A physically motivated model that accounts for the spatial and temporal evolution of self-interstitial agglomerates in ion-implanted Si is presented. For the calibration of the model, a genetic algorithm is used to find the optimum set of physical parameters from experimental data. Mean-size evolution of {113} defects obtained by transmission electron microscopy and self-interstitial oversaturation results measured in the vicinity of extended defects are combined in the same fitting procedure. The calibration of parameters shows that binding energies of small self-interstitial clusters exhibit strong maxima, as reported in other investigations. Results of the calibrated model are compared to experimental data obtained in complementary investigations. It is demonstrated that the model is able to predict a wide variety of physical phenomena, from the oversaturation of self-interstitials via the mean-size evolution of {113} defects to the depth distribution of the density of the latter

This paper introduces Dr.LiTHO, a research and development oriented lithography simulation environment developed at Fraunhofer IISB to flexibly integrate our simulation models into one coherent platform. We propose a light-weight approach to a lithography simulation environment: The use of a scripting (batch) language as an integration platform. Out of the great variety of different scripting languages, Python proved superior in many ways: It exhibits a good-natured learning-curve, it is efficient, available on virtually any platform, and provides sophisticated integration mechanisms for existing programs. In this paper, we will describe the steps, required to provide Python bindings for existing programs and to finally generate an integrated simulation environment. In addition, we will give a short introduction into selected software design demands associated with the development of such a framework. We will especially focus on testing and (both technical and user-oriented) documentation issues.Dr.LiTHO Python files contain not only all simulation parameter settings but also the simulation flow, providing maximum flexibility. In addition to relatively simple batch jobs, repetitive tasks can be pooled in libraries. And as Python is a full-blown programming language, users can add virtually any functionality, which is especially useful in the scope of simulation studies or optimization tasks, that often require masses of evaluations. Furthermore, we will give a short overview of the numerous existing Python packages. Several examples demonstrate the feasibility and productiveness of integrating Python packages into custom Dr.LiTHO scripts.

Intuitive design of the lithographic process becomes increasingly complicated in the regime of off-axis illumination and optical proximity correction. Therefore, new optimization procedures have to be introduced to facilitate the search for ideal process settings. This paper proposes mutual optimization of illumination and mask geometries using an automatic optimization approach based on a genetic algorithm. As presented elsewhere, this optimization procedure has been applied to different mask representations. It has been found that a blend of a fully pa rameterized and a pixel-based representation, i.e., a rectangle representation, leads to highly innovative solutions, but can still maintain an acceptable convergence behavior. This representation is revisited and its main principles and limitations are shortly discussed. The main focus of this paper is on a refinement of the source geometry representation. In previous versions, the general illumination setup had to be prespecified. Merely its parameters (e.g, inner and outer radius of annular illumination, number, offset, and radius of poles for multipole illumination) were optimized. In this work, the source is represented by a sector/track definition, which allows different sections of the illumination to have different transmission values. The obtained illumination geometry is transfer red into a pixel-based representation, processable by the utilized Fraunhofer IISB in-house lithography simulator. The illumination shapes achieved with the proposed approach can, for example, be produced by diffractive optics elements (DOEs). Various merit criteria determine the imaging performance of both the mask and the source settings. As the merit or fitness function plays one of the central roles in the proposed optimization scheme, individual fitness criteria and their transformation into an objective function are revisited and shortly explained. New results for both dense and chain contact hole layouts, and a comparison with former results validate the proposed approach and illustrate its further potentials

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