Pin-Constrained Chip Design - Digital Microfluidic Biochips - page 67

TAble 3.3
Random Synthetic Benchmarks, Sample Size = 1000

DIP Array Size NSR

0.1 25 × 25 0.31

0.1 50 × 50 0.24

0.1 75 × 75 0.19

0.15 25 × 25 0.28

0.15 50 × 50 0.20

0.15 75 × 75 0.14

of size N × N , ( N = 25, 50, 75) are considered here. For each array, we con-
sider 1000 simulated droplet-movement plans. Each droplet-movement plan
is defined by a starting snapshot and destination snapshot. The starting
snapshot is generated by injecting a droplet in the array with probability k ,
referred to as the droplet injection probability (DIP). A check is incorporated in
the generation process to avoid the violation of fluidic constraints. Results
derived from this process can be viewed as snapshots of droplets moving
around the chip. Each droplet-movement plan is provided as input to the
grouping-based method and the number of steps required for droplet move-
ment is calculated. One-at-a-time droplet movement is also considered a
baseline, and the results are recorded for the purpose of comparison.
To evaluate the proposed method, we introduce the parameter “number-
of-steps-ratio” (NSR), defined by the equation NSR = N p / N o , where N p ( N o )
is the number of movement steps for the grouping-based method (one-at-a-
time baseline method). Small values of NSR are clearly desirable. We calcu-
late the NSR values for different array sizes and the results (see Table 3.3) .
As shown Table 3.3, regardless of DIP value, the NSR decreases with array
size. This shows that the grouping-based method is more efficient for con-
current droplet manipulation on large-scale digital microfluidic arrays. For a
given array size, the proposed method achieves lower NSR values for higher
values of DIP. Thus, we see that compared to the one-at-a-time scheme, drop-
lets can be manipulated more efficiently for high-throughput biochips with
higher concurrency in biochip operations.

3.2.5.2 A Multiplexed Bioassay Example

Next, we evaluate the proposed scheduling and grouping-based droplet-
manipulation methods by using them to implement the routing plan for a
set of real-life bioassays, namely, multiplexed in-vitro diagnostics on human
physiological fluids.
As a typical example of multiplexed and concurrent assays, three types
of human physiological fluids, urine, serum, and plasma, are sampled and
dispensed into the digital microfluidic biochip, and glucose and lactate mea-
surements are performed for each type of physiological fluid. The assay