As part of the semiconductor industry "contamination-free manufacturing" effort, significant emphasis has been placed on reducing potential sources of contamination from process equipment and process equipment components. Process tools contain process chambers and components that are exposed to the process environment or process chemistry and in some cases are in direct contact with production wafers. Any contamination from these sources must be controlled or eliminated in order to maintain high process yields, device performance, and device reliability. This paper discusses new nondestructive analytical methods for quantitative measurement of the cleanliness of metal, quartz, polysilicon and ceramic components that are used in process equipment tools. The goal of these new procedures is to measure the effectiveness of cleaning procedures and to verify whether a tool component part is sufficiently clean for installation and subsequent routine use in the manufacturing line. These procedures provide a reliable "qualification method" for tool component certification and also provide a routine quality control method for reliable operation of cleaning facilities. Cost advantages to wafer manufacturing include higher yields due to improved process cleanliness and elimination of yield loss and downtime resulting from the installation of "bad" components in process tools. We also discuss a representative example of wafer contamination having been linked to a specific process tool component.

Short-term wind power forecasting is crucial for updating the wind power trading strategy, equipment protection and control regulation. To solve the difficulty surrounding the instability of the statistical model and the time-consuming nature of the physical model in short-term wind power forecasting, two innovative wind field reconstruction methods combining CFD and a reduced-order model were developed. In this study, POD and Tucker decomposition were employed to obtain the spatial–temporal information correlation of 2D and 3D wind fields, and their inverse processes were combined with sparse sensing to reconstruct multi-dimensional unsteady wind fields. Simulation and detailed discussion were performed to verify the practicability of the proposed algorithms. The simulation results indicate that the wind speed distributions could be reconstructed with reasonably high accuracy (where the absolute velocity relative error was less than 0.8%) using 20 sensors (which only accounted for 0.04% of the total data in the 3D wind field) based on the proposed algorithms. The factors influencing the results of reconstruction were systematically analyzed, including all-time steps, the number of basis vectors and 4-mode dimensions, the diversity of CFD databases, and the reconstruction time. The results indicated that the reconstruction time could be shortened to the time interval of data acquisition to synchronize data acquisition with wind field reconstruction, which is of great significance in the reconstruction of unsteady wind fields. Although there are still many studies to be carried out to achieve short-term predictions, both unsteady reconstruction methods proposed in this paper enable a new direction for short-term wind field prediction.

A new positioning method in mobile networks is presented. Based on the data fusion technology, it processes multi-layer information fusion for the location estimates achieved by the Chan algorithm, which increases mobile positioning accuracy effectively by only using measured difference of arriving (TDOA) signals. The method is simple and practical, especially when the location estimates are corrupted by the non-line-of-sight (NLOS) error. It not only has high positioning accuracy, but also reduces the location failure probability. Results from computer simulation show that the proposed method is effective in various environments.

No abstract

No abstract