout and routing while providing accurate information for operation scheduling. A multiobjective reward function is constructed by normalizing and weighting biochemical reaction time, total channel length, and valve count, enabling efficient exploration of the decision space through policy gradient updates. Experimental results show that the proposed method achieves a 2.1% reduction in biochemical reaction time, a 21.3% reduction in total channel length, and a 65.0% reduction in valve count on benchmark test cases, while maintaining feasibility for larger-scale chips. Objective CFMBs have gained sustained attention in biomedical applications because of miniaturization, high reliability, and low sample consumption. With increasing integration density, design complexity escalates substantially. Traditional stepwise design methods often yield suboptimal solutions, extended design cycles, and feasibility limitations for large-scale chips. To address these challenges, a one-pass architectural synthesis framework is proposed that integrates DRL to achieve coordinated optimization of binding, scheduling, layout, and routing. Methods All CFMB design tasks are integrated into a unified optimization framework formulated as a Markov decision process. The state space includes device binding information, device locations, operation priorities, and related parameters, whereas the action space adjusts device placement, operation-to-device binding, and operation priority. High-dimensional state features are extracted using GCNs. PPO is applied to iteratively update policies. The reward function accounts for biochemical reaction time, total flow-channel length, and the number of additional valves. These metrics are evaluated using the A* algorithm and list scheduling, normalized, and weighted to balance trade-offs among objectives. Results and Discussions Based on the current state and candidate actions, architectural solutions are generated iteratively through PPO-guided policy updates combined with the A* algorithm and list scheduling. The defined reward function enables the generation of CFMB architectures with improved overall quality. Experimental results show an average reduction of 2.1% in biochemical reaction time, an average reduction of 21.3% in total flow-channel length, with a maximum reduction of 57.1% in the ProteinSplit benchmark, and an average reduction of 65.0% in additional valve count compared with existing methods. These improvements reduce manufacturing cost and operational risk. Conclusions A one-pass architectural synthesis method for CFMBs based on DRL is proposed to address flow-layer design challenges. By applying GCN-based state feature extraction and PPO-based policy optimization, the multiobjective design problem is transformed into a sequential decision-making process that enables joint optimization of binding, scheduling, layout, and routing. Experimental results obtained from multiple benchmark test cases confirm improved performance in biochemical reaction completion time, total channel length, and valve count, while preserving scalability for larger chip designs.
- Continuous-Flow Microfluidic Biochips (CFMB),
- One-pass architectural synthesis,
- Deep Reinforcement Learning (DRL),
- Lab-on-a-chip

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References(16)

References

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