Chapter 9 Summary and Outlook

This thesis introduces a set of newly developed geometry-aware algorithms for hierarchical grids which are centered around identifying and utilizing the discrete surface curvatures of topographies arising in semiconductor process TCAD simulations to optimize computational processing. These geometry-aware algorithms significantly increase the performance of topography simulations by selectively refining or simplifying the discrete representation of the device topography during a simulation.

The three most prominently used discrete surface representations during topography simulations were introduced; level-set functions, surface meshes, and point clouds. Furthermore, the most common ways of switching between these surface representations and their role during topography simulations were discussed. The primary numerical methods used during a topography simulation were considered: The representation of materials on the wafer surface, the evolution of these materials in time (i.e., the level-set method), and three strategies for estimating the surface flux. These discussed methods were then combined into a general workflow for topography simulations. Finally, the concept of the surface curvature on continuous surfaces was discussed, as well as several strategies of how to use this concept on the previously discussed discrete surface representations.

The surface curvatures of the discretized surfaces were used to formulate an automatic feature detection algorithm which detects parts of a discrete surface with significant geometric variation. For 3D level-set functions three methods from the literature and a novel extension of the standard calculation method of the surface curvatures have been investigated for their applicability in topography simulations. Two methods stood out, depending on the quality requirements of the feature detection. For performance oriented applications the Shape Operator method is superior to all other methods. This method uses the smallest finite difference stencil to calculate the mean curvature of the level-set function, while avoiding the calculation of the Gaussian curvature for a robust feature detection. The second method is the novel Big Stencil method, which has a similar computational performance to the other tested methods, yet it has a higher numerical accuracy and is less susceptible to numerical noise. Additionally, a feature detection parameter for topography simulations has been obtained through a parameter study performed on typical device topographies.

The feature detection algorithm and feature detection parameter were used to guide a hierarchical grid placement algorithm to refine the simulation domains of topography simulations. Due to the detected features of the device topography, the hierarchical grid placement algorithm was able to precisely place sub-grids at parts of the simulation domain that improve the discrete description of the topography, while minimizing impacts on simulation performance. This hierarchical approach has been used to simulate selective epitaxial growth of \(\mathrm {SiGe}\) crystals, which leads to an improvement in computation time, while maintaining an accurate description of the crystal surface.

Furthermore, the feature detection algorithm has been used to improve Monte Carlo ray tracing based surface flux calculations on surface meshes. The detected features have been used to split the surface mesh into two separate regions which are used to guide a surface mesh simplification algorithm. Depending on the previously calculated regions, the surface mesh simplification algorithm is able to remove more or less triangles from the original surface mesh. Additionally, the quality of the triangles between the regions is taken into consideration to create a steady increase in the size of the triangles to prevent the formation of bad mesh elements. This approach maximizes the amount of triangles that are removed from the surface mesh, while maintaining a detailed description of its features.

A specially designed feature detection algorithm for etching simulations of thin material layers utilizing Boolean operations has been developed. This algorithm analyzes the thickness of the material layers that are affected by an etching simulation and determines a minimal required refinement level. Thus, it prevents the formation of numerical artifacts as a consequence of a too coarse resolution of the simulation domain. The computational performance of the algorithm is further improved by dynamically increasing the resolution of the final sub-grid to reach the previously determined minimal required refinement level.

Some possible, future extensions of the geometry-aware algorithms introduced in this work for topography simulations are discussed in the following paragraphs. Monte Carlo ray tracing based surface flux calculations introduce numerical noise into the discrete surface description. This noise prevents more straightforward implementations of feature detection strategies from accurately detecting the features of the surface. The Big Stencil method is able to ignore surface noise introduced by the process model and the finite difference scheme used to solve the level-set equation. Thus, the Big Stencil method could be able to only detect features of the topography and ignore the noise from Monte Carlo based simulations. Furthermore, finite difference schemes with even bigger finite difference stencils could be investigated, which may lead to a more reliable feature detection on surfaces with noise.

The introduced feature detection algorithm can be used to speed up Monte Carlo ray tracing based surface flux calculations on point clouds. In this case, the features of the device topography can be detected with the help of the implicitly defined level-set function, thus redistributing the expensive curvature calculations on point clouds to their computationally cheaper calculations on level-set functions. The detected features could then be further used to simplify the point cloud during its extraction from the level-set function.

The initial motivation of the feature detection algorithm and hierarchical grid placement algorithm described in this work was to improve simulation performance of topography simulations by selectively refining the simulation domain at features of the topography. Clearly, the feature detection and hierarchical grid placement steps introduce an overhead into a topography simulation, which is evidently small enough to improve simulation performance. However, it is possible that for particularly complex topographies the amount of required sub-grids is so high that the overhead of the hierarchical approach exceeds the performance gains. Thus, it can be of interest to develop a heuristic that determines if a simulation should use a certain amount of grid levels and sub-grids or use a higher base grid resolution with fewer grid-levels.

Bibliography

• [1] S. Hofstein and F. Heiman. “The Silicon Insulated-Gate Field-Effect Transistor”. In: Proceedings of the IEEE 51.9 (1963), pp. 1190–1202. doi: 10.1109/PROC.1963.2488.

• [2] Y. Yasuda-Masuoka, J. Jeong, K. Son, S. Lee, S. Park, Y. Lee, J. Youn Kim, J. Lee, M. Cho, S. Lee, S. Hong, H. Hong, Y. Jung, C. Yoon, Y. Ko, K. Jung, T. Myung, J. M. Youn, and G. Jeong. “High Performance 4nm FinFET Platform (4LPE) with Novel Advanced Transistor Level DTCO for Dual-CPP/HP-HD Standard Cells”. In: Proceedings of the IEEE International Electron Devices Meeting (IEDM). 2021, pp. 13.3.1–13.3.4. doi: 10.1109/IEDM19574.2021.9720656.

• [3] G. E. Moore. “Cramming More Components Onto Integrated Circuits, Reprinted From Electronics, Volume 38, Number 8, April 19, 1965, pp.114 ff.” In: IEEE Solid-State Circuits Society Newsletter 11.3 (2006), pp. 33–35. doi: 10.1109/N-SSC.2006.4785860.

