Browsing by Author "Lei, Yung-Chun"

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Chain-Based Pin Count Minimization for General-Purpose Digital Microfluidic Biochips
(2016)
Lei, Yung-Chun
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Hsu, Chen-Shing
;
Huang, Juinn-Dar
;
Jou, Jing-Yang
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電子工程學系及電子研究所
Minimizing the number of external control pins is one of the most important optimization objectives in digital microfluidic biochip (DMFB) designs especially as the chip size gets even bigger. So far, only few works focus on this issue for general-purpose DMFBs. In this paper, we present a pin count minimization algorithm based on sophisticated electrode chaining on regular or irregular electrode arrays. The key idea of the proposed method is that actuation information can be implied from previous neighborhood electrodes to later ones throughout a chain. Experimental results show that the pin count reduction can be near 50% in large DMFBs.
Forecast-Based Sample Preparation Algorithm for Unbalanced Splitting Correction on DMFBs
(2019-01-01)
Song, Ling-Yen
;
Chen, Yi-Ling
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Lei, Yung-Chun
;
Huang, Juinn-Dar
;
電子工程學系及電子研究所
Sample preparation is regarded as one of essential processing steps in most biochemical assays. In the past decade, numerous techniques have been presented to deal with sample preparation under the (1:1) mixing model on digital microfluidic biochips (DMFBs) for various optimization goals. However, most of previous works assumed that mixing-then-splitting would get two identical output droplets, which is not always true due to unbalanced splitting. As a consequence, those works may fail to provide correct solutions at the presence of unbalanced splitting. Several methods have been proposed to deal with this issue. Nevertheless, some of them rely on hypotheses that may not be practical, while the others demand extra reactants or special hardware. In this paper, we propose a new probability-based sample preparation algorithm for unbalanced splitting correction. Our new algorithm not only guarantees a correct solution, but requires neither extra reactants nor on-chip special hardware. Experimental results show that the effect of unbalanced splitting can be eliminated only at the cost of 20% more operation steps. That is, the proposed algorithm is both reliable and efficient.
Multi-Objective Sample Preparation Algorithm for Microfluidic Biochips Supporting Various Mixing Models
(2016-01-01)
Lei, Yung-Chun
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Lin, Tung-Hsuan
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Huang, Juinn-Dar
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電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Sample preparation is one of the most crucial processes in most biochemical applications. Reagents are repeatedly diluted in an appropriate sequence to get a target solution with a specific concentration value. For flow-based microfluidic biochips (FMFBs), several research works have been proposed for reactant minimization. In this paper, we propose the first sample preparation algorithm for microfluidic biochips with various mixing models that can perform multi-objective optimization simultaneously. It first formulates the problem in a network-flow model and then solves it through integer linear programming (ILP). Experimental results show that the proposed method can provide better solutions (in terms of reactant, waste, and operation jointly) as compared with the prior art.
Multi-Target Many-Reactant Sample Preparation for Reactant Minimization on Microfluidic Biochips
(2018-01-01)
Lei, Yung-Chun
;
Lin, Tien-Kuo
;
Huang, Juinn-Dar
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Sample preparation is one of essential steps in biochemical applications. It produces solutions with target concentrations through mixing various reactants in a specific way. In this paper, we propose a reactant cost minimization technique, M2SPA, for multi-target many-reactant sample preparation on microfluidic biochips through maximally sharing identical intermediate solutions among different targets. M2SPA first represents target concentrations as a recipe cube, searches all feasible candidates for intermediate solution sharing among targets, and then selects the one with the best cost saving for action. Experimental results show that the proposed algorithm can reduce up to 15.7% of reactant cost as compared to a state-of-the-art method.
Reactant Cost Minimization through Target Concentration Selection on Microfluidic Biochips
(2016-01-01)
Lei, Yung-Chun
;
Chen, Yi-Ling
;
Huang, Juinn-Dar
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Nowadays, lab-on-a-chip is one of the most sophisticated technologies, while sample preparation, one of the essential steps in bioassays, is the process that mixes buffer and reactant to achieve the desired target concentration. Since the reactant consumption generally takes a notable part of the total cost in a bioassay, minimizing its usage is generally preferable. Existing techniques only consider one fixed target concentration, whereas a range of values is acceptable in real applications. In this paper, we propose two algorithms that can select a feasible concentration value within a given range for cost minimization. Experimental results show that the proposed methods can further reduce the reactant cost up to 20%.
Time-Constrained Sample Preparation Algorithm for Reactant Minimization on Digital Microfluidic Biochips
(2019-01-01)
Song, Ling-Yen
;
Li, Yu-Ying
;
Lei, Yung-Chun
;
Huang, Juinn-Dar
;
電子工程學系及電子研究所
Sample preparation is one of the essential processes for most biochemical assays on biochips. Many studies have been conducted for dealing with the reactant minimization problem during sample preparation. Nevertheless, those approaches try to minimize reactant consumption at the cost of more extra operations, which may lead to deterioration of reactant and even wrong results. In this paper, we propose a time-constrained sample preparation algorithm for reactant minimization on digital microfluidic biochips (DMFBs). Starting from a given reactant-minimized solution, the proposed algorithm tries to incrementally reduce the operation count at the cost of more reactant consumption to meet the given timing constraint. The experimental results show that our algorithm achieves a 33% reactant reduction over a delay-optimal method with the same operation time. Meanwhile, the proposed method can save 5% operation count as compared with a state-of-the-art reactant minimization algorithm under the same reactant consumption.