Synthesis of Digital Microfluidic Biochips with Reconfigurable Operation Execution

Synthesis of Digital Microfluidic Biochips

with Reconfigurable Operation Execution

Elena MafteiTechnical University of Denmark

DTU Informatics

www.dreamstime.com

2Duke University

Digital Microfluidic Biochip

3

Applications

Sampling and real time testing of air/water for biochemical toxins Food testing DNA analysis and sequencing Clinical diagnosis Point of care devices Drug development

4

Challenges: Design complexity Radically different design and test methods required Integration with microelectronic components in future SoCs

Advantages & Challenges

Advantages: High throughput (reduced sample / reagent consumption) Space (miniaturization) Time (parallelism) Automation (minimal human intervention)

5

Outline

Motivation Architecture Operation Execution Contribution I

Module-Based Synthesis with Dynamic Virtual Devices

Contribution II Routing-Based Synthesis

Contribution III Droplet-Aware Module-Based Synthesis

Conclusions & Future Directions

6

Architecture and Working Principles

Biochip architecture Cell architecture

Electrowetting-on-dielectric

Droplet

Top plate

Ground electrode

Control electrodes

InsulatorsFiller fluid

Bottom plate

S2

R

2

B

S3

S1 W

R

1

Reservoir

Detector

7

S2

R

2

B

S3

S1 W

R

1

Dispensing Detection Splitting/Merging Storage Mixing/Dilution

Microfluidic Operations

8

Reconfigurability

S2

R

2

B

S3

S1 W

R

1


9

Reconfigurability

S2

R

2

B

S3

S1 W

R

1


Non-reconfigurable

10

Reconfigurability

S2

R

2

B

S3

S1 W

R

1

Dispensing Detection

Splitting/Merging Storage Mixing/Dilution

Non-reconfigurable

Reconfigurable

11

Module-Based Operation Execution

S2

R

2

B

S3

S1 W

R

1

2 x 4 module

12


S2

R

2

B

S3

S1 W

R

1

Operation

Area (cells)

Time (s)

Mix 2 x 4 3

Mix 2 x 2 4

Dilution 2 x 4 4

Dilution 2 x 2 5Module library

2 x 4 module

13


S2

R

2

B

S3

S1 W

R

1

2 x 4 module

segregation cells

14


S2

R

2

B

S3

S1 W

R

1

Operations confined to rectangular, fixed modules

Positions of droplets inside modules ignored

Segregation cells

15

Module-Based Synthesis with Dynamic Virtual Modules

16

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D1

tApplication graph

1 2

9

12

8

3 4

5 6

711

13

10

In S1

In B In S2 In R

1

Dilute Mix

In S3

In B

Dilute

In BIn R2

Dilute

17

Example

Biochip

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D1

Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

18

Example

t

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D1

D2(O

5)

Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

19

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D3

(O12

)

M1

(O6)

D4

(O13

)

store O5

t+4Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

20

Example

Application graph

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D3

(O12

)

D4

(O13

)

M2(

O7)

t+8

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

21

Example

Schedule

Mixer1

Mixer2

Diluter2

Diluter3

Diluter4

O5

O6

O12

O13

O7

store

t t+8 t+11Allocation

Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

22

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

D1

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

23

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

24

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

25

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

26

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

27

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

28

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

29

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

30

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

31

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

32

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

33

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

D1

tApplication graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

34

Example

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

D2(O

5)

D1

t

M1

(O6)

Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

35

Example

t+4

D3

(O12

)

S

1

S

2

S

3

B

1

R

2

R

1

WB

2

M2 (O

7)

D4

(O13

)

Application graph

2 x 22 x 2

2 x 4

2 x 22 x 4

S1

B1

S2 R

1

R2 B

2 B

1 S

3

1 2

9

12

8

3 4

5 6

711

13

10

36

Example

Schedule – operation execution with dynamic virtual modules

O6Mixer

1

Mixer2

Diluter2

Diluter3

Diluter4

O5

t t+9

O12

O13

O7

t+4Allocation

Mixer1

Mixer2

Diluter2

Diluter3

Diluter4

O5

O6

O12

O13

O7

store

t t+8 t+11Allocation

Schedule – operation execution with fixed virtual modules

37

Solution

Tabu Search

List Scheduling

Maximal Empty Rectangles

Binding of modules to operations

Schedule of the operations Placement of modules performed inside scheduling

Placement of the modules Free space manager based on [Bazargan et al. 2000]

thatdivides free space on the chip into overlapping rectangles

38

Dynamic Placement Algorithm

S

1

S

2

S

3

B

1

R

1

R

2

WB

2

Rect2

Rec

t 1 Rec

t 3

(0,0) (7,0)