• [4] M. G. S. and S. Costas J. Fundamentals of Semiconductor Manufacturing and Process Control. John Wiley & Sons, 2006. doi: 10.1002/0471790281.

• [5] M. C. K. Introducing Technology Computer-Aided Design (TCAD). 1st. John Wiley & Sons, 2017. doi: 10.1201/9781315364506.

• [6] A. Yanguas-Gil. Growth and Transport in Nanostructured Materials: Reactive Transport in PVD, CVD, and ALD. Springer, 2016. doi: 10.1007/978-3-319-24672-7.

• [7] L. M. A. and L. A. J. Principles of Plasma Discharges and Materials Processing. Jenny Stanford Publishing, 2005. doi: 10.1002/0471724254.

• [8] S. J. A. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, 1999.

• [9] E. Chason, S. T. Picraux, J. M. Poate, J. O. Borland, M. I. Current, T. Diaz de la Rubia, D. J. Eaglesham, O. W. Holland, M. E. Law, C. W. Magee, J. W. Mayer, J. Melngailis, and A. F. Tasch. “Ion Beams in Silicon Processing and Characterization”. In: Journal of Applied Physics 81.10 (1997), pp. 6513–6561. doi: 10.1063/1.365193.

• [10] K. K. Bhuwalka, H. Wu, W. Zhao, G. Rzepa, O. Baumgartner, F. Benistant, Y. Chen, and C. Liu. “Optimization and Benchmarking FinFETs and GAA Nanosheet Architectures at 3-nm Technology Node: Impact of Unique Boosters”. In: IEEE Transactions on Electron Devices 69.8 (2022), pp. 4088–4094. doi: 10.1109/TED.2022.3178665.

• [11] H. Kwon, H. Huh, H. Seo, S. Han, I. Won, J. Sue, D. Oh, F. Iza, S. Lee, S. K. Park, and S. Cha. “TCAD Augmented Generative Adversarial Network for Hot-Spot Detection and Mask-Layout Optimization in a Large Area HARC Etching Process”. In: Physics of Plasmas 29.7 (2022), p. 073504. doi: 10.1063/5.0093076.

• [12] X. Klemenschits, P. Manstetten, L. Filipovic, and S. Selberherr. “Process Simulation in the Browser: Porting ViennaTS using WebAssembly”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2019, pp. 339–342. doi: 10.1109/SISPAD.2019.8870374.

• [13] X. Klemenschits. “Emulation and Simulation of Microelectronic Fabrication Processes”. Doctoral Dissertation. TU Wien, 2022.

• [14] N. Patrikalakis and T. Maekawa. Shape Interrogation for Computer Aided Design and Manufacturing. 1st. Springer, 2002. doi: 10.1007/978-3-642-04074-0.

• [15] S. Osher and R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Vol. 153. Springer, 2003. doi: 10.1007/b98879.

• [16] P. Frey and L. George Paul. Mesh Generation: Application to Finite Elements. 2nd. Wiley-ISTE, 2013.

• [17] X. Klemenschits, S. Selberherr, and L. Filipovic. “Modeling of Gate Stack Patterning for Advanced Technology Nodes: A Review”. In: Micromachines 9.12 (2018), p. 631. doi: 10.3390/mi9120631.

• [18] P. Manstetten, J. Weinbub, A. Hössinger, and S. Selberherr. “Using Temporary Explicit Meshes for Direct Flux Calculation on Implicit Surfaces”. In: Procedia Computer Science 108 (2017), pp. 245–254. doi: 10.1016/j.procs.2017.05.067.

• [19] M. J. Berger and J. Oliger. “Adaptive Mesh Refinement for Hyperbolic Partial Differential Equations”. In: Journal of Computational Physics 53.3 (1984), pp. 484–512. doi: 10.1016/0021-9991(84)90073-1.

• [20] B. Zönnchen and G. Köster. “A Parallel Generator for Sparse Unstructured Meshes to Solve the Eikonal Equation”. In: Journal of Computational Science 32 (2019), pp. 141–147. doi: 10.1016/j.jocs.2018.09.009.

• [21] Å. Ervik, K. Y. Lervåg, and S. T. Munkejord. “A Robust Method For Calculating Interface Curvature and Normal Vectors Using an Extracted Local Level Set”. In: Journal of Computational Physics 257 (2014), pp. 259–277. doi: 10.1016/j.jcp.2013.09.053.

• [22] R. A. Trompert and J. G. Verwer. “A Static-Regridding Method for Two-Dimensional Parabolic Partial Differential Equations”. In: Applied Numerical Mathematics 8.1 (1991), pp. 65–90. doi: 10.1016/0168-9274(91)90098-K.

• [23] P. Lu and X. Xu. “A Robust Multilevel Preconditioner Based on a Domain Decomposition Method for the Helmholtz Equation”. In: Journal of Scientific Computing 81 (2019), pp. 291–311. doi: 10.1007/s10915-019-01015-z.

• [24] C. Wang, W. Wang, S. Pan, and F. Zhao. “A Local Curvature Based Adaptive Particle Level Set Method”. In: Journal of Scientific Computing 91 (2022). doi: 10.1007/s10915-022-01772-4.

• [25] M. P. d. Carmo. Differential Geometry of Curves & Surfaces. 2nd. Dover Publications, Inc., 2016.

• [26] H. T. Ho and D. Gibbins. “Multi-Scale Feature Extraction for 3D Models using Local Surface Curvature”. In: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA). 2008, pp. 16–23. doi: 10.1109/DICTA.2008.64.

• [27] Q. Mérigot, M. Ovsjanikov, and L. J. Guibas. “Voronoi-Based Curvature and Feature Estimation From Point Clouds”. In: IEEE Transactions on Visualization and Computer Graphics 17 (2011), pp. 743–756. doi: 10.1109/TVCG.2010.261.

• [28] H. S. Kim, H. K. Choi, and K. H. Lee. “Feature Detection of Triangular Meshes Based on Tensor Voting Theory”. In: CAD Computer Aided Design 41 (2009), pp. 47–58. doi: 10.1016/j.cad.2008.12.003.

• [29] U. Clarenz, M. Rumpf, and A. Telea. “Robust Feature Detection and Local Classification for Surfaces Based on Moment Analysis”. In: IEEE Transactions on Visualization and Computer Graphics 10 (2004), pp. 516–524. doi: 10.1109/TVCG.2004.34.

