Browsing by Author "Kuo, Chun-Hao"

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Co-placement optimization in sensor-reusable cyber-physical digital microfluidic biochips
(2019-01-01)
Li, Jian-De
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Kuo, Chun-Hao
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Lu, Guan-Ruei
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Wang, Sying-Jyan
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Li, Katherine Shu-Min
Digital microfluidic biochips (DMFBs) facilitate modern healthcare applications. One of the most important applications is point-of-care (POC) clinical diagnosis which is directly related to human health. However, biochemical experiments are usually error-prone, which makes monitoring intermediate results during bioassay execution be required to ensure the correctness of POC clinical diagnosis. To tackle this problem, cyber-physical digital microfluidic biochips with integrated sensors have been proposed and have attracted attention recently. In order to fully utilize the sensors for detection, the reusability of sensors should be taken into consideration during the module placement stage of cyber-physical DMFBs synthesis flow. Moreover, excessive actuation of electrodes may cause reliability degradation and this issuse should also be taken care of during module placement stage. In order to deal with the aforementioned problems, this paper presents the first co-optimization method for both module and sensor placement. Experimental results show that the proposed method can effectively minimize bioassay completion time while meeting all constraints from sensors and electrodes.
Flexible Droplet Routing in Active Matrix-Based Digital Microfluidic Biochips
(2018-04-01)
Lu, Guan-Ruei
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Kuo, Chun-Hao
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Chiang, Kuen-Cheng
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Banerjee, Ansuman
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Bhattacharya, Bhargab B.
The active matrix (AM)-based architecture offers many advantages over conventional digital electrowetting-on-dielectric (EWOD) microfluidic biochips, such as the capability of handling variable-size droplets, more flexible droplet movement, and precise control over droplet navigation. However, a major challenge in choosing the routing paths is to decide when the droplets are to be reshaped depending on the congestion of the intended path, or split- and route sub droplets,and merging them at their respective destinations. As the number of microelectrodes in AM-EWOD chips is large, the path selection problem becomes further complicated. In this article, we propose a negotiation-guided flow based on routing of subdroplets that obviates the explicit need for deciding when the droplets are to be manipulated, yet fully utilizing the power of droplet reshaping, splitting, and merging them to facilitate their journey. The proposed algorithm reduces routing cost and provides more freedom in deadlock avoidance in the presence of multiple routing tasks by assigning certain congestion penalty for sibling subdroplets and fluidic penalty for heterogeneous droplets. Compared to existing techniques, it reduces latest arrival time by an average of 29% for several benchmark and random test suites. Furthermore, our method is observed to provide 100% routability of nets for all test cases, whereas existing and baseline routers fail to produce feasible solutions in many instances. We also propose a reliable mode droplet routing strategy where the number of unreliable splitting operations can be reduced by paying a small penalty on latest arrival time.
Placement Optimization of Cyber-Physical Digital Microfluidic Biochips
(2016-01-01)
Kuo, Chun-Hao
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Lu, Guan-Ruei
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Ho, Tsung-Yi
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Chen, Hung-Ming
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Hu, Shiyan
Digital microfluidic biochips maximize the possibilities in modern healthcare applications, such as point-of-care (POC) tests and immunoassays. Because of the error-prone biochemical experiments, cyber-physical digital microfluidic biochips with sensor integration is essential. However, the correctness of bioassays is closely related to the actuating times of electrodes, which should be taken care of during the module placement stage. Moreover, sensor constraint in sensor planning should also be considered to avoid sensing errors. This paper presents the first module and sensor co-placement algorithm of cyberphysical digital microfluidic biochips while considering the sensor constraint and minimizing the actuating times of electrodes. The experimental results show that we can effectively minimize the bioassay completion time and satisfy the sensor constraint.