Directed Placement for mVLSI Devices

Author:

Crites Brian1ORCID,Kong Karen1,Brisk Philip1

Affiliation:

1. University of California, Riverside, CA

Abstract

Continuous-flow microfluidic devices based on integrated channel networks are becoming increasingly prevalent in research in the biological sciences. At present, these devices are physically laid out by hand by domain experts who understand both the underlying technology and the biological functions that will execute on fabricated devices. The lack of a design science that is specific to microfluidic technology creates a substantial barrier to entry. To address this concern, this article introduces Directed Placement, a physical design algorithm that leverages the natural “directedness” in most modern microfluidic designs: fluid enters at designated inputs, flows through a linear or tree-based network of channels and fluidic components, and exits the device at dedicated outputs. Directed placement creates physical layouts that share many principle similarities to those created by domain experts. Directed placement allows components to be placed closer to their neighbors compared to existing layout algorithms based on planar graph embedding or simulated annealing, leading to an average reduction in laid-out fluid channel length of 91% while improving area utilization by 8% on average. Directed placement is compatible with both passive and active microfluidic devices and is compatible with a variety of mainstream manufacturing technologies.

Funder

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Reducing Microfluidic Very Large-Scale Integration (mVLSI) Chip Area by Seam Carving;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2021-10

2. Dynamic Radial Placement and Routing in Paper Microfluidics;IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems;2021-10

3. Machine learning enables design automation of microfluidic flow-focusing droplet generation;Nature Communications;2021-01-04

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