• [30] L. F. Aguinsky. “Phenomenological Modeling of Reactive Single-Particle Transport in Semiconductor Processing”. Doctoral Dissertation. TU Wien, 2022. doi: 10.34726/hss.2023.107502.

• [31] P. Hong, Z. Zhao, J. Luo, Z. Xia, X. Su, L. Zhang, C. Li, and Z. Huo. “An Improved Dimensional Measurement Method of Staircase Patterns with Higher Precision in 3D NAND”. In: IEEE Access 8 (2020), pp. 140054–140061. doi: 10.1109/ACCESS.2020.3012012.

• [32] X. Zhou, P. Tian, C. W. Sher, J. Wu, H. Liu, R. Liu, and H. C. Kuo. “Growth, Transfer Printing and Colour Conversion Techniques Towards Full-Colour Micro-LED Display”. In: Progress in Quantum Electronics 71 (2020), p. 100263. doi: 10.1016/j.pquantelec.2020.100263.

• [33] O. Ertl and S. Selberherr. “Three-Dimensional Level Set Based Bosch Process Simulations using Ray Tracing for Flux Calculation”. In: Microelectronic Engineering 87.1 (2010), pp. 20–29. doi: 10.1016/j.mee.2009.05.011.

• [34] L. F. Aguinsky, F. Rodrigues, G. Wachter, M. Trupke, U. Schmid, A. Hössinger, and J. Weinbub. “Phenomenological Modeling of Low-Bias Sulfur Hexafluoride Plasma Etching of Silicon”. In: Solid-State Electronics 191 (2022), p. 108262. doi: 10.1016/j.sse.2022.108262.

• [35] T. Reiter, X. Klemenschits, and L. Filipovic. “Impact of Plasma Induced Damage on the Fabrication of 3D NAND Flash Memory”. In: Solid-State Electronics 192 (2022). invited, p. 108261. doi: 10.1016/j.sse.2022.108261.

• [36] H.-K. Zhao, T. Chan, B. Merriman, and S. Osher. “A Variational Level Set Approach to Multiphase Motion”. In: Journal of Computational Physics 127.1 (1996), pp. 179–195. doi: 10.1006/jcph.1996.0167.

• [37] S. C. Endres, M. Avila, and L. Mädler. “A Discrete Differential Geometric Formulation of Multiphase Surface Interfaces for Scalable Multiphysics Equilibrium Simulations”. In: Chemical Engineering Science 257 (2022), p. 117681. doi: 10.1016/j.ces.2022.117681.

• [38] L. Ma, Y. Li, J. Li, C. Wang, R. Wang, and M. A. Chapman. “Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review”. In: Remote Sensing 10.10 (2018). doi: 10.3390/rs10101531.

• [39] M. Engelcke, D. Rao, D. Z. Wang, C. H. Tong, and I. Posner. “Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 2017, pp. 1355–1361. doi: 10.1109/ICRA.2017.7989161.

• [40] S. B. Walsh, D. J. Borello, B. Guldur, and J. F. Hajjar. “Data Processing of Point Clouds for Object Detection for Structural Engineering Applications”. In: Computer-Aided Civil and Infrastructure Engineering 28.7 (2013), pp. 495–508. doi: 10.1111/mice.12016.

• [41] I. Wald, S. Woop, C. Benthin, G. S. Johnson, and M. Ernst. “Embree: A Kernel Framework for Efficient CPU Ray Tracing”. In: ACM Transactions on Graphics 33.4 (2014), pp. 1–8. doi: 10.1145/2601097.2601199.

• [42] J. Otepka, S. Ghuffar, C. Waldhauser, R. Hochreiter, and N. Pfeifer. “Georeferenced Point Clouds: A Survey of Features and Point Cloud Management”. In: ISPRS International Journal of Geo-Information 2.4 (2013), pp. 1038–1065. doi: 10.3390/ijgi2041038.

• [43] T. Mølhave, P. K. Agarwal, L. Arge, and M. Revsbæk. “Scalable Algorithms for Large High-Resolution Terrain Data”. In: Proceedings of the International Conference and Exhibition on Computing for Geospatial Research & Application (COM-GEO). 2010. doi: 10.1145/1823854.1823878.

• [44] H. Samet. Foundations of Multidimensional and Metric Data Structures. 4th. Morgan Kaufmann, 2006.

• [45] T. Caelli and J. Berkmann. “Computation of Surface Geometry and Segmentation Using Covariance Techniques”. In: IEEE Transactions on Pattern Analysis & Machine Intelligence 16.11 (1994), pp. 1114–1116. doi: 10.1109/34.334391.

• [46] K. Klasing, D. Althoff, D. Wollherr, and M. Buss. “Comparison of Surface Normal Estimation Methods for Range Sensing Applications”. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 2009, pp. 3206–3211. doi: 10.1109/ROBOT.2009.5152493.

• [47] C. Siu-Wing, D. Tamal K., and S. Jonathan. Delaunay Mesh Generation. 1st. Chapman and Hall/CRC, 2013.

• [48] M. Berg, M. Kreveld, M. Overmars, and O. Schwarzkopf. Computational Geometry: Algorithms and Applications. Springer, 2000. doi: 10.1007/978-3-662-03427-9.

• [49] P. P. Pébay and T. J. Baker. “Analysis of Triangle Quality Measures”. In: Mathematics of Computation 72 (2003), pp. 1817–1839. doi: 10.1090/S0025-5718-03-01485-6.

• [50] I. Babuška and A. K. Aziz. “On the Angle Condition in the Finite Element Method”. In: SIAM Journal on Numerical Analysis 13.2 (1976), pp. 214–226. doi: 10.1137/0713021.

• [51] J. D. Foley, A. van Dam, S. K. Feiner, and J. F. Hughes. Computer Graphics: Principles and Practice. 2nd. Addison-Wesley, 1997.

• [52] M. Chen, X. Chen, K. Tang, and M. Yuen. “Efficient Boolean Operation on Manifold Mesh Surfaces”. In: Computer-Aided Design and Applications 7 (2013), pp. 405–415. doi: 10.3722/cadaps.2010.405-415.

