We evaluate optimization techniques to reduce the necessary user interaction for inverse modeling applications as they are used in the technology computer-aided design field. Four optimization strategies are compared. Two well-known global optimization methods, simulated annealing and genetic optimization, a local gradient-based optimization strategy, and a combination of a local and a global method. We rate the applicability of each method in terms of the minimal achievable target value for a given number of simulation runs and in terms of the fastest convergence. A brief overview over the three used optimization algorithms is given. The optimization framework that is used to distribute the workload over a cluster of workstations is described. The actual comparison is achieved by means of an inverse modeling application that is performed for various settings of the optimization algorithms. All presented optimization algorithms are capable of evaluating several targets in parallel. The best optimization strategy that is found is used in the calibration of a model for silicon self-interstitial cluster formation and dissolution.

We deal with problems arising in the coupling of process and device simulators. It is analyzed what kind of data and algorithms such simulations are based on. An overview of existing technology computer-aided design data models is given. A generic object-oriented data model suitable for three-dimensional process and device simulations, the so-called WAFER-STATE-SERVER is presented. By taking advantage of object-oriented abstraction mechanisms, key tasks in the coupling of simulators are relocated from the simulator into the WAFER-STATE-SERVER. The new data model allows an efficient data exchange between existing process and device simulators. It is capable of managing geometries of different dimensions, and handling grids and distributed quantities stored therein. The data model also defines algorithms to perform geometrical operations as they occur in topography simulations. Three process simulators based on the new data model were developed, one of which is presented in this work.

Diagnostics in medicine is often based on analysis of medical images. Extraction of the shape of objects is one basic step for further image analysis. It consists of two steps: (i) edge detection and (ii) contour recovery based on the detected edge points. Here, the authors focus on the second step. They describe the boundary of an object by trigonometric polynomials and present a fast and robust method for the approximation of the boundary from a given set of nonuniformly distributed noisy edge points. The authors show how the proposed technique can be efficiently generalized to 3-D surface recovery. Examples from 2-D and 3-D echocardiography are given.