1 / 55

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

Recent Research and Emerging Challenges in the System-Level Design of Digital Microfluidic Biochips. Paul Pop, Elena Maftei , Jan Madsen Technical University of Denmark. Outline. Digital microfluidic biochips Architecture model: module vs. routing-based Application model

libra
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related