Architecture Krish Presntation

8/14/2019 Architecture Krish Presntation

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Architectural-Level Synthesis of

Digital Microfluidics-Based

Biochips

Fei Su & Krishnendu Chakrabarty

Electrical and Computer Engineering

Duke University


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Motivation

Application to clinical diagnostics, e.g., healthcare

for premature infant

Bio-smoke alarm to counter bioterrorism Massive parallel DNA analysis; automated drug

discovery

Conventional Biochemical

Analyzer

Shrink

Microfluidic

Lab-on-Chip

Microfluidics-Based

Biochip


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Microfluidics

Continuous-flow biochips: Permanently etched

microchannels, micropumps and microvalves

Digital microfluidic biochips: Manipulation ofliquids as discrete droplets (digital microfluidics)

(University of Michigan)

1998

(Duke University)

2002


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Integration of microfluidics: one of the systemIntegration of microfluidics: one of the system--

level design challenges (level design challenges ( 50nm/beyond 2009)50nm/beyond 2009)2003 International Technology Roadmap for

Semiconductors (ITRS)

Heterogeneous SOCs

-Mixed-signal

-Mixed-technology

Digital

blocks

Analog & RF

blocks

MEMS

components

Fluidic

components

CAD support needed for

biochip design


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Outline

Motivation

Background

Related prior work

Architectural-level synthesis for digitalmicrofluidic biochips Sequencing graph model

Mathematical programming model

Heuristics for scheduling problem

Simulation experiments

Conclusions


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Background

Novel microfluidic platform invented at Duke University

Droplet actuation is achieved through an effect called

electrowetting Electrical modulation of the solid-liquid interfacial tension

No PotentialA droplet on a hydrophobic

surface originally has a

large contact angle.

Applied PotentialThe droplets surface energy

increases, which results in a

reduced contact angle. The

droplet now wets the surface.


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Background (Cont.)

Actuation principle of digital microfluidics

Droplet Transport

A droplet can be transported by

removing a potential on the

current electrode, and applying a

potential to an adjacent electrode.


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Background (Cont.)

Digital microfluidics-based biochips

MIXERSMIXERSTRANSPORTTRANSPORT DISPENSINGDISPENSING REACTORSREACTORS DETECTIONDETECTION

Digital Microfluidic

Biochip

Basic microfluidic functions(transport, splitting, merging,

and mixing) have already been

demonstrated on a 2-D array

Digital microfluidics-based

biochip is a high reconfigurable

system

INTEGRATE


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Background (Cont.)

The in-vitro measurement of glucose in human

physiological fluids

O4HneQuinoneimiTOPSAAP-4O2H

OHAcidGluconicOOHGlucose

2

Peroxidase

22

22

OxidaseGlucose

22

+ ++

+ ++


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Background (Cont.) Digital microfluidics-based biochip used in glucose

measurementDispensing/Transportation:

Sample droplet (glucose) and reagent

droplet (glucose oxidase, peroxidase,

4-AAP and TOPS), are dispensed into

the microfluidic system from reservoirs.

Optical Detection:

Assay result (quinoneimine) is detected

by a green LED and a photodiode.

Mixing:

Sample droplet and reagent droplet are

mixed in a mixer (i.e. 2x2 array mixer).

Length of the control electrode

L = 1.5mm

Height between two platesH = 475nm

Thickness of insulator layer

(parylene C) = 800nm

Thickness of hydrophobic film(Teflon AF) = 60nm


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Background (Cont.) Detection of lactate, glutamate and pyruvate has also been

demonstrated.

Biochip used for multiplexed in-vitro diagnostics on human

physiological fluids

Fabricated microfluidic array usedin multiplexed biomedical assays.


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Synthesis Methodology

Full-custom design Top-down system-level design

Scheduling of operations

Binding to functional

resources Physical design


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Architectural-Level Synthesis

Sequencing graph model Multiplexed in-vitro diagnostics

Sample

Plasma: S1

Serum: S2

Saliva: S4

Urine : S3

Enzymatic Assay

Glucose Measurement

Reagent

Lactate Measurement

Pyruvate Measurement

Glutamate Measurement

R1

R2

R3

R4


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Sequencing Graph Model (Cont.)

Node representing the inputoperationSj

(Dispensing sample Sj,j=1,, m)

Ij+m

Rj

(Dispensing reagent Rj,j=1,, n)

Ij

Assumption 1: The timerequired to generate

and dispense droplets

from the reservoir isdetermined mainly by

the system hardware

parameters


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Node representing different types ofmixingoperations

M1

S1

(Mixing of sample S1 and

reagentRi, i=1, , n)

Ri

M2 Mm

S2 Ri Sm Ri

(Mixing of sample S2 and


(Mixing of sample Sm and


...

Assumption 2: The time required forcomplete mixing mainly depends

on the viscosity of the sample


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Node representing the detection operations

D1

Optical detection of

Assay 1, e.g.,

glucose assay

Si+R1

D2 Dn


Assay 2, e.g.,

lactate assay


Assay n, e.g.,

glutamate assay

...

Si+R2 Si+Rn

Assumption 3: The type

of enzymatic assay

determines the

optical detection time.


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Assumption 4: In contrast to the above operations,droplet movement on a digital microfluidic array is

very fast. We can ignore the droplet movement

time for scheduling assay operations.

Size view Top view


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Sequencing graph model of a

multiplexed bioassays

I1

S1

Inputoperations:

2mn Nodes

I1 Im Im Im+nIm+1 Im+1 Im+n

M1 M1 Mm Mm

D1 Dn D1 Dn

NOP

NOP

Mixing

operations:

mn Nodes

Detection

operations:

mn Nodes

12mn

2mn+1 3mn

4mn3mn+1

S1 Sm Sm R1 Rn R1 Rn

M1

D1

15

25

30

Storage unit is

required during

this time period

Time Step


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Mathematical Programming Model

First define a binary variable

=ijX

1 if operation vi starts at

time slotj.

