Cleanroom contamination and its impact on the performance of devices are beginning to be investigated due to the increasing sensitivity of the semiconductor manufacturing process to airborne molecular contamination (AMC). A clean bench was equipped with different filter modules and then most AMC in the cleanroom and in the clean bench was detected through air-sampling and wafer-sampling experiments. Additionally, the effect of AMC on device performance was examined by electrical characterization. A combination of the NEUROFINE PTFE filter and chemical filters was found to control metal, organic, and inorganic contamination. We believe that the new combination of filters can be used to improve the manufacturing environment of devices, which are being continuously shrunk to the nanometer scale.
Tide is a phenomenon of water level change caused by gravity. Tidal level forecasting is not only a key theoretical topic but also crucial in coastal and ocean engineering applications. The waiting time before a cargo ship enters a port affects the efficiency of cargo transportation, the tidal difference affects the establishment of turbine generators, and an excessive tidal water level reduces vessel safety. With the proliferation of information technology, the application of deep learning models in the analysis and study of hydrological problems has become increasingly common. This study proposed a deep learning model to predict the tidal water level. A forecasting model was developed on the basis of the long short-term memory (LSTM) recurrent neural network for predicting the water levels of 17 harbors in Taiwan. Tidal water level data for 21 years were collected from different observation stations. To objectively evaluate model performance, the developed model was compared with six other forecasting models in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the forecasting results. The results indicated that the LSTM model had the lowest forecasting error for the tidal water level for up to 30 days. The average MAPE and RMSE values for the developed model were 6.97% and 0.049 m, respectively; thus, the model could effectively reduce the overlapping problems caused by machine learning methods in continuous forecasting. INDEX TERMS-Deep learning, long short-term memory, tidal level forecasting, time series.
The major shipping routes in the North Pacific (NP) and North Atlantic (NA) are analyzed via ship-reported records compiled by the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The shipping route seasonal characteristics and associated climatic features are also examined. In the NP, the dominant cross-basin route takes a great-circle path between East Asia and North America along 54°N north of the Aleutian Islands throughout the year. This route penetrates the Aleutian low center where ocean waves and winds are relatively weaker than those in the low's southern section south of 50°N. Moreover, the Earth's spherical shape makes a higher-latitude route shorter in navigational distance across the NP than a lower-latitude route. Two additional mid-latitude routes through the 40° -50°N region appear in summer when the Aleutian low vanishes. In the NA, the major shipping routes form an X-shaped pattern in the oceans south of 40°N to connect North America/the Panama Canal and the Mediterranean Sea/the British Isles and Europe. These major shipping routes are far from the influence of the Icelandic low and thus are used throughout the year due to the stability in marine conditions and their general efficiency. A third and more zonal route appears to the north of the X-shaped routes in the 40° -50°N region. Weak influence from the Icelandic low on marine conditions during summer and spring means that more ships take this route in summer and spring than in winter and fall.
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