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Research on particle size identification method of residue in sealed electronic devices

机译：密封电子设备中残留物粒度识别方法的研究

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摘要

Residue is one of the main factors affecting the reliability of sealed electronic equipment. The traditional particle collision noise detection (PIND) method is only suitable for the detection of the presence or absence of residues, but the particle size identification of the residue is important for tracking the source of production and improving the process. In this paper, the endpoint detection algorithm based on spectrum variance is used to extract the residual pulse signal, which is beneficial to the concentration of characteristic quantities. Fisher’s discriminant method was used to conduct dimensionality reduction processing and clustering analysis of characteristic parameters, and BP neural network was used to realize the classification of residual particle size, and the recognition accuracy could reach 93.75%.
机译：残留物是影响密封电子设备可靠性的主要因素之一。传统的粒子碰撞噪声检测（PIND）方法仅适用于检测是否存在残留物，但是残留物的粒度识别对于跟踪生产来源和改进工艺非常重要。本文采用基于谱方差的端点检测算法提取残余脉冲信号，有利于特征量的集中。用Fisher判别法进行降维处理和特征参数聚类分析，并用BP神经网络实现残余粒径的分类，识别精度可达93.75％。

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