Hardware Reference
In-Depth Information
1.3
Outline of the Topic
This topic addresses a number of optimization problems related to cyberphysical
microfluidic biochips. The reminder of the topic is organized as follows.
Chapter
2
presents a transformative cyberphysical approach towards achieving
closed-loop and sensor feedback-driven biochip operation under the program
control. Section
2.1
presents the motivation for developing a “physical-aware”
system reconfiguration technique that uses sensor data at intermediate checkpoints
to dynamically reconfigure the biochip. Section
2.2
develops an algorithm for the
measurement and tracking of droplets based on real-time imaging data from a
charge-coupled device (CCD) camera. Section
2.3
introduces a reliability-driven
error recovery strategy. Section
2.4
presents the parallel recombinative simulated
annealing (PRSA)-based and greedy algorithms for reliability-driven synthesis. In
Sect.
2.5
, simulation results for three representative bioassays are discussed. Finally,
conclusions are drawn in Sect.
2.6
.
A hardware-assisted error-recovery method that relies on an error dictionary
for rapid error recovery is presented in Chap.
3
. The motivation for the hardware-
assisted cyberphysical biochip is introduced in Sect.
3.1
. The proposed algorithm for
creation of the error dictionary is presented in Sect.
3.2
. Section
3.3
introduces the
generation of actuation matrices corresponding to synthesis solutions in the error
dictionary. Section
3.4
illustrates that actuation matrices of bioassays are sparse.
Based on this conclusion, Sect.
3.5
further discusses the procedures for compaction
of the error dictionary. The implementation of dictionary-based error recovery on
FPGA is introduced in Sect.
3.6
. A fault simulation method with consideration of
parameter variations in the fabrication process is discussed in Sect.
3.7
. Simulation
results are shown in Sect.
3.8
, and conclusions are presented in Sect.
3.9
.
Chapter
4
describes the synthesis algorithm for bioassays under completion-time
uncertainties in fluidic operations. Section
4.1
discusses the drawbacks of previous
uncertainty-oblivious methods of biochip design. Section
4.2
presents the design
of microfluidic biochips with multiple clock frequencies. Section
4.3
introduces the
framework of operation-dependency-aware synthesis. Based on results derived from
the proposed synthesis algorithm, integrated on-line decision-making for droplet
transportation path is presented in Sect.
4.4
. Simulation results are presented in
Sect.
4.5
. Section
4.6
concludes the chapter.
Chapter
5
presents the optimization algorithms for PCR on a cyberphysical
digital microfluidic biochip. The working principle of the PCR biochip is introduced
in Sect.
5.1
. Section
5.2
describes the statistical model for the amplification of
DNA strands and the on-line decision making method during an actual experiment.
Section
5.3
presents the device placement algorithm with the consideration of device
interferences. Section
5.4
describes an application-specific reservoir allocation
method. The consideration of droplet visibility during operation scheduling is
discussed in Sect.
5.5
. Simulation results for three widely used bioassays are
presented in Sect.
5.6
. Section
5.7
concludes the chapter.
