Evolutionary atomistic methods for the simulation of surface processing with applications in micro engineering

CIC nanoGUNE Seminars

M. A. Gosálvez, DIPC and UPV, Donostia, Spain
nanoGUNE seminar room, Tolosa Hiribidea 76, Donostia - San Sebastian
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Evolutionary atomistic methods for the simulation of surface processing with applications in micro engineering Atomistic methods, such as Kinetic Monte Carlo (KMC) and Continuous Cellular Automata (CCA) are often used to describe the time evolution of complex surface processes. Traditionally, the reaction rates employed by these methods are obtained via a bottom-up procedure, involving _ab initio_ calculations of activation energies for the various competing atomistic processes as well as physical insight about the mesoscopic and macroscopic behavior of the system. As an alternative, a top-down approach is presented in this talk, whereby an Evolutionary Algorithm (EA) is used to determine optimal values of the reaction rates by directly comparing the macroscopic behavior of the simulated system with that from the experiments. The deterministic nature of the CCA method allows to illustrate the key aspects of the proposed procedure. This includes the particular features of the evolutionary search and the inherent need for extremely efficient simulations. As an example application, anisotropic wet etching in aqueous solutions offers both the complex atomistic nature and the relevant front propagation features, as well as a wide-spread use in the microfabrication of complex three-dimensional structures. Based on new, extensive experiments, the evolutionary CCA approach is validated by (i) describing the correct macroscopic etch rate distribution for different materials (silicon and quartz) in numerous etchants at different concentrations and temperatures, and (ii) performing fast, accurate, full 3D simulations of microengineering structures. This opens new opportunities for knowledge transfer to companies around the world. Similar efforts for the ongoing evolutionary KMC implementation are also described. If the time allows for it, major aspects about the computational efficiency will be described. A novel, fast, octree-based, parallel implementation of the CCA method will be presented based on performing the calculations in increasingly affordable, commodity Graphics Processing Units (GPUs), where we achieve simulation speeds that are two orders of magnitude faster than the equivalent, sequential implementation on a Central Processing Unit (CPU). For purely sequential architectures, a novel, fast implementation of the method will also be presented, based on keeping track of the front arrival time (or Predicted Reaction Time, PRT) and using a self-balanced binary search tree (SB-BST) for efficient access to the stored PRT values. This allows to draw relations between the CCA and the Level Set Method.