Design Automation Methods and Tools for Microfluidics-Based Biochips (Google eBook)
Krishnendu Chakrabarty, Jun Zeng
Springer Science & Business Media, Nov 8, 2006 - Science - 414 pages
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. 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 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.
MODELING AND SIMULATION OF ELECTRIFIED DROPLETS AND ITS APPLICATION TO COMPUTERAIDED DESIGN OF DIGITAL MICR...
MODELING SIMULATION AND OPTIMIZATION OF ELECTROWETTING
ALGORITHMS IN FASTSTOKES AND ITS APPLICATION TO MICROMACHINED DEVICE SIMULATION
COMPOSABLE BEHAVIORAL MODELS AND SCHEMATICBASED SIMULATION OF ELECTROKINETIC LABONACHIP SYSTEMS
FFTSVD A FAST MULTISCALE BOUNDARY ELEMENT METHOD SOLVER SUITABLE FOR BIOMEMS AND BIOMOLECULE SIMULATION
MACROMODEL GENERATION FOR BIOMEMS COMPONENTS USING A STABILIZED BALANCED TRUNCATION PLUS TRAJECTORY PIE...
SYSTEMLEVEL SIMULATION OF FLOW INDUCED DISPERSION IN LABONACHIP SYSTEMS
MICROFLUIDIC INJECTOR MODELS BASED ON ARTIFICIAL NEURAL NETWORKS
COMPUTERAIDED OPTIMIZATION OF DNA ARRAY DESIGN AND MANUFACTURING
SYNTHESIS OF MULTIPLEXED BIOFLUIDIC MICROCHIPS
MODELING AND CONTROLLING PARALLEL TASKS IN DROPLETBASED MICROFLUIDIC SYSTEMS
PERFORMANCE CHARACTERIZATION OF A RECONFIGURABLE PLANAR ARRAY DIGITAL MICROFLUIDIC SYSTEM
A PATTERNMINING METHOD FOR HIGHTHROUGHPUT LABONACHIP DATA ANALYSIS
actuation algorithm analysis applied approach assay atomic patterns behavioral models biofluidic bioMEMS cells centroid CFD-ACE+ Chakrabarty channel chip complex component computational Computer-Aided Design concentration conﬂicts contact angle deﬁned Design Automation devices digital microfluidic dispersion DMFS DNA array droplet droplet motion droplet type droplet-based electric field electrode electrokinetic electrophoretic electrophoretic separation electrostatic electrowetting elements embedding equation EWOD example FastStokes FFTSVD ﬁeld Figure ﬁnd ﬁrst flow ﬂuid function graph grid Hamming distance heuristic homogeneous patterns IEEE injector input integrated Integrated Circuits interface lab-on-a-chip layout linear liquid matrix method methodology microchip Microelectromechanical Systems microﬂuidic microfluidic biochips microfluidic systems microfluidics-based biochips mixing multiple neural network nodes null space operations optimal output panel parameters performance placement probe problem reduced routing sample schematic Section shown in Fig simulation solution Stokes flow subsystem surface synthesis techniques topology TPWL vector velocity vertex VLSI voltage Wang ZBDDs