TSV (Through Silicon Via) is a key technology for three-dimensional (3D) packaging due to its unique vertical interconnection method. However, its particular manufacturing process of-ten leads to internal defects, such as gaps, bottom voids, filling missing, which are usually difficult to be detected by common means. In order to discover the internal defect of TSV packaging effectively, a novel non-destructive inspection method based on built-in integrated temperature sensor array is proposed. The relationship between temperature distribution and internal defect is dis-covered and then corresponding sensor array layout is designed. The simulation analysis shows that this kind of sensor array can recognize the internal TSV defect. And supervised machine learning is used to construct the classification model by which different defects can be found and classified with relatively high accuracy, and the classification accuracy rate can reach 95.625%. Experiments were conducted and the rationality of this built-in sensing array was verified. The research provides a non-destructive testing method for TSV internal defects based on bulit-in-integrated sensors, and verifies the feasibility of sensor arrangement through simulation, laying a foundation for the realization of later TSV design optimization.
Aeroengines are the core components of an aircraft; therefore, their health determines flight safety. Currently, owing to their complex structure and problems associated with their various detection parameters, predicting the remaining useful life (RUL) of aeroengines is very important to ensure their safety and reliability. In this paper, we propose a new hybrid method based on convolutional neural networks (CNN), timing convolutional neural networks (TCN), and the multi-head attention mechanism. Firstly, an CNN-TCN model is established for multi-dimensional features, in which two layers of the CNN extract features of multi-dimensional input data, and the TCN process the timing features. Subsequently, the outputs of multiple CNN-TCNs are weighted using the multi-head attention mechanism, and the results are stitched together. Next, we compare the root mean square error (RMSE) and scores of various RUL prediction methods to show the superiority of the proposed method. The results showed that compared with previous research results, the RMSE and Score of FD001 decreased by 10.87% and 42.57%, respectively, whereas those of FD003 decreased by 14.13% and 58.15%, respectively.
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