Testing and Diagnosis of Digital Microfluidic Biochips using Multiple Droplets

2021 ◽  
Author(s):  
Sourav Ghosh ◽  
Surajit Kumar Roy ◽  
Chandan Giri
Keyword(s):  
Microfluidic Biochips ◽  
Digital Microfluidic

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

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.

Integrated Control-Fluidic Codesign Methodology for Paper-Based Digital Microfluidic Biochips

2020 ◽  
Vol 39 (3) ◽  
pp. 613-625 ◽  
Author(s):  
Qin Wang ◽  
Weiqing Ji ◽  
Zeyan Li ◽  
Haena Cheong ◽  
Oh-Sun Kwon ◽  
...  
Keyword(s):  
Integrated Control ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

Optimal Testing of Digital Microfluidic Biochips

2011 ◽  
Vol 23 (4) ◽  
pp. 518-529 ◽  
Author(s):  
Bogdan Paşaniuc ◽  
Robert Garfinkel ◽  
Ion Măndoiu ◽  
Alex Zelikovsky
Keyword(s):  
Microfluidic Biochips ◽  
Digital Microfluidic

Multiple fault diagnosis in digital microfluidic biochips

2006 ◽  
Vol 2 (4) ◽  
pp. 262-276 ◽  
Author(s):  
Daniel Davids ◽  
Siddhartha Datta ◽  
Arindam Mukherjee ◽  
Bharat Joshi ◽  
Arun Ravindran
Keyword(s):  
Fault Diagnosis ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

Design Automation and Testing of MEDA-Based Digital Microfluidic Biochips: A Brief Survey

2018 ◽  
Vol 66 (5) ◽  
pp. 662-676
Author(s):  
Pampa Howladar ◽  
Pranab Roy ◽  
Hafizur Rahaman
Keyword(s):  
Design Automation ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

scholarly journals Resource-Constrained Scheduling for Digital Microfluidic Biochips

2018 ◽  
Vol 14 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Kenneth O'neal ◽  
Daniel Grissom ◽  
Philip Brisk
Keyword(s):  
Resource Constrained ◽  
Microfluidic Biochips ◽  
Digital Microfluidic
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