• [53] A. Requicha and H. Voelcker. “Boolean Operations in Solid Modeling: Boundary Evaluation and Merging Algorithms”. In: Proceedings of the IEEE 73.1 (1985), pp. 30–44. doi: 10.1109/PROC.1985.13108.

• [54] S. Osher and J. A. Sethian. “Fronts Propagating with Curvature Dependent Speed”. In: Journal of Computational Physics 79.1 (1988), pp. 12–49. doi: 10.1016/0021-9991(88)90002-2.

• [55] R. T. Whitaker. “A Level-Set Approach to 3D Reconstruction from Range Data”. In: International Journal of Computer Vision 29 (1998), pp. 203–231. doi: 10.1023/A:1008036829907.

• [56] D. Adalsteinsson and J. A. Sethian. “A Fast Level Set Method for Propagating Interfaces”. In: Journal of Computational Physics 118.2 (1995), pp. 269–277. doi: 10.1006/jcph.1995.1098.

• [57] E. L. C. Partial Differential Equations. 2nd. Berkeley: Graduate Studies in Mathematics, 1998. doi: 10.1090/gsm/019.

• [58] O. Ertl. “Numerical Methods for Topography Simulation”. Doctoral Dissertation. TU Wien, 2010. doi: 10.34726/hss.2010.001.

• [59] B. Wyvill, A. Guy, and E. Galin. “Extending the CSG Tree. Warping, Blending and Boolean Operations in an Implicit Surface Modeling System”. In: Computer Graphics Forum 18.2 (1999), pp. 149–158. doi: 10.1111/1467-8659.00365.

• [60] A. Pasko, V. Adzhiev, A. Sourin, and V. Savchenko. “Function Representation in Geometric Modeling: Concepts, Implementation and Applications”. In: The Visual Computer 11 (1995), pp. 429–446. doi: 10.1007/BF02464333.

• [61] C. Lenz, A. Toifl, M. Quell, F. Rodrigues, A. Hössinger, and J. Weinbub. “Curvature Based Feature Detection for Hierarchical Grid Refinement in TCAD Topography Simulations”. In: Solid-State Electronics 191 (2022), p. 108258. doi: 10.1016/j.sse.2022.108258.

• [62] W. E. Lorensen and H. E. Cline. “Marching Cubes: A High Resolution 3D Surface Construction Algorithm”. In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference (SIGGRAPH) 21.4 (1987), pp. 163–169. doi: 10.1145/37401.37422.

• [63] C. Maple. “Geometric Design and Space Planning Using the Marching Squares and Marching Cube Algorithms”. In: Proceedings of the International Conference on Geometric Modeling and Graphics (GMAG). 2003, pp. 90–95. doi: 10.1109/GMAG.2003.1219671.

• [64] M. W. Jones. 3D Distance from a Point to a Triangle. Tech. rep. Department of Computer Science, University of Wales Swansea Technical Report CSR-5, 1995.

• [65] U. Pinkall and K. Polthier. “Computing Discrete Minimal Surfaces and Their Conjugates”. In: Experimental Mathematics 2.1 (1993), pp. 15–36. doi: 10.1080/10586458.1993.10504266.

• [66] F. Cazals and M. Pouget. “Estimating Differential Quantities Using Polynomial Fitting of Osculating Jets”. In: Computer Aided Geometric Design 22.2 (2005), pp. 121–146. doi: 10.1016/j.cagd.2004.09.004.

• [67] M. Meyer, M. Desbrun, P. Schröder, and A. H. Barr. “Discrete Differential-Geometry Operators for Triangulated 2-Manifolds”. In: Proceedings of Visualization and Mathematics III (MATHVISUAL). Ed. by H.-C. Hege and K. Polthier. Springer Berlin Heidelberg, 2003, pp. 35–57. doi: 10.1007/978-3-662-05105-4_2.

• [68] Q. Du, V. Faber, and M. Gunzburger. “Centroidal Voronoi Tessellations: Applications and Algorithms”. In: SIAM Review 41.4 (1999), pp. 637–676. doi: 10.1137/S0036144599352836.

• [69] D. Ulrich, H. Stefan, K. Albrecht, and W. Ortwin. Minimal Surfaces II. 1st ed. Springer, 1992.

• [70] D. Ulrich, H. Stefan, K. Albrecht, and W. Ortwin. Minimal Surfaces I. 1st ed. Springer, 1992.

• [71] E. Abbena, S. Salamon, and A. Gray. Modern Differential Geometry of Curves and Surfaces with Mathematica. 3rd. Chapman and Hall/CRC, 2006. doi: 10.1201/9781315276038.

• [72] K. Polthier and M. Schmies. “Straightest Geodesics on Polyhedral Surfaces”. In: Mathematical Visualization: Algorithms, Applications and Numerics. Springer Berlin Heidelberg, 1998, pp. 135–150. doi: 10.1007/978-3-662-03567-2_11.

• [73] R. Goldman. “Curvature Formulas for Implicit Curves and Surfaces”. In: Computer Aided Geometric Design 22.7 (2005), pp. 632–658. doi: 10.1016/j.cagd.2005.06.005.

• [74] C. Lenz, L. F. Aguinsky, A. Hössinger, and J. Weinbub. “A Complementary Topographic Feature Detection Algorithm Based on Surface Curvature for Three-Dimensional Level-Set Functions”. In: Journal of Scientific Computing 94 (2023), p. 21. doi: 10.1007/s10915-023-02133-5.

• [75] G. Kindlmann, R. Whitaker, T. Tasdizen, and T. Moller. “Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications”. In: Proceedings of the IEEE Visualization Conference (VIS). 2003, pp. 513–520. doi: 10.1109/VISUAL.2003.1250414.

• [76] R. T. Whitaker and X. Xue. “Variable-Conductance, Level-Set Curvature for Image Denoising”. In: Proceedings of the International Conference on Image Processing (ICIP). 2001, pp. 142–145. doi: 10.1109/icip.2001.958071.

• [77] A. Lefohn and R. T. Whitaker. A GPU-Based, Three-Dimensional Level Set Solver with Curvature Flow. Tech. rep. UC Davis: Institute for Data Analysis and Visualization, 2002.

