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Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

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Published:28 June 2021Publication History
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Abstract

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

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          • Published in

            cover image ACM Transactions on Design Automation of Electronic Systems
            ACM Transactions on Design Automation of Electronic Systems  Volume 26, Issue 6
            November 2021
            218 pages
            ISSN:1084-4309
            EISSN:1557-7309
            DOI:10.1145/3472284
            Issue’s Table of Contents

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            Publication History

            • Published: 28 June 2021
            • Accepted: 1 April 2021
            • Revised: 1 March 2021
            • Received: 1 September 2020
            Published in todaes Volume 26, Issue 6

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