0 otherwise

Starting time of operation vi :

=

=

T

jiji XjSt

1

Completion time of operation:

C= max {Sti + d(vi) : vi D1, ,Dn}

Objective function:

minimize C

Dependency constraints

Stj Sti + d(vi) if there is a dependency

between viand v

j Resource constraints

Reservoirs/dispensing ports

Nrreservoirs/dispensing ports assigned to

each type of fluid (Nr = 1)

: 1j T,11:

Iviij

i

X +

nmi IviijX

:

1

Reconfigurable mixers and

storage units

Nmixer(j) + 0.25Nmemory(j) Na 1 j T

Optical detectors

Nddetectors are assigned to each

bioassay (Nd = 1)

,11: )(

=

Dvi

j

vdjlij

i i

X = 1: )(1

ni iDvi

j

vdjlijX 1j T

Objective Constraints


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Mathematical Programming Model (Cont.)

Evaluated for a problem of the modest size: Plasma and serum are sampled and assayed for glucose,

lactate and pyruvate measurements; i.e., m = 2, n = 3

AssumeNr = Nd = 1, andNa = 4

NOP

d(Ii)=1

1

I1 I1 I1 I2 I2 I2 I3 I4 I5 I3 I4 I5

M1 M1 M1 M2 M2 M2

D1 D2 D3 D1 D2 D3

S1

2 3 4 5 6 7 8 9 10 11 12

13 14 15 16 17 18

19 20 21 22 23 24

d(M1)=5

d(M2)=3

d(D1)=5

d(D2)=4

d(D3)=6

S1 S1 S2 S2 S2 R1 R2 R3 R1 R2 R31

3

4

5

6

7

8

9

10

11

12

13

14

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17

1

2

6

5

7

104

8

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1415

16

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2122

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forS1

Reconfigurable

Mixers

Reservoirs Optical Detectors

forD1 forD2 forD3forS2 forR1 forR2 forR3

Time

step

2

3 9 13

Optimal schedule obtained by using integer linear programming

Completion time is 17 time-slots; i.e., 34 seconds.


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Heuristics for the Scheduling Problem

Heuristic algorithms

Modified List Schedulingalgorithm (M-LS)

Extend well-known List Schedulingalgorithm Modified to handle the reconfigurable resources

(i.e., mixer and storage units)

Heuristic based on a Genetic algorithm (GA)

Representation of chromosome (random keys)

Ad-hocschedule construction procedure Evolution strategy

Simulation Experiments


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Simulation Experiments Lower bound (LB)

LB = mmax{d(D1), d(Dn)}+min{d(M1), d(Mm)}+d(Ii)+1 Upper bound (UB)

UB = mmax{d(D1),d(Dn)}+kmax{d(M1),d(Mm)}+max(m, n)d(Ii)+ 1

Detecto

largest d

Mixer with

smallestdReservoirs

Min{d(M1),, d(Mm)}

1

(b)

Input operations:

Max duration =

max(m, n)

Phase I

Phase II

Phase III

NMix1+0.25(2mn-2NMix1)NaNMix12Na-mn

Worst case: 2mn storage units needed

Step 1: NMix1 mixing operations scheduled

Mixing operations:Max duration= kmax{d(M1),, d(Mm)}

Detection operations:Max duration

= mmax{d(D1),, d(Dn)}

Reservoirs

mMax{d(D1),

, d(Dn)}

(a)

NMix2+0.25NMix1+0.25(2mn-2NMix1-2NMix2)Na

NMix22Na-mn+0.5NMix1

Step 2: NMix2 mixing operations scheduled

Step k: NMixkmixing operations scheduled

r with

Simulation Experiments (Cont )


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Simulation Experiments (Cont.)

Five examples (four samples) S1: Plasma, S2: Serum, S3:

Urine, S4: Saliva, Assay1: Glucose assay, Assay2: Lactate assay,Assay3: Pyruvate assay, Assay4: Glutamate assay

S1, S2, S3 and S4 are assayed for

Assay1, Assay2, Assay3 andAssay4.

Example 5

(Nr=Nd=1,Na=9) m=4, n=4

S1, S2, and S3 are assayed forAssay1, Assay2, Assay3 andAssay4.

Example 4(Nr=Nd=1,Na=7) m=3, n=4

S1, S2, and S3 are assayed forAssay1, Assay2, and Assay3.

Example 3

(Nr=Nd=1,Na=5) m=3, n=3

S1, and S2 are assayed for

Assay1, Assay2, and Assay3.

Example 2

(Nr=Nd=1,Na=4) m=2, n=3

S1 and S2 are assayed forAssay1 and Assay2.

Example 1(Nr=Nd=1,Na=3) m=2, n=2

DescriptionExample

Simulation Experiments (Cont )


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Simulation Experiments (Cont.)

Simulation results

34355929N/AExample 5

26274323N/AExample 4

25264723N/AExample 31719251717Example 2

1517231515Example 1

GAM-LSUBLBOptExample

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5

Example

RatioofHeuristic/LowerBou

GA/LB

M-LS/LB


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Conclusions

A system design methodology to apply classicalarchitectural-level synthesis techniques to digitalmicrofluidics-based biochips

An optimal strategy based on integer linearprogramming for scheduling assay operationsunder resource constraints

Two heuristic techniques that scale well for largeproblem instances M-LS: computationally more efficient

GA: yields lower completion times for bioassays

A clinical diagnostic procedure used to evaluate

the proposed methodology

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