(0,4)

(3,8) (8,8)

D1

39


S

1

S

2

S

3

B

1

R

1

R

2

WB

2

D1

Rect2

Rec

t 1 Rec

t 3

(0,0)

D2(O

5)

(6,4)

(3,4)

(8,8)

(7,0)

40


Rect2

(4,0)

Rect1

(8,4)

(6,0)

S

1

S

2

S

3

B

1

R

1

R

2

WB

2

D2(O

5)

D1

(8,8)

41

Experimental Evaluation

Tabu Search-based algorithm implemented in Java Benchmarks

Real-life applications Colorimetric protein assay In-vitro diagnosis Polymerase chain reaction – mixing stage

Synthetic benchmarks 10 TGFF-generated benchmarks with 10 to 100

operations

Comparison between: Module-based synthesis with fixed modules (MBS) T-Tree [Yuh et al. 2007] Module-based synthesis with dynamic modules (DMBS)

42

Experimental EvaluationBest-, average schedule length and standard

deviation out of 50 runs for MBS

Area Time limit (min)

Best (s) Average (s)

Standard dev. (%)

13 x 13 60 182 189.99 2.90

10 182 192.00 3.64

1 191 199.20 4.70

12 x 12 60 182 190.86 3.20

10 185 197.73 6.50

1 193 212.62 10.97

60 184 192.50 3.78

10 194 211.72 14.37

1 226 252.19 15.76

Colorimetric protein assay

Colorimetric protein assayColorimetric protein assayColorimetric protein assay

43


Colorimetric protein assayColorimetric protein assay

Best schedule length out of 50 runs for MBS vs. T-Tree

10 x 10 10 x 9 9 x 90

50

100

150

200

250

300

MBS

T-Tree

Area (cells x cells)

Be

st s

che

du

le le

ng

th (

s)Colorimetric protein assayColorimetric protein assayColorimetric protein assay

Colorimetric protein assayColorimetric protein assay22.91 % improvement for 9 x 9

44

Experimental EvaluationAverage schedule length out of 50 runs for DMBS vs. MBS



13 x 13 12 x 12 11 x 120

50

100

150

200

250

DMBS

MBS


Ave

rag

e s

che

du

le le

ng

th (

s)

45

Routing-Based Operation Execution

46

Module-Based vs. Routing-Based Operation Execution

S

2

R

2

B

S

3

S

1

W

R

1

S

2

R

2

B

S

3

S

1

W

R

1

c

2

c

1

47

Operation Execution Characterization

S

2

R

2

B

S

3

S

1

W

R

1

c

2

90◦90◦

0◦

180◦c

1

p90, p180, p0 ?

48


Type Area (cells)

Time(s)

Mix/Dlt 2 x 4 2.9

Mix/Dlt 1 x 4 4.6

Mix/Dlt 2 x 3 6.1

Mix/Dlt 2 x 2 9.9

Input - 2

Detect 1 x 1 30

0◦ 0◦

0◦0◦ 90◦90◦

90◦

90◦

0◦

0◦ 90◦90◦

90◦

90◦

0◦180◦ 0◦

180◦0◦0◦

90◦ 90◦90◦

90◦

p90, p180, p0

Electrode pitch size = 1.5 mm, gap spacing = 0.3 mm, average velocity rate = 20 cm/s.

49


Type Area (cells)

Time(s)

Mix/Dlt 2 x 4 2.9

Mix/Dlt 1 x 4 4.6

Mix/Dlt 2 x 3 6.1

Mix/Dlt 2 x 2 9.9

Input - 2

Detect 1 x 1 30

p90 = 0.1 %

p180 = - 0.5 %

p0 = 0.29 %

p0 = 0.58 %

Electrode pitch size = 1.5 mm, gap spacing = 0.3 mm, average velocity rate = 20 cm/s.

