Simulation and inverse modeling of TEOS deposition processes using a fast level set method

Deposition and etching of silicon trenches is an important manufacturing step for state of the art memory cells. Understanding and simulating the transport of gas species and surface evolution enables to achieve void-less filling of deep trenches, to predict the resulting profiles, and thus to optimize process parameters with respect to manufacturing throughput and the quality of the resulting memory cells. For the simulation of the SiO₂ deposition process from TEOS (Tetraethoxysilane), the level set method was used in addition to physical models. The level set algorithm devised minimizes computational effort while ensuring high accuracy by intertwining narrow banding and extending the speed function. In order to make the predictions of the simulation more accurate, model parameters were extracted by comparing the step coverages of the deposited layers in the simulation with those of SEM (scanning electron microscope) images.