Study on the behavior of clusters in the physical recovery of GaAs scrap.

Gallium arsenide (GaAs) is the most widely used second-generation semiconductor material. However, a large amount of GaAs scrap is generated at various stages of the GaAs wafer production process. Volatile GaAs clusters are inevitably generated during the process of GaAs vacuum thermal decomposition, resulting in lower purity of the recovered arsenic and the loss of gallium. In this study, thermodynamic analysis and dynamics simulation were combined to discuss the possibility of separating GaAs clusters and arsenic from a microscopic perspective. A vacuum thermal decomposition-directional condensation recovery process for GaAs scrap was proposed. By properly adjusting the separation parameters such as heating temperature, holding time and raw material size, high purity of gallium (99.99%) and arsenic (99.5%) were directly recovered under a system pressure of 1 Pa, heating temperature of 1323 K, holding time of 3 h, and GaAs scrap size of 2.5 cm. GaAs clusters were also recovered in powder form. The problem of difficult separation of GaAs clusters from arsenic was effectively solved by this method, and the purity of recovered arsenic was greatly improved. No additives are required and no waste liquid or gas emission in the whole process. The complexity of subsequent arsenic purification operations and the threat of arsenic containing waste to the environment were reduced as well.

Fully automated registration of 3D CT data to CAD model for surface deviation measurement.

The aim of this study is to present a fully automated registration algorithm that allows for alignment and errors analysis of the 3D surface model obtained from industrial computed tomography (CT) images with the computer-aided design (CAD) model. First, two pre-processing steps are executed by the algorithm namely, CAD model subdivision and representing models. Next, two improved registration procedures are applied including covariance descriptors-based coarse registration with a novel and automatic calibration, followed by a fine registration technique that utilizes an improved iterative closest points (ICP) algorithm, which is what we proposed with a novel estimation method for registration error. Finally, using a novel strategy that we proposed for error display, the quantitative data analysis results can simultaneously estimate both positive and negative deviation of the surface registration errors more precisely and fully expressed. Comparing to the original ICP algorithm, the quantitative data of experimental results demonstrate that the average registration errors of carburetors and valves are reduced by 0.80 millimeter at least. Therefore, this study demonstrates that the proposed new algorithm is not only capable of fully automating the registration of 3D surface model to a CAD model but also beneficial for quantitatively determining the surface manufacturing error more precisely.