1

2

50

Example

1 2

7

10

4In S

1In R

1

Mix

Mix

In R2

3In S

2

8Mix

Dilute 9

5 6In S

3In B

11

13 Waste

In R1 1

2Mix

Application graph

Biochip

S

2

R

2

B

R

1S

1

W

S

3

51

Example

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

S

2

R

2

B

R

1S

1

W

S

3

M2(

O7)

M1(

O8)

t = 2.04 s

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

52

Example

t = 6.67 s

S

2

R

2

B

R

1S

1

W

S

3

M3(

O10

)

D1(

O9)

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

53

Example

t = 9.5 s

S

2

R

2

B

R

1S

1

W

S

3

M4(

O12

)

M3(

O10

)Application

graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

54

Example

Mixer1

Mixer3

Mixer2

O8

O9

2.04 12.506.67

O12

O10

Diluter1

Mixer4

O7

ScheduleApplication graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

55

Example

t = 2.03 s

S

2

R

2

R

1S

1

B

W

83

4

4

4

9

6

6

6

5

1 1 1 227

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

56

Example

t = 4.20 s

S

2

R

2

S

1

W

S

3

R

1

B

13.58 times

7

8

9

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

57

Example

t = 4.28 s

R

2

WS

1

R

1

S

3

B

S

2

8

7

8

7

8810

7

7

7

e913 12e

13 13

13 13 13

13

13 11

11

11

11

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

58

Example

t = 6.34 s

R

2

WS

1

R

1

S

3

B

S

2

10.33 times

10

12

Application graph

1 2

7

10

43

8 9

5 6

11

13

12

1 x 41 x 4 2 x 4

2 x 41 x 4 W

R1

R2 S

3 B S

2 R

1 S

1

59

Example

Schedule – module-based operation execution

Schedule – routing-based operation execution

Mixer1

Mixer3

Mixer2

O8

O9

2.04 12.506.67

O12

O10

Diluter1

Mixer4

O7

2.03 6.354.20

O10

O7

O8

O9

O12

60

Solution

1 2

7

10

4In S

1In R

1

Mix

Mix

In R2

Source

3In S

2

8Mix

Dilute

Sink

9

5 6In S

3In B

11

13 Waste

In R1 1

2Mix

Merge

Mix

61

Solution

1 2

7

10

4In S

1In R

1

Mix

Mix

In R2

Source

3In S

2

8Mix

Dilute

Sink

9

5 6In S

3In B

11

13 Waste

In R1 1

2Mix

Merge

Mix Minimize the completiontime for the operation

Minimize the time until the droplets meet

62

Solution

Greedy Randomized Adaptive Search Procedure (GRASP)

S

2

R

2

S

3

R

1S

1

B

W

3

3

3

2

1

63

Solution


S

2

R

2

S

3

R

1S

1

B

W

3

3

3

2

1

For each droplet: Determine possible moves Evaluate each move

Merge: minimize Manhattan distance

Mix: maximize operation execution

Make a list of the best N moves Perform a random move from N

64

Solution


S

2

R

2

S

3

R

1S

1

B

W

3

3

3

2

1





65

Solution


S

2

R

2

S

3

R

1S

1

B

W

3

3

3

2

1 1





66

Solution


S

2

R

2

S

3

R

1S

1

B

W

3

3

3

2

1 1

3





67


GRASP-based algorithm implemented in Java Benchmarks

Real-life applications Colorimetric protein assay

Synthetic benchmarks 10 TGFF-generated benchmarks with 10 to 100

operations

Comparison between: Routing-based synthesis (RBS) Module-based synthesis with fixed modules (MBS)

68



44.95% improvement for 10 x 10


Average schedule length out of 50 runs for RBS vs. MBS

Colorimetric protein assay

11 x 11 11 x 10 10 x 10 0

50

100

150

200

250

RBS

MBS


Ave

rag

e s

che

du

le le

ng

th (



69

Routing-Based Operation Execution - Conclusions

Improved completion time compared to module-based synthesis

Challenge: contamination

70




S

1 R

1

W

S

3

B

1

B

2

S

2

R

3

R

21

residues

71




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

72




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

73




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

74




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

Partition1

Partition2

w2

75




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

Partition1

Partition2

w2

S

1 R

1

S

3

B

1

B

2

S

2

R

2

1

WR

3

76




S

1 R

1

S

3

B

1

B

2

S

2

R

2

WR

3

1

w1

Partition1

Partition2

w2

S

1 R

1

S

3

B

1

B

2

S

2

R

2

1

WR

3

w2

w1

77

Droplet-Aware Operation Executionwithout Contamination

78

Example

Biochip

1 2

9

12

In S1

In B

8In S

3

3 4 In R1

5 6

7

In B

11

13

10In R

2

Dilute Dilute

MixDilute

In S2

Mix

In B

Application graph

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

79

Example

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

Application graph

Biochip

B1

R1

80

Example

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

(O6)D1

(O5)

t = 2 sApplication graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

81

Example

t = 4.9 s

D3(O13)

