The authors present a new method to determine film thicknesses and sticking coefficients (SC) of precursor molecules for atomic layer deposition (ALD) in high aspect ratio three dimensional (3D) geometries as they appear in microelectromechanical system manufacturing. The method combines a specifically designed experimental test structure with the theoretical predictions from a novel 3D Monte Carlo process simulation for large structures. The authors exemplify our method using Al2O3 and SiO2 ALD processes. SCs for trimethylaluminium and bis-diethyl aminosilane (BDEAS) are extracted. The SC for BDEAS is determined for the first time.
We present a new simulation method predicting thicknesses of thin films obtained by atomic layer deposition in high aspect ratio 3D geometries as they appear in MEMS manufacturing. The method features a Monte-Carlo computation of film deposition in free molecular flow, as well as in the Knudsen and diffusive gas regime, applicable for large structures. We compare our approach to analytic and simulation results from the literature. The capability of the method is demonstrated by a comparison to experimental film thicknesses in a large 3D structure. Finally, the feasability to extract process parameters, i.e. sticking coefficients is shown.
Image processing techniques are widely used within automotive series production, including production of electronic control units (ECUs). Deep learning approaches have made rapid advances during the last years, but are not prominent in those industrial settings yet. One major obstacle is the lack of suitable training data. We adapt the recently developed method of domain randomization to our use case of 3D pose estimation of ECU housings. We create purely synthetic data with high visual diversity to train artificial neural networks (ANNs). This enables ANNs to estimate the 3D pose of a real sample part with high accuracy from a single low-resolution RGB image in a production-like setting. Requirements regarding measurement hardware are very low. Our entire setup is fully automated and can be transferred to related industrial use cases.
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