1. The Case for Semi-Automated Design of Microfluidic Very Large Scale Integration (mVLSI) Chips
Jeffrey McDaniel1, William H. Grover2
and Philip Brisk1
1Department of Computer Science and Engineering
2Department of Bioengineering
University of California, Riverside

2. The State of the Field
Semiconductor VLSI/CAD has provided an algorithmic foundation for microfluidic VLSI
It remains unclear if this foundation addresses the concerns of biologists and chemists or lowers fabrication costs
Microfluidic VLSI
VLSI
FPGA
Scheduling
Algorithms
Clock Tree Routing
Negotiated Congestion Routing
PCB Escape Routing
Standard Cell Placement

3. The State of the Field VLSI FPGA PCB Escape Routing
Scheduling
Algorithms
Clock Tree Routing
Negotiated Congestion Routing
PCB Escape Routing
Standard Cell Placement

4. Our “Customer Discovery” Process (NSF I-Corps)
Speak with ~100 potential customers
Identify the pain points that they experience during LoC design
Diagnostics for all

5. Bioengineering/Biomedical Communities
Microfluidics Publications by Journal Topic Area
“We know [computer scientists] are useful, we just don’t know how”
Microfluidics Consortium member
Engineering Journals
Multidisciplinary Journals
Biology and Medicine Journals
Sackmann, E. K., Fulton, A. L., & Beebe, D. J. (2014). The present and future role of microfluidics in biomedical research. Nature, 507(7491), 181–9.

6. The Problems Device Designers Face

7. Microfluidic Entities (Standardized)
“There won’t be a repository of microfluidic components for at least 10 years”
Entities are considered black boxes
In reality they have shape
10 years at least for a repository of open source component entities

8. Replicating Existing Devices vs. Original Devices
Development and evaluation of a microdevice for
amino acid biomarker detection and analysis on Mars
Mathies Lab, UC Berkeley
Automated microfluidic chromatin immunoprecipitation from 2,000 cells
Angela Wu
Quake Labs, Stanford
Algorithmically designed device
ChIP (cite), D&T us (cite grover), Giant ? For original

9. Common Fallacies
LoC Designers desired end-to-end “push button” design automation software
Pure algorithmic solutions are not trusted
Designers desire interactive design
Automation
Fabricated Design
Experiment Design
Philip: Somehow, I lost the big red X. Sorry about that...

10. What We Learned from Customer Discovery
LoC designers are not opposed to algorithmic assistance
They do not want to cede control to software
They want to see each algorithmic change as it occurs
Manual intervention must be allowed at each step
Experiment Design
Fabricated Design
ManualDesign
Algorithm

11. Common Fallacies Semiconductor optimization metrics are relevant
Reality: Channel length, area utilization, and control skew are
of limited concern to microfluidic designers
Reality: Fabrication processes, costs, and economies of scale
are different than semiconductors
Reality: Testability, and repeatability are of utmost importance
Reality: Reliability is important, but most chips are disposable

12. The Reality of Area and Device Complexity
Most devices are small, i.e., <= 10s of valves
Not a challenge to place-and-route
Optimality metrics (e.g., channel length) minimally affect performance, as chemical/biological processes dominate latency
Large and complex devices exist, but…
Often tailored to specific applications with limited scope
High academic profile != industrial market share
There are “pseudo-standard” device sizes

13. The Future of Design Automation

14. Fully Manual Design (CIDAR Lab, Boston University)
Citation:

15. Our Direction: Semi-Automated Design
Place components and specify connections
Route channels automatically

16. Our Direction: Algorithms Interact with Manual Design
Place components manually
Route each channel as the user specifies connections between components
Final design is legal and made with ease

17. Conclusion
Producing original devices to advance biology is the only way for microfluidic CAD to bring value to the community
Algorithms can create more reliable and robust microfluidic devices
Designers will not wait 10s of minutes or hours for your ILP to converge
Optimization is pointless unless it addresses metrics that are relevant to biologists or lowers production costs

18. Thank you!

19. Acknowledgment
This material is based upon work supported by the National Science Foundation under Grant Nos , , and Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.