• [78] J. A. Sethian and D. Adalsteinsson. “An Overview of Level Set Methods for Etching, Deposition, and Lithography Development”. In: IEEE Transactions on Semiconductor Manufacturing 10 (1997), pp. 167–184. doi: 10.1109/66.554505.

• [79] C.-W. Shu and S. Osher. “Efficient Implementation of Essentially Non-Oscillatory Shock-Capturing Schemes”. In: Journal of Computational Physics 77.2 (1988), pp. 439–471. doi: 10.1016/0021-9991(88)90177-5.

• [80] R. J. Spiteri and S. J. Ruuth. “A New Class of Optimal High-Order Strong-Stability-Preserving Time Discretization Methods”. In: SIAM Journal on Numerical Analysis 40.2 (2002), pp. 469–491. doi: 10.1137/S0036142901389025.

• [81] B. Engquist and S. Osher. “Stable and Entropy Satisfying Approximations for Transonic Flow Calculations”. In: Mathematics of Computation 34.149 (1980), pp. 45–75. doi: 10.2307/2006220.

• [82] M. G. Crandall and P.-L. Lions. “Two Approximations of Solutions of Hamilton-Jacobi Equations”. In: Mathematics of Computation 43 (1984), pp. 1–19. doi: 10.1090/S0025-5718-1984-0744921-8.

• [83] S. K. Godunov and I. Bohachevsky. “Finite Difference Method for Numerical Computation of Discontinuous Solutions of the Equations of Fluid Dynamics”. In: Matematičeskij sbornik 47(89).3 (1959), pp. 271–306.

• [84] W. H. Press. Numerical Recipes: The Art of Scientific Computing. 3rd ed. Cambridge University Press, 1986.

• [85] B. Radjenović, J. K. Lee, and M. Radmilović-Radjenović. “Sparse Field Level Set Method for Non-Convex Hamiltonians in 3D Plasma Etching Profile Simulations”. In: Computer Physics Communications 174.2 (2006), pp. 127–132. doi: 10.1016/j.cpc.2005.09.010.

• [86] A. Harten and S. Osher. “Uniformly High-Order Accurate Nonoscillatory Schemes. I”. In: SIAM Journal on Numerical Analysis 24.2 (1987), pp. 279–309. doi: 10.1137/0724022.

• [87] A. Toifl, M. Quell, X. Klemenschits, P. Manstetten, A. Hössinger, S. Selberherr, and J. Weinbub. “The Level-Set Method for Multi-Material Wet Etching and Non-Planar Selective Epitaxy”. In: IEEE Access 8 (2020), pp. 115406–115422. doi: 10.1109/ACCESS.2020.3004136.

• [88] J. C. Strikwerda. Finite Difference Schemes and Partial Differential Equations, Second Edition. Society for Industrial and Applied Mathematics, 2004. doi: 10.1137/1.9780898717938.

• [89] R. Courant, K. Friedrichs, and H. Lewy. “Über die Partiellen Differenzengleichungen der Mathematischen Physik”. In: Mathematische Annalen 100 (1928), pp. 32–74.

• [90] D. L. Chopp. “Computing Minimal Surfaces via Level Set Curvature Flow”. In: Journal of Computational Physics 106.1 (1993), pp. 77–91. doi: 10.1006/jcph.1993.1092.

• [91] J. A. Sethian. “A Fast Marching Level Set Method for Monotonically Advancing Fronts”. In: Proceedings of the National Academy of Sciences 93.4 (1996), pp. 1591–1595. doi: 10.1073/PNAS.93.4.1591.

• [92] E. Rouy and A. Tourin. “A Viscosity Solutions Approach to Shape-From-Shading”. In: SIAM Journal on Numerical Analysis 29.3 (1992), pp. 867–884.

• [93] J. V. Gomez, D. Alvarez, S. Garrido, and L. Moreno. “Fast Methods for Eikonal Equations: An Experimental Survey”. In: IEEE Access 7 (2019), pp. 39005–39029. doi: 10.1109/ACCESS.2019.2906782.

• [94] J. Weinbub and A. Hössinger. “Comparison of the Parallel Fast Marching Method, the Fast Iterative Method, and the Parallel Semi-Ordered Fast Iterative Method”. In: Procedia Computer Science 80 (2016), pp. 2271–2275. doi: 10.1016/j.procs.2016.05.408.

• [95] D. Adalsteinsson and J. A. Sethian. “The Fast Construction of Extension Velocities in Level Set Methods”. In: Journal of Computational Physics 148.1 (1999), pp. 2–22. doi: 10.1006/jcph.1998.6090.

• [96] D. Adalsteinsson and J. A. Sethian. “A Level Set Approach to a Unified Model for Etching, Deposition, and Lithography I: Algorithms and Two-Dimensional Simulations”. In: Journal of Computational Physics 120 (1995), pp. 128–144. doi: 10.1006/JCPH.1995.1153.

• [97] D. Adalsteinsson and J. A. Sethian. “A Level Set Approach to a Unified Model for Etching, Deposition, and Lithography II: Three-Dimensional Simulations”. In: Journal of Computational Physics 122 (1995), pp. 348–366. doi: 10.1006/JCPH.1995.1221.

• [98] M. Quell, A. Toifl, A. Hössinger, S. Selberherr, and J. Weinbub. “Parallelized Level-Set Velocity Extension Algorithm for Nanopatterning Applications”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2019, pp. 335–338. doi: 10.1109/SISPAD.2019.8870482.

• [99] A. L. Magna and G. Garozzo. “Factors Affecting Profile Evolution in Plasma Etching of SiO2 : Modeling and Experimental Verification”. In: Journal of The Electrochemical Society 150 (2003), F178–F185. doi: 10.1149/1.1602084.

• [100] F. Rodrigues, L. F. Aguinsky, A. Toifl, A. Scharinger, A. Hössinger, and J. Weinbub. “Surface Reaction and Topography Modeling of Fluorocarbon Plasma Etching”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2021, pp. 229–232. doi: 10.1109/SISPAD54002.2021.9592583.

• [101] L. F. Aguinsky, F. Rodrigues, G. Wachter, M. Trupke, U. Schmid, A. Hössinger, and J. Weinbub. “Phenomenological Modeling of Low-Bias Sulfur Hexafluoride Plasma Etching of Silicon”. In: Solid-State Electronics 191 (2022), p. 108262. doi: 10.1016/j.sse.2022.108262.