D2(O12)M2 (

O7)

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

Application graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

82

Example

Diluter1

O5

O12

2 114.9

O7

O6

O13

Diluter2

Diluter3

Mixer1

Mixer2

ScheduleApplication graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

83

Example

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

(O6)D1

(O5)

t = 2 sApplication graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

84

Example

t = 2 s

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1(O6)D1(O5)

65

Application graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

85

Example

t = 4.17 s

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

D3(O13)

M2(O7)D2(O12)

7 12

13

Application graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

86

Example

Schedule

Diluter1

O5

O

12

2 4.17

O7

O6

O

13

Diluter2

Diluter3

Mixer1

Mixer2

6.67

Application graph

1 2

9

12

In S1

8In S

3

3 4 In R1

5 6

711

13

10

Dilute Dilute

MixDilute

In S2

Mix

2 x 42 x 4

1 x 4

R2

B2

2 x 32 x 3

S3

B1

S1

S2

B1

R1

87

Example

Schedule – module-based operation execution

Schedule – droplet-aware operation

execution

Diluter1

O5

O

12

2 4.17

O7

O6

O

13

Diluter2

Diluter3

Mixer1

Mixer2

6.67

Diluter1

O5

O12

2 114.9

O7

O6

O13

Diluter2

Diluter3

Mixer1

Mixer2

88

Solution

Location of modules determined using Tabu Search

Greedy movement of droplets inside modules Routing of droplets between modules and

between modules and I/O ports determined using GRASP

89

Droplet-Aware Operation Execution

S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

M2(O

7)

D3(O

13)

D2(O

12)

90


S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

13 13 13 13

12

12

12 12

777

7

91


S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

13 13 13 13

12

12

12 12

777

7

92


S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

13 13 13 13

12

12

12 12

7

777

7

93


Algorithm implemented in Java Benchmarks

Real-life applications In-vitro diagnosis Colorimetric protein assay

Synthetic benchmarks 3 TGFF-generated benchmarks with 20, 40, 60

operations

Comparison between: Droplet-aware module-based synthesis (DAS) Module-based synthesis (MBS)

94



Colorimetric protein assayColorimetric protein assayColorimetric protein assayColorimetric protein assay

Average schedule length out of 50 runs for DAS vs. MBS


15 x 15 14 x 14 13 x 130

20

40

60

80

100

120

140

DAS

MBS


Ave

rag

e s

che

du

le le

ng

th (

s)



95


Algorithm implemented in Java Benchmarks

Real-life applications Colorimetric protein assay

Synthetic benchmarks 3 TGFF-generated benchmarks with 20, 40, 60

operations

Comparison between: Droplet-aware module-based synthesis (DASC) Routing-based synthesis (RBSC)with contamination avoidance

96




Average schedule length out of 50 runs for DASC vs. RBSC

15 x 15 14 x 14 13 x 13 0

50

100

150

200

250

DASC

RBSC


Ave

rag

e s

che

du

le le

ng

th (

s)



97

Contributions

Tabu Search-based algorithm for the module-based synthesis with fixed devices [CASES09]

Module-based synthesis with virtual devices [CASES09] Module-based synthesis with non-rectangular virtual

devices [DAEM10] Analytical method for operation execution

characterization [CASES10] Routing-based synthesis [CASES10] + contamination

[DAEM, submitted] Droplet-aware module based synthesis [JETC,

submitted] ILP formulation for the synthesis of digital biochips

[VLSI-SoC08]

98

Conclusions

Proposed several synthesis techniques for DMBs Considered the reconfigurability characteristic of

DMBs Shown that by considering reconfigurability during

operation execution improvements in the completion time of applications can be obtained

99

Future Directions

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

M2

100

Future Directions

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

M2

M1

M2

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

Module-Based Synthesis with Overlapping Devices

101

Future Directions

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

Fault-Tolerant Module-Based Synthesis

M1

(O1)

