The image created in reflected light DIC can often be interpreted as a true three-dimensional representation of the surface geometry, provided a clear distinction can be realized between raised and lowered regions in the specimen. It may be helpful if our definition of saliency embraces work on the human visual system (HVS) as well as the more abstract work on saliency, as it is certain that understanding by humans will always stand between recording of a useful signal from all manner of sensors and so-called actionable intelligence. A DARPA/DSO program lays down this requirement in a current program (Kruse 2010): The vision for the Neurotechnology for Intelligence Analysts (NIA) Program is to revolutionize the way that analysts handle intelligence imagery, increasing both the throughput of imagery to the analyst and overall accuracy of the assessments. Current computer-based target detection capabilities cannot process vast volumes of imagery with the speed, flexibility, and precision of the human visual system.
This report describes the SummitView 1.0 computer code developed at Sandia National Laboratories. SummitView is designed to generate a 3D solid model, amenable to visualization and meshing, that represents the end state of a microsystem fabrication process such as the SUMMiT (Sandia Ultra-Planar Multilevel MEMS Technology) V process. Functionally, SummitView performs essentially the same computational task as an earlier code called the 3D Geometry modeler [1]. However, because SummitView is based on 2D instead of 3D data structures and operations, it has significant speed and robustness advantages. As input it requires a definition of both the process itself and the collection of individual 2D masks created by the designer and associated with each of the process steps. The definition of the process is contained in a special process definition file [2] and the 2D masks are contained in MEM format files [3]. The code is written in C++ and consists of a set of classes and routines. The classes represent the geometric data and the SUMMiT V process steps. Classes are provided for the following process steps: Planar Deposition, Planar Etch, Conformal Deposition, Dry Etch, Wet Etch and Release Etch. SummitView is built upon the 2D Boolean library GBL-2D [4], and thus contains all of that library's functionality.
This report describes version 1.0 of GBL-2D, a geometric Boolean library for 2D objects. The library is written in C++ and consists of a set of classes and routines. The classes primarily represent geometric data and relationships. Classes are provided for 2D points, lines, arcs, edge uses, loops, surfaces and mask sets. The routines contain algorithms for geometric Boolean operations and utility functions. Routines are provided that incorporate the Boolean operations: Union(OR), XOR, Intersection and Difference. A variety of additional analytical geometry routines and routines for importing and exporting the data in various file formats are also provided. The GBL-2D library was originally developed as a geometric modeling engine for use with a separate software tool, called SummitView [1], that manipulates the 2D mask sets created by designers of Micro-Electro-Mechanical Systems (MEMS). However, many other practical applications for this type of software can be envisioned because the need to perform 2D Boolean operations can arise in many contexts.