• [102] M. J. Kushner. “Hybrid Modelling of Low Temperature Plasmas for Fundamental Investigations and Equipment Design”. In: Journal of Physics D: Applied Physics 42.19 (2009), p. 194013. doi: 10.1088/0022-3727/42/19/194013.

• [103] P. Manstetten. “Efficient Flux Calculations for Topography Simulation”. Doctoral Dissertation. TU Wien, 2018. doi: 10.34726/hss.2018.57263.

• [104] O. Ertl and S. Selberherr. “A Fast Void Detection Algorithm for Three-Dimensional Deposition Simulation”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2009, pp. 174–177. doi: 10.1109/SISPAD.2009.5290221.

• [105] K. Bean. “Anisotropic Etching of Silicon”. In: IEEE Transactions on Electron Devices 25.10 (1978), pp. 1185–1193. doi: 10.1109/T-ED.1978.19250.

• [106] I. Zubel. “Anisotropic Etching of Si”. In: Journal of Micromechanics and Microengineering 29.9 (2019), p. 093002. doi: 10.1088/1361-6439/ab2b8d.

• [107] P. Manstetten, A. Hössinger, J. Weinbub, and S. Selberherr. “Accelerated Direct Flux Calculations Using an Adaptively Refined Icosahedron”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2017, pp. 73–76. doi: 10.23919/SISPAD.2017.8085267.

• [108] T. S. Cale, G. B. Raupp, and T. H. Gandy. “Free Molecular Transport and Deposition in Long Rectangular Trenches”. In: Journal of Applied Physics 68.7 (1990), pp. 3645–3652. doi: 10.1063/1.346328.

• [109] R. Y. Rubinstein and D. P. Kroese. Simulation and the Monte Carlo Method. 3rd. Wiley, 2016.

• [110] P. Manstetten, J. Weinbub, A. Hössinger, and S. Selberherr. “Using Temporary Explicit Meshes for Direct Flux Calculation on Implicit Surfaces”. In: Procedia Computer Science 108 (2017), pp. 245–254. doi: 10.1016/j.procs.2017.05.067.

• [111] S. J. Ruuth. “A Diffusion-Generated Approach to Multiphase Motion”. In: Journal of Computational Physics 145.1 (1998), pp. 166–192. doi: 10.1006/jcph.1998.6028.

• [112] K. Smith, F. Solis, and D. Chopp. “A Projection Method for Motion of Triple Junctions by Level Sets”. In: Interfaces and Free Boundaries 4.3 (2002), pp. 263–276. doi: 10.4171/IFB/61.

• [113] H. Li, Y. Yap, J. Lou, and Z. Shang. “Numerical Modelling of Three-Fluid Flow Using the Level-Set Method”. In: Chemical Engineering Science 126 (2015), pp. 224–236. doi: 10.1016/j.ces.2014.11.062.

• [114] O. Ertl and S. Selberherr. “A Fast Level Set Framework for Large Three-Dimensional Topography Simulations”. In: Computer Physics Communications 180.8 (2009), pp. 1242–1250. doi: 10.1016/j.cpc.2009.02.002.

• [115] G. Hager and G. Wellein. Introduction to High Performance Computing for Scientists and Engineers. 1st. Chapman and Hall/CRC, 2010. doi: 10.1201/EBK1439811924.

• [116] Mahapatra, N. R. and Venkatrao, Balakrishna. “The Processor-Memory Bottleneck: Problems and Solutions”. In: XRDS 5.3 (1999), 2–es. doi: 10.1145/357783.331677.

• [117] Null, L. and Lobur, J. The Essentials of Computer Organization and Architecture. 5th. Jones and Bartlett Publishers, 2006.

• [118] G. M. Amdahl. “Validity of the Single Processor Approach to Achieving Large Scale Computing Capabilities”. In: Proceedings of the Spring Joint Computer Conference (AFIPS). 1967, pp. 483–485. doi: 10.1145/1465482.1465560.

• [119] J. L. Gustafson. “Reevaluating Amdahl’s Law”. In: Communications of the ACM 31.5 (1988), pp. 532–533. doi: 10.1145/42411.42415.

• [120] Vienna Scientific Cluster. https://vsc.ac.at/; Accessed: 2023-5-18. 2022.

• [121] S. V. Process. https://www.silvaco.com/tcad/victory-process-3d/; Accessed: 2023-5-18. 2022.

• [122] W. Schroeder, K. Martin, and B. Lorensen. The Visualization Toolkit. Kitware, 2006.

• [123] The CGAL Project. CGAL User and Reference Manual. 4.12.1. CGAL Editorial Board, 2018.

• [124] F. Rudolf, J. Weinbub, K. Rupp, and S. Selberherr. “The Meshing Framework ViennaMesh for Finite Element Applications”. In: Journal of Computational and Applied Mathematics 270 (2014), pp. 166–177. doi: 10.1016/j.cam.2014.02.005.

• [125] Intel Embree. https://www.embree.org/; Accessed:2023-5-18. 2022.

• [126] Y. Liu, F. Kong, and F. Yan. “Level Set Based Shape Model for Automatic Linear Feature Extraction from Satellite Imagery”. In: Sensors and Transducers 159.11 (2013), pp. 39–45.

• [127] B. Beddad and K. Hachemi. “Brain Tumor Detection by Using a Modified FCM and Level Set Algorithms”. In: Proceedings of the International Conference on Control Engineering Information Technology (CEIT). 2016, pp. 1–5. doi: 10.1109/CEIT.2016.7929114.

• [128] N. Christoff, A. Manolova, L. Jorda, S. Viseur, S. Bouley, and J.-L. Mari. “Level-Set Based Algorithm for Automatic Feature Extraction on 3D Meshes: Application to Crater Detection on Mars”. In: Proceedings of the Computer Vision and Graphics Conference (ICCVG). 2018, pp. 103–114. doi: 10.1007/978-3-030-00692-1_10.

• [129] S. Popinet. “Numerical Models of Surface Tension”. In: Annual Review of Fluid Mechanics 50 (2018), pp. 49–75. doi: 10.1146/annurev-fluid-122316-045034.