M2

(O2) Faulty cell

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

(O1)

M2

(O2)

M3(

O3)

102

Future Directions

S

1

R

2

W

S

3

B

1

S

2

R

1

B

2

Fault-Tolerant Module-Based Synthesis

Faulty cellS

1

R

2

W

S

3

B

1

S

2

R

1

B

2

M1

(O1)

M2

(O2)

M3(

O3)

M2

(O2)

M1

(O1) M

3(O

3)

104

Back-up slides

105

Electrowetting

106

Surface Tension

Imbalance of forces between moleculesat an interface (gas/liquid, liquid/liquid,gas/solid, liquid/solid)

107

Dispensing

108

Dispensing

109

Dispensing

110

Splitting

111

Mixing

112

Capacitive sensor

113

Design Tasks

Operation Area(cells) Time(s)

Mix 2 x 2 10

Mix 1 x 3 5

Dilute 1 x 3 8

Dilute 2 x 5 3

114

Design Tasks


Mix 2 x 2 10

Mix 1 x 3 5

Dilute 1 x 3 8

Dilute 2 x 5 3

S

1

S

2

S

3

B

R

1

R

2

115

Experimental EvaluationQuality of the solution compared to classical operation execution

Best out of 50

Colorimetric protein assayColorimetric protein assay9.73% improvement for 11 x 12


13 x 13 12 x 12 11 x 120

50

100

150

200

250

DMBS vs. FMBS

DMBS

FMBS


Be

st s

che

du

le le

ng

th (

s)

116


Best out of 50

11 x 11 11 x 10 10 x 10 0

50

100

150

200

250

RBS vs. MBS

RBS

MBS


Be

st s

che

du

le le

ng

th (



117


Best out of 50



15 x 15 14 x 14 13 x 130

20

40

60

80

100

120

140

DAS vs. MBS

DAS

MBS


Be

st s

che

du

le le

ng

th (

s)

118

Future Directions

S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

3

2

1

Pin-Constrained Routing-Based Synthesis

119

Future Directions

S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

3

2

1


120

Future Directions

S

1

R

2

W

S

3

B

1

S

2

B

2

R

1

3

2

1


121


S2

R

2

B

S3

S1 W

R

1

Dispensing

Dispensing

122


S2

R

2

B

S3

S1 W

R

1

Dispensing Detection

123

S2

R

2

B

S3

S1 W

R

1

124

S2

R

2

B

S3

S1 W

R

1

Dispensing Detection Splitting/Merging


125

S2

R

2

B

S3

S1 W

R

1

Dispensing Detection Splitting/Merging Storage


126

Motivational Example (for the first contrib)

Application graph


Mix 2 x 4 3

Mix 2 x 2 4

Dilution 2 x 4 4

Dilution 2 x 2 5

Dispense - 2

Module library

1 2

9

12

In S1

In B

8In S

3

3 4 In R1

5 6

7

In B

11

13

10In R

2

Dilute Dilute

MixDilute

In S2

Mix

In B

127

Motivational Example(for the 2nd contrib)

1 2

7

10

4In S

1In R

1

Mix

Mix

In R2

Source

3In S

2

8Mix

Dilute

Sink

9

5 6In S

3In B

11

13 Waste

In R1 1

2Mix

Application graph

Type Area (cells)

Time(s)

Mix/Dlt 2 x 4 2.9

Mix/Dlt 1 x 4 4.6

Mix/Dlt 2 x 3 6.1

Mix/Dlt 2 x 2 9.9

Input - 2

Detect 1 x 1 30

Module library

128

Example(for the 3rd contrib)

Type Area (cells)

Time(s)

Mix/Dlt 2 x 4 2.9

Mix/Dlt 1 x 4 4.6

Mix/Dlt 2 x 3 6.1

Mix/Dlt 2 x 2 9.9

Input - 2

Detect 1 x 1 30

Module libraryApplication graph

1 2

9

12

In S1

In B

8In S

3

3 4 In R1

5 6

7

In B

11

13

10In R

2

Dilute Dilute

MixDilute

In S2

Mix

In B

Synthesis of Digital Microfluidic Biochips with Reconfigurable Operation Execution

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Transcript of Synthesis of Digital Microfluidic Biochips with Reconfigurable Operation Execution