期刊
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
卷 41, 期 11, 页码 4612-4625出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2021.3135323
关键词
Biochips; Mixers; Task analysis; Electrodes; Sequential analysis; Testing; Routing; Generic; labs on chip (LoC); medical; microfluidic platform; sample preparation
类别
资金
- Linz Institute of Technology Project [LIT613361001 SP2PDM]
- LIT Secure and Correct Systems Lab - State of Upper Austria
- FFG Project AUTOMATE [890068]
- BMDW
- BMK
- State of Upper Austria
Sample preparation is a crucial task in medical applications, with microfluidic devices offering solutions for this purpose. A generic approach has been proposed to address the diversity of different microfluidic platforms, enabling platform-independent sample preparation.
Sample preparation refers to the task of generating fluids with a specified target concentration. Generally, this is achieved by performing a set of mixing operations between biochemical fluids with a given volumetric ratio. Sample preparation plays a crucial role in several medical applications. Microfluidic devices or labs-on-chips (LoCs) got established as a suitable solution to realize this task in a miniaturized, integrated, and automatic fashion. Over the years, a variety of different microfluidic platforms emerged, which all have their respective pros and cons. Accordingly, numerous approaches aiming at the sample preparation problem have been proposed-each specialized on a single platform only. More precisely, sample preparation methods introduced thus far provide solutions for a particular platform only, i.e., they are platform specific. In this work, we propose a generic approach that generalizes the constraints of the different microfluidic platforms and, by this, provides a platform-independent sample preparation method. This allows designers to quickly check what existing platform is most suitable for the considered task and to easily support upcoming and future microfluidic platforms as well. We evaluated the performance of the proposed method with a wide range of test cases and concluded (from the evaluations) that the proposed generic approach is capable of efficiently generating results for various platforms with a quality that is close to results from dedicated approaches presented thus far.
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