Transient liquid phase (TLP) bonding is a promising interconnection technology for high-temperature electronic packaging. In this paper, the effect of Cu particles on the microstructure and shear property of Cu/In-xCu/Cu TLP joints was investigated. The results show that Cu/In-xCu/Cu joint is mainly composed of an In and Cu 11 In 9 phase after bonded for 60 min. A small amount of Cu 2 In phase forms at the interface of Cu/Cu 11 In 9 with bonding time exceeding 300 min. The bonding efficiency and shear property of In-Cu mixed particle solder joint are superior to that of the In foil solder joint, because the bonding time was reduced and the shear strength of the solder joint was improved. The phase composition of Cu 11 In 9 and Cu 2 In in the joint increased, the porosity decreased and the shear strength increased with increasing Cu content. When the Cu content of the In-xCu solder was 45 wt.%, the shear strength of the Cu/In-45Cu/Cu joint reached the peak value of 15.7 MPa.
Event extraction is one of the most challenging tasks in information extraction. It is a common phenomenon where multiple events exist in the same sentence. However, extracting multiple events is more difficult than extracting a single event. Existing event extraction methods based on sequence models ignore the interrelated information between events because the sequence is too long. In addition, the current argument extraction relies on the results of syntactic dependency analysis, which is complicated and prone to error transmission. In order to solve the above problems, a joint event extraction method based on global event-type guidance and attention enhancement was proposed in this work. Specifically, for multiple event detection, we propose a globaltype guidance method that can detect event types in the candidate sequence in advance to enhance the correlation information between events. For argument extraction, we converted it into a table-filling problem, and proposed a tablefilling method of the attention mechanism, that is simple and can enhance the correlation between trigger words and arguments. The experimental results based on the ACE 2005 dataset showed that the proposed method achieved 1.6% improvement in the task of event detection, and obtained state-of-the-art results in the argument extraction task, which proved the effectiveness of the method.
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