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

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

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

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

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. http://doi.org/10.1038/nature13118

The Problems Device Designers Face

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

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

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...

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

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

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

The Future of Design Automation

Fully Manual Design (CIDAR Lab, Boston University) Citation: http://cidarlab.github.io/3DuF/

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

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

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

Thank you!

Acknowledgment This material is based upon work supported by the National Science Foundation under Grant Nos. 1351115, 1560596, and 1640757. 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.