Publications

We have developed and implemented a method which given a three-dimensional object can infer from topology the two-dimensional masks needed to produce that object with surface micro-machining. The masks produced by this design tool can be generic, process independent masks, or if given process constraints, specific for a target process. This design tool calculates the two-dimensional mask set required to produce a given three-dimensional model by investigating the vertical topology of the model.

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Genetic expression and control pathways can be successfully modeled as electrical circuits. To tackle large multicellular and genome scale simulations, the massively-parallel, electronic circuit simulator, Xyce™ [11], was adapted to address biological problems. Unique to this bio-circuit simulator is the ability to simulate not just one or a set of genetic circuits in a cell, but many cells and their internal circuits interacting through a common environment. Additionally, the circuit simulator Xyce can couple to the optimization and uncertainty analysis framework Dakota [2] allowing one to find viable parameter spaces for normal cell functionality and required parameter ranges for unknown or difficult to measure biological constants. Using such tools, we investigate the Drosophila sp. segmental differentiation network's stability as a function of initial conditions.

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This manual describes the use of theXyceParallel Electronic Simulator.Xycehasbeen designed as a SPICE-compatible, high-performance analog circuit simulator, andhas been written to support the simulation needs of the Sandia National Laboratorieselectrical designers. This development has focused on improving capability over thecurrent state-of-the-art in the following areas:%04Capability to solve extremely large circuit problems by supporting large-scale par-allel computing platforms (up to thousands of processors). Note that this includessupport for most popular parallel and serial computers.%04Improved performance for all numerical kernels (e.g., time integrator, nonlinearand linear solvers) through state-of-the-art algorithms and novel techniques.%04Device models which are specifically tailored to meet Sandia's needs, includingmany radiation-aware devices.3 XyceTMUsers' Guide%04Object-oriented code design and implementation using modern coding practicesthat ensure that theXyceParallel Electronic Simulator will be maintainable andextensible far into the future.Xyceis a parallel code in the most general sense of the phrase - a message passingparallel implementation - which allows it to run efficiently on the widest possible numberof computing platforms. These include serial, shared-memory and distributed-memoryparallel as well as heterogeneous platforms. Careful attention has been paid to thespecific nature of circuit-simulation problems to ensure that optimal parallel efficiencyis achieved as the number of processors grows.The development ofXyceprovides a platform for computational research and de-velopment aimed specifically at the needs of the Laboratory. WithXyce, Sandia hasan %22in-house%22 capability with which both new electrical (e.g., device model develop-ment) and algorithmic (e.g., faster time-integration methods, parallel solver algorithms)research and development can be performed. As a result,Xyceis a unique electricalsimulation capability, designed to meet the unique needs of the laboratory.4 XyceTMUsers' GuideAcknowledgementsThe authors would like to acknowledge the entire Sandia National Laboratories HPEMS(High Performance Electrical Modeling and Simulation) team, including Steve Wix, CarolynBogdan, Regina Schells, Ken Marx, Steve Brandon and Bill Ballard, for their support onthis project. We also appreciate very much the work of Jim Emery, Becky Arnold and MikeWilliamson for the help in reviewing this document.Lastly, a very special thanks to Hue Lai for typesetting this document with LATEX.TrademarksThe information herein is subject to change without notice.Copyrightc 2002-2003 Sandia Corporation. All rights reserved.XyceTMElectronic Simulator andXyceTMtrademarks of Sandia Corporation.Orcad, Orcad Capture, PSpice and Probe are registered trademarks of Cadence DesignSystems, Inc.Silicon Graphics, the Silicon Graphics logo and IRIX are registered trademarks of SiliconGraphics, Inc.Microsoft, Windows and Windows 2000 are registered trademark of Microsoft Corporation.Solaris and UltraSPARC are registered trademarks of Sun Microsystems Corporation.Medici, DaVinci and Taurus are registered trademarks of Synopsys Corporation.HP and Alpha are registered trademarks of Hewlett-Packard company.Amtec and TecPlot are trademarks of Amtec Engineering, Inc.Xyce's expression library is based on that inside Spice 3F5 developed by the EECS De-partment at the University of California.All other trademarks are property of their respective owners.ContactsBug Reportshttp://tvrusso.sandia.gov/bugzillaEmailxyce-support%40sandia.govWorld Wide Webhttp://www.cs.sandia.gov/xyce5 XyceTMUsers' GuideThis page is left intentionally blank6

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We have developed and implemented a method, which given a three-dimensional object can infer from topology the two-dimensional masks needed to produce that object with surface micromachining. The masks produced by this design tool can be generic, process independent masks, or if given process constraints, specific for a target process. This design tool calculates the two-dimensional mask set required to produce a given three-dimensional model by investigating the vertical topology to the model. The 3D model is first separated into bodies that are non-intersecting, made from different materials or only linked through a ground plane. Next, for each body unique vertical cross sections are located and arranged into a tree based on their topological relationship. A branch-wise search of the tree uncovers locations where deposition boundaries must lie and identifies candidate masks creating a generic mask set for the 3D model. Finally, in the last step specific process requirements are considered that may constrain the generic mask set. Constraints can include the thickness or number of deposition layers, specific ordering of masks as required by a process and type of material used in a given layer. Candidate masks are reconciled with the process constraints through a constrained optimization.

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