• [130] C. Lenz, A. Scharinger, M. Quell, P. Manstetten, A. Hössinger, and J. Weinbub. “Evaluating Parallel Feature Detection Methods for Implicit Surfaces”. In: Proceedings of the Austrian-Slovenian HPC Meeting (ASHPC). 2021, p. 31. doi: 10.3359/2021hpc.

• [131] C. Dorai and A. Jain. “COSMOS-A Representation Scheme for 3D Free-Form Objects”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence 19.10 (1997), pp. 1115–1130. doi: 10.1109/34.625113.

• [132] M. Abramowitz and I. A. Stegun. Handbook of Mathematical Functions: With Formulas, Graphs, and Mathematical Tables. Dover Publications, 1974.

• [133] B. Houston, M. B. Nielsen, C. Batty, O. Nilsson, and K. Museth. “Hierarchical RLE Level Set”. In: ACM Transactions on Graphics 25 (2006), pp. 151–175. doi: 10.1145/1122501.1122508.

• [134] L. Filipović, O. Ertl, and S. Selberherr. “Parallelization Strategy for Herarchical Run Length Encoded Data Structures”. In: Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks (PDCN). 2011, pp. 131–138. doi: 10.2316/P.2011.719-045.

• [135] J. Peng, Y. Qi, H.-C. Lo, P. Zhao, C. Yong, J. Yan, X. Dou, H. Zhan, Y. Shen, S. Regonda, O. Hu, H. Yu, M. Joshi, C. Adams, R. Carter, and S. Samavedam. “Source/Drain eSiGe Engineering for FinFET Technology”. In: Semiconductor Science and Technology 32.9 (2017), p. 094004. doi: 10.1088/1361-6641/aa7d3f.

• [136] Ted J. Hubbard. “MEMS Design: The Geometry of Silicon Micromachining”. PhD Thesis. California Institute of Technology, 1994. doi: 10.7907/TK4C-M144.

• [137] H. Jang, S. Koo, D.-S. Byeon, Y. Choi, and D.-H. Ko. “Facet Evolution of Selectively Grown Epitaxial Si1−x Gex Fin Layers in sub-100 nm Trench Arrays”. In: Journal of Crystal Growth 532 (2020), p. 125429. doi: 10.1016/j.jcrysgro.2019.125429.

• [138] Z. Yang, J. Ming, C. Qiu, M. Li, and X. He. “A Multigrid Multilevel Monte Carlo Method for Stokes–Darcy Model with Random Hydraulic Conductivity and Beavers–Joseph Condition”. In: Journal of Scientific Computing 90 (2022). doi: 10.1007/s10915-021-01742-2.

• [139] W. Joppich and S. Mijalković. Multigrid Methods for Process Simulation. 1st. Springer, 1993. doi: 10.1007/978-3-7091-9253-5.

• [140] M. E. Hubbard. “Adaptive Mesh Refinement for Three-Dimensional Off-Line Tracer Advection over the Sphere”. In: International Journal for Numerical Methods in Fluids 40.3-4 (2002), pp. 369–377. doi: 10.1002/fld.320.

• [141] S. L. Cornford, D. F. Martin, V. Lee, A. J. Payne, and E. G. Ng. “Adaptive Mesh Refinement Versus Subgrid Friction Interpolation in Simulations of Antarctic Ice Dynamics”. In: Annals of Glaciology 57.73 (2016), pp. 1–9. doi: 10.1017/aog.2016.13.

• [142] F. Löffler, Z. Cao, S. R. Brandt, and Z. Du. “A new Parallelization Scheme for Adaptive Mesh Refinement”. In: Journal of Computational Science 16 (2016), pp. 79–88. doi: 10.1016/j.jocs.2016.05.003.

• [143] A. Talpaert. “Direct Numerical Simulation of Bubbles with Adaptive Mesh Refinement with Distributed Algorithms”. PhD Thesis. Université Paris Sacla, 2017.

• [144] R. A. Trompert, J. G. Verwer, and J. G. Blom. “Computing Brine Transport in Porous Media with an Adaptive-Grid Method”. In: International Journal for Numerical Methods in Fluids 16.1 (1993), pp. 43–63. doi: 10.1002/fld.1650160104.

• [145] C. Lenz, A. Toifl, A. Hössinger, and J. Weinbub. “Curvature-Based Feature Detection for Hierarchical Grid Refinement in Epitaxial Growth Simulations”. In: Proceedings of the Joint International EUROSOI Workshop and International Conference on Ultimate Integration on Silicon (EUROSOI-ULIS). 2021, pp. 109–110.

• [146] K. Museth. “VDB: High-Resolution Sparse Volumes with Dynamic Topology”. In: ACM Transactions on Graphics 32.3 (2013). doi: 10.1145/2487228.2487235.

• [147] M. Quell. “Parallel Velocity Extension and Load-Balanced Re-Distancing on Hierarchical Grids for High Performance Process TCAD”. Doctoral Dissertation. TU Wien, 2022. doi: 10.34726/hss.2022.97084.

• [148] M. Berger and I. Rigoutsos. “An Algorithm for Point Clustering and Grid Generation”. In: IEEE Transactions on Systems, Man and Cybernetics 21.5 (1991), pp. 1278–1286. doi: 10.1109/21.120081.

• [149] M. Quell, G. Diamantopoulos, A. Hössinger, and J. Weinbub. “Shared-Memory Block-Based Fast Marching Method for Hierarchical Meshes”. In: Journal of Computational and Applied Mathematics 392 (2021), p. 113488. doi: 10.1016/j.cam.2021.113488.

• [150] C. Lenz, P. Manstetten, L. F. Aguinsky, F. Rodrigues, A. Hössinger, and J. Weinbub. “Automatic Grid Refinement for Thin Material Layer Etching in Process TCAD Simulations”. In: Solid-State Electronics 200 (2023), p. 108534. doi: 10.1016/j.sse.2022.108534.

• [151] C. Lenz, P. Manstetten, A. Hössinger, and J. Weinbub. “Automatic Grid Refinement for Thin Material Layer Etching in Process TCAD Simulations”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2022, pp. 11–12.

