Design Automation Methods and Tools for Microfluidics-Based Biochips

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Jun Zeng
Springer Science & Business Media, Nov 8, 2006 - Technology & Engineering - 403 pages
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Microfluidics-based biochips, also known as lab-on-a-chip or bio-MEMS, are becoming increasingly popular for DNA analysis, clinical diagnostics, and the detection/manipulation of bio-molecules. These systems automate highly repetitive laboratory tasks by replacing cumbersome equipment with miniaturized and integrated systems, and they enable the handling of small amounts, e.g., nanoliters, of fluids. Thus they are able to provide ultra-sensitive detection at significantly lower costs per assay than traditional methods.

As the use of microfluidics-based biochips increases, their complexity is expected to become significant due to the need for multiple and concurrent assays on the chip, as well as more sophisticated control mechanisms for resource management. Time-to-market and fault tolerance are also expected to emerge as design considerations. As a result, current full-custom design techniques will not scale well for larger designs. There is a need to deliver the same level of CAD support to the biochip designer that the semiconductor industry now takes for granted.

Design Automation Methods and Tools for Microfluidics-Based Biochips deals with all aspects of design automation for microfluidics-based biochips. Experts have contributed chapters on various aspects of biochip design automation. Topics that are covered include device modeling; adaptation of bioassays for on-chip implementations; numerical methods and simulation tools; architectural synthesis, scheduling and binding of assay operations; physical design and module placement; fault modeling and testing; reconfiguration methods.

 

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Contents

MICROFLUIDICSBASED BIOCHIPS TECHNOLOGY ISSUES IMPLEMENTATION PLATFORMS AND DESIGN AUTOMATION CHALLENGES
1
MODELING AND SIMULATION OF ELECTRIFIED DROPLETS AND ITS APPLICATION TO COMPUTERAIDED DESIGN OF DIGITAL MICR...
31
MODELING SIMULATION AND OPTIMIZATION OF ELECTROWETTING
53
ALGORITHMS IN FASTSTOKES AND ITS APPLICATION TO MICROMACHINED DEVICE SIMULATION
85
COMPOSABLE BEHAVIORAL MODELS AND SCHEMATICBASED SIMULATION OF ELECTROKINETIC LABONACHIP SYSTEMS
108
FFTSVD A FAST MULTISCALE BOUNDARY ELEMENT METHOD SOLVER SUITABLE FOR BIOMEMS AND BIOMOLECULE SIMULATION
143
MACROMODEL GENERATION FOR BIOMEMS COMPONENTS USING A STABILIZED BALANCED TRUNCATION PLUS TRAJECTORY PIE...
169
SYSTEMLEVEL SIMULATION OF FLOW INDUCED DISPERSION IN LABONACHIP SYSTEMS
189
MICROFLUIDIC INJECTOR MODELS BASED ON ARTIFICIAL NEURAL NETWORKS
215
COMPUTERAIDED OPTIMIZATION OF DNA ARRAY DESIGN AND MANUFACTURING
234
SYNTHESIS OF MULTIPLEXED BIOFLUIDIC MICROCHIPS
271
MODELING AND CONTROLLING PARALLEL TASKS IN DROPLETBASED MICROFLUIDIC SYSTEMS
301
PERFORMANCE CHARACTERIZATION OF A RECONFIGURABLE PLANAR ARRAY DIGITAL MICROFLUIDIC SYSTEM
328
A PATTERNMINING METHOD FOR HIGHTHROUGHPUT LABONACHIP DATA ANALYSIS
357
INDEX
401
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About the author (2006)

Krishnendu Chakrabarty is an Associate Professor of Electrical and Computer Engineering at Duke University. He has co-authored two books, edited a third book, and published over 150 papers in archival journals and refereed conference proceedings.

S. S. Iyengar (AAAS Fellow, IEEE Fellow, ACM Fellow) is the Roy Paul Daniels Professor of Computer Science and Chairman of the Department of Computer Science at Louisiana State University. He is the author/co-author of 13 books and he has published more than 280 papers in journals and refereed conference proceedings. He has given more than 50 plenary talks and invited lectures.He is the Editor-in-Chief of the International Journal of Distributed Sensor Networks (Taylor-Francis/CRC Press).