Non-convex profile evolution in two dimensions using volume of fluids

A new Volume of Fluid (VoF) method is applied to the problem surface evolution in two dimensions. The VoF technique is applied to problems that are representative of those that arise in semiconductor manufacturing, specifically photolithography and ion milling. The types of surface motion considered are those whose etch rates vary as a function of both surface position and orientation. Functionality is demonstrated for etch rates that are (non?)-convex in regard to surface orientation. A new method of computing surface curvature using divided differences of the volume fractions is also introduced and applied to the advancement of surfaces as a vanishing diffusive term

Non-convex profile evolution in two dimensions using volume of fluids

A new Volume of Fluid (VoF) method is applied to the problem of surface evolution in two dimensions (2D). The VoF technique is applied to problems that are representative of those that arise in semiconductor manufacturing, specifically photolithography and ion-milling. The types of surface motion considered are those whose etch rates vary as a function of both surface position and orientation. Functionality is demonstrated for etch rates that are non-convex in regard to surface orientation. A new method of computing surface curvature using divided differences of the volume fractions is also introduced, and applied to the advancement of surfaces as a vanishing diffusive term

Two new methods for simulating photolithography development in 3D

Two methods are presented for simulating the development of photolithographic profiles during the resist dissolution phase. These algorithms are the volume-of-fluid algorithm, and the steady level-set algorithm. They are compared with the ray-trace, cell, and level-set techniques employed in SAMPLE-3D. The volume-of-fluid algorithm employs an Euclidean Grid with volume fractions. At each time step, the surface is reconstructed by computing an approximation of the tangent plane of the surface in each cell that contains a value between 0 and 1. The geometry constructed in this manner is used to determine flow velocity vectors and the flux across each edge. The material is then advanced by a split advection scheme. The steady Level Set algorithm is an extension of the Iterative Level Set algorithm. The steady Level Set algorithm combines Fast Level Set concepts and a technique for finding zero residual solutions to the ( ) function. The etch time for each cell is calculated in a time ordered manner. Use of heap sorting data structures allows the algorithm to execute extremely quickly. Comparisons of the methods have been performed and results shown

Fast and Accurate Surface Normal Integration on Non-Rectangular Domains

International audienceThe integration of surface normals for the purpose of computing the shape of a surface in 3D space is a classic problem in computer vision. However, even nowadays it is still a challenging task to devise a method that is flexible enough to work on non-trivial computational domains with high accuracy, robustness, and computational efficiency. By uniting a classic approach for surface normal integration with modern computational techniques, we construct a solver that fulfils these requirements. Building upon the Poisson integration model, we use an iterative Krylov subspace solver as a core step in tackling the task. While such a method can be very efficient, it may only show its full potential when combined with suitable numerical preconditioning and problem-specific initialisation. We perform a thorough numerical study in order to identify an appropriate preconditioner for this purpose. To provide suitable initialisation, we compute this initial state using a recently developed fast marching integrator. Detailed numerical experiments illustrate the benefits of this novel combination. In addition, we show on real-world photometric stereo datasets that the developed numerical framework is flexible enough to tackle modern computer vision applications