Publications Search Results
Now showing 1 - 10 of 14
PublicationMemetic algorithms: Parametric optimization for microlithography( 2007)
;Dürr, C. ;Fühner, T. ;Tollkühn, B. ;Erdmann, A.Kokai, G.
PublicationBenchmark of a lithography simulation tool for next generation applications( 2006)
;Tollkühn, B. ;Uhle, M. ;Fuhrmann, J. ;Gärtner, K. ;Heubner, A.Erdmann, A.Lithography continues to be a key process in IC manufacturing. A number of process steps, e.g., exposure, baking, and development are required to transfer patterns into the photoresist. In many cases, simulation helps to understand and improve these processes. This paper focuses on a critical step during the process of chemically amplified resists, namely the resist bake after exposure, which changes the solubility of the resist. Two different numerical algorithms are evaluated for the simulation of this process step. First, the IISB internal research simulation tool Dr. LiTHO and second, the WIAS research toolbox pdelib2, a general solver for systems of nonlinear reaction diffusion equations, are briefly described. Finally, the algorithms are compared for different patterns printed into the resist.
PublicationCorrelation analysis: A fast and reliable method for a better understanding of simulation models in optical lithography( 2005)
;Tollkühn, B. ;Heubner, A. ;Elian, K. ;Ruppenstein, B.Erdmann, A.
PublicationAerial image analysis for defective masks in optical lithography( 2005)
;Graf, T. ;Erdmann, A. ;Evanschitzky, P. ;Tollkühn, B. ;Eggers, K. ;Ziebold, R. ;Teuber, S.Höllein, I.The quality of photomasks in optical lithography is important for the quality of the wafer printing process. Lithography simulation software can be used to compute the influence of mask defects on the aerial or resist image of lithographic processes. The influence of various defect types and defect sizes can be compared and defect severity lists can be established. To investigate the quality of wafer images in current optical lithography different experimental tools such as AIMS and SEM are used to measure mask and wafer structures. Furthermore, it is possible to compare experimental and computational investigations and to calibrate the simulation models for future technology nodes.
PublicationSimplified resist models for efficient simulation of contact holes and line ends( 2005)
;Tollkühn, B. ;Erdmann, A. ;Semmler, A.Nölscher, C.
PublicationTowards automatic mask and source optimization for optical lithography( 2004)
;Erdmann, A. ;Fühner, T. ;Schnattinger, T.Tollkühn, B.
PublicationGenetic algorithms to improve mask and illumination geometries in lithographic imaging systems( 2004)
;Fühner, T. ;Erdmann, A. ;Farkas, R. ;Tollkühn, B.Kokai, G.This paper proposes the use of a genetic algorithm to optimize mask and illumination geometries in optical projection lithography. A fitness function is introduced that evaluates the imaging quality of arbitrary line patterns in a specified focus range. As a second criterion the manufacturability and inspectability of the mask are taken into account. With this approach optimum imaging conditions can be identified without any additional a-priori knowledge of the lithographic process. Several examples demonstrate the successful application and further potentials of the proposed concept.
PublicationDo we need complex resist models for predictive simulation of lithographic process performance?( 2004)
;Tollkühn, B. ;Erdmann, A. ;Lammers, J. ;Nolscher, C.Semmler, A.
PublicationMethodology and practical application of an ArF resist model calibration( 2004)
;Ziebold, R. ;Küchler, B. ;Nölscher, C. ;Rößiger, M. ;Elian, K.Tollkühn, B.This paper focuses on a novel methodology for a fast and efficient resist model calibration. One of the most crucial parts when calibrating a resist model is the fitting of experimental data where up to 20 resist model parameters are varied. Although general optimization approaches such as simplex algorithms or genetic algorithms have proven suitable for many applications, they do not exploit specific properties of resist models. Therefore, we have developed a new strategy based on Design of Experiment methods which makes use of these specific characteristics. This algorithm will be outlined and then be demonstrated by applying it to the calibration of a Solid-C resist model for one ArF line/space resist. As characterizing dataset we chose: a) a Focus Exposure Matrix (FEM) for the dense array, b) linearity, c) OPE (optical proximity) curve and e) the MEEF (mask error enhancement factor) for a dense array. It turned out that a simultaneous fit of the complete data set wa s not possible by varying resist parameters only. Considering the optical parameters appeared to be crucial as well. Therefore the influence of the optical settings (illumination, projection, 3D mask effects) on the lithography process will be discussed at this point. Finally we obtained an excellent matching of model predictions and experimental results.