Paul Pop, Elena Maftei , Jan Madsen Technical University of Denmark

1. Recent Research and Emerging Challenges in the System-Level Design of Digital Microfluidic Biochips Paul Pop, Elena Maftei, Jan MadsenTechnical University of Denmark

2. Outline • Digital microfluidic biochips • Architecture model: module vs. routing-based • Application model • System-level design • Module-based synthesis • Routing-based synthesis • Challenges

3. Architecture model Biochip from Duke University

4. Electrowetting on Dielectric

5. Reconfigurability R2 B S2 S3 R1 S1 W Non-reconfigurable • Dispensing • Detection • Splitting/Merging • Storage • Mixing/Dilution Reconfigurable

6. Operation execution: Module based R2 B S2 S3 R1 S1 W Module library 2 x 4 module

7. Operations: Mixing • Droplets can move anywhere • Fixed area: module-basedoperation execution • Unconstrained:routing-basedoperation execution

8. Operation execution: Routing based • Droplets can move anywhere • Constrained to a module • We know the completion time from the module library. • Unconstrained, any route • How can we find out the operation completion times?

9. Application model: from this… • Trinder’s reaction, a colorimetric enzyme-based method • Glucose assay steps on the biochip • Several such reactions assays in parallel:“in-vitro diagnostics” application • Reconfigurable architecture

10. Application model: …to this—an acyclic directed graph • “in-vitro diagnostics”application

11. Another application example:“Colorimetric protein assay”

12. System-level design tasks Allocation Binding Placement & routing Scheduling

13. Module-Based Synthesis

14. Module-Based Synthesis

15. Module-Based Synthesis

16. Module-Based Synthesis

17. Module-Based Synthesis

18. Module-Based Synthesis

19. Module-Based Synthesis

20. Problem Formulation • Given • Application: graph • Biochip: array of electrodes • Library of modules • Determine • Allocationof modules from modules library • Binding of modules to operations in the graph • Schedulingof operations • Placement of modules on the array • Such that • the application execution time is minimized

21. Solution Tabu Search • 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] that divides free space on the chip into overlapping rectangles • Other solutions proposed in the literature: • Integer Linear Programming • Simulated Annealing List Scheduling Maximal Empty Rectangles

22. Routing-Based Synthesis

23. Routing-Based Synthesis

24. Routing-Based Synthesis

25. Routing-Based Synthesis

26. Routing-Based Synthesis

27. Routing-Based Synthesis

28. Routing-Based Synthesis

29. Routing-Based Synthesis

30. Routing-Based Synthesis

31. Routing-Based Synthesis

32. Routing-Based Synthesis

33. Routing-Based Synthesis

34. When will the operations complete? • For module-based synthesis we know the completion time from the module library. • But now there are no modules, the droplets can move anywhere: • How can we find out the operation completion times?

35. Characterizing operations • If the droplet does not move: very slow mixing by diffusion • If the droplet moves, how long does it take to complete? • Mixing percentages: p0, p90, p180 ?

36. Characterizing operations • We know how long an operation takes on modules • Starting from this, can determine the percentages?

37. Decomposing modules • Safe, conservative estimates p90 = 0.1%, p180 = -0.5%, p0 = 0.29% and 0.58% • Moving a droplet one cell takes 0.01 s.

38. Routing-Based Synthesis

39. Routing-Based Synthesis

40. Routing- vs. Module-Based Synthesis

41. Routing- vs. Module-Based Synthesis Routing-Based Synthesis Module-Based Synthesis

42. Problem Formulation • Given • Application: graph • Biochip: array of electrodes • Library of non-reconfigurable devices • Determine • Droplet routesfor all reconfigurable operations • Allocationand bindingof non-reconfigurable modules from a library • Schedulingof operations • Such that • the application completion time is minimized

43. Proposed Solution

44. Proposed Solution Meet Execute

45. Proposed Solution Meet Minimize the time until thedroplet(s)arrive at destination Execute Minimize the completion time for the operation

46. GRASP-Based Heuristic • Greedy Randomized Adaptive Search Procedure • For each droplet: • Determine possible moves • Evaluate possible moves • Make a list ofbest N possible moves • Perform a randomlychosen possible move

47. GRASP-Based Heuristic • Greedy Randomized Adaptive Search Procedure • For each droplet: • Determine possible moves • Evaluate possible moves • Make a list ofbest N possible moves • Perform a randomlychosen possible move

48. GRASP-Based Heuristic • Greedy Randomized Adaptive Search Procedure • For each droplet: • Determine possible moves • Evaluate possible moves • Make a list ofbest N possible moves • Perform a randomlychosen possible move

49. GRASP-Based Heuristic • Greedy Randomized Adaptive Search Procedure • For each droplet: • Determine possible moves • Evaluate possible moves • Make a list ofbest N possible moves • Perform a randomlychosen possible move

50. GRASP-Based Heuristic • Greedy Randomized Adaptive Search Procedure • For each droplet: • Determine possible moves • Evaluate possible moves • Make a list ofbest N possible moves • Perform a randomlychosen possible move