• [152] S. Zhang, Z. Gong, J. J. McKendry, S. Watson, A. Cogman, E. Xie, P. Tian, E. Gu, Z. Chen, G. Zhang, A. E. Kelly, R. K. Henderson, and M. D. Dawson. “CMOS-Controlled Color-Tunable Smart Display”. In: IEEE Photonics Journal 4 (2012), pp. 1639–1646. doi: 10.1109/JPHOT.2012.2212181.

• [153] P. Lindstrom and G. Turk. “Fast and Memory Efficient Polygonal Simplification”. In: Proceedings of the Conference IEEE Visualization (VIS). IEEE Computer Society Press, 1998, pp. 279–286. doi: 10.1109/VISUAL.1998.745314.

• [154] M. Garland and P. S. Heckbert. “Surface Simplification Using Quadric Error Metrics”. In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference (SIGGRAPH). 1997, pp. 209–216. doi: 10.1145/258734.258849.

• [155] H. Borouchaki and P. Frey. “Simplification of Surface Mesh using Hausdorff Envelope”. In: Computer Methods in Applied Mechanics and Engineering 194.48 (2005), pp. 4864–4884. doi: 10.1016/j.cma.2004.11.016.

• [156] S. J. Kim, C. H. Kim, and D. Levin. “Surface Simplification Using a Discrete Curvature Norm”. In: Computers & Graphics 26.5 (2002), pp. 657–663. doi: 10.1016/S0097-8493(02)00121-8.

• [157] C. Lenz, A. Scharinger, A. Hössinger, and J. Weinbub. “A Novel Surface Mesh Coarsening Method for Flux-Dependent Topography Simulations of Semiconductor Fabrication Processes”. In: Proceedings of the International Conferences on Scientific Computing in Electrical Engineering (SCEE). 2020, pp. 99–100.

• [158] C. Lenz, A. Scharinger, P. Manstetten, A. Hössinger, and J. Weinbub. “A Novel Surface Mesh Simplification Method for Flux-Dependent Topography Simulations of Semiconductor Fabrication Processes”. In: Scientific Computing in Electrical Engineering. Ed. by M. van Beurden, N. Budko, and W. Schilders. Springer, 2021, pp. 73–81. doi: 10.1007/978-3-030-84238-3_8.

• [159] H. Hoppe, T. DeRose, T. Duchamp, J. McDonald, and W. Stuetzle. “Mesh Optimization”. In: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference (SIGGRAPH). 1993, pp. 19–26. doi: 10.1145/166117.166119.

• [160] T. Birsan and D. Tiba. “One Hundred Years Since the Introduction of the Set Distance by Dimitrie Pompeiu”. In: Proceedings of the Conference on System Modeling and Optimization (CSMO). 2006, pp. 35–39.

• [161] R. Straub. “Exact Computation of the Hausdorff Distance Between Triangular Meshes”. In: EG Short Papers. Proceedings of the Conference of The Eurographics Association (EG), 2007. doi: 10.2312/egs.20071023.

Own Publications

Journal Articles

• [1] Lenz, C., Aguinsky, L. F., Hössinger, A., Weinbub, J., “A Complementary Topographic Feature Detection Algorithm Based on Surface Curvature for Three-Dimensional Level-Set Functions”. In: Journal of Scientific Computing 94 (2023), p. 21. doi: 10.1007/s10915-023-02133-5.

• [2] Lenz, C., Manstetten, P., Aguinsky, L. F., Rodrigues, F., Hössinger, A., Weinbub, J., “Automatic Grid Refinement for Thin Material Layer Etching in Process TCAD Simulations”. In: Solid-State Electronics 200 (2023), p. 108534. doi: 10.1016/j.sse.2022.108534.

• [3] Lenz, C., Toifl, A., Quell, M., Rodrigues, F., Hössinger, A., Weinbub, J., “Curvature Based Feature Detection for Hierarchical Grid Refinement in TCAD Topography Simulations”. In: Solid-State Electronics 191 (2022), p. 108258. doi: 10.1016/j.sse.2022.108258.

Book Contributions

• [4] Lenz, C., Scharinger, A., Manstetten, P., Hössinger, A., Weinbub, J., “A Novel Surface Mesh Simplification Method for Flux-Dependent Topography Simulations of Semiconductor Fabrication Processes”. In: Scientific Computing in Electrical Engineering. Ed. by M. van Beurden, N. Budko, and W. Schilders. Springer, 2021, pp. 73–81. doi: 10.1007/978-3-030-84238-3_8.

• [5] Lenz, C., Toifl, A., Hössinger, A., Weinbub, J., “Curvature Based Feature Detection for Hierarchical Grid Refinement in TCAD Topography Simulations”. In: Joint International EUROSOI Workshop and International Conference on Ultimate Integration on Silicon (EUROSOI-ULIS). Ed. by B. Cretu. IEEE, 2021, pp. 1–4. doi: 10.1109/EuroSOI-ULIS53016.2021.9560690.

Conference Contributions

• [6] Lenz, C., Manstetten, P., Hössinger, A., Weinbub, J., “Automatic Grid Refinement for Thin Material Layer Etching in Process TCAD Simulations”. In: Proceedings of the International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). 2022, pp. 11–12.

• [7] Lenz, C., Toifl, A., Hössinger, A., Weinbub, J., “Curvature-Based Feature Detection for Hierarchical Grid Refinement in Epitaxial Growth Simulations”. In: Proceedings of the Joint International EUROSOI Workshop and International Conference on Ultimate Integration on Silicon (EUROSOI-ULIS). 2021, pp. 109–110.

• [8] Lenz, C., Scharinger, A., Quell, M., Manstetten, P., Hössinger, A., Weinbub, J., “Evaluating Parallel Feature Detection Methods for Implicit Surfaces”. In: Proceedings of the Austrian-Slovenian HPC Meeting (ASHPC). 2021, p. 31. doi: 10.3359/2021hpc.

• [9] Lenz, C., Scharinger, A., Hössinger, A., Weinbub, J., “A Novel Surface Mesh Coarsening Method for Flux-Dependent Topography Simulations of Semiconductor Fabrication Processes”. In: Proceedings of the International Conferences on Scientific Computing in Electrical Engineering (SCEE). 2020, pp. 99–100.

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