Our findings provide important information for future music-intervention planners to improve the design and processes that will benefit patients in such programs.

Simple regression cannot wholly analyze large-scale wafer backside wall chipping because the wafer grinding process encounters many problems, such as collected data missing, data showing a non-linear distribution, and correlated hidden parameters lost. The objective of this study is to propose a novel approach to solving this problem. First, this study uses time series, random forest, importance analysis, and correlation analysis to analyze the signals of wafer grinding to screen out key grinding parameters. Then, we use PCA and Barnes-Hut t-SNE to reduce the dimensionality of the key grinding parameters and compare their corresponding heat maps to find out which dimensionality reduction method is more sensitive to the chipping phenomenon. Finally, this study imported the more sensitive dimensionality reduction data into the Data Driven-Bidirectional LSTM (DD-BLSTM) model for training and predicting the wafer chipping. It can adjust the key grinding parameters in time to reduce the occurrence of large-scale wafer chipping and can effectively improve the degree of deterioration of the grinding blade. As a result, the blades can initially grind three pieces of the wafers without replacement and successfully expand to more than eight pieces of the wafer. The accuracy of wafer chipping prediction using DD-BLSTM with Barnes-Hut t-SNE dimensionality reduction can achieve 93.14%.

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