• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE transactions on industrial informatics >Cascaded Approach to Defect Location and Classification in Microelectronic Bonded Joints: Improved Level Set and Random Forest
【24h】

Cascaded Approach to Defect Location and Classification in Microelectronic Bonded Joints: Improved Level Set and Random Forest

机译：微电子粘合关节缺陷位置和分类的级联方法：改进水平集和随机林

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The monitoring of bonded joints in microelectronics packaging is generally done manually and offline. However, this method is inefficient and of low precision due to limited personal sensing and experience. This article proposes a hybrid cascade approach with an improved level set and a random forest to locate and automatically classify defective joints in microelectronics packaging. A grayscale variance-based pixel neighborhood is introduced to accurately locate the joint, and an improved gray projection is used to remove the redundant nonjoint area. We have used an improved level set algorithm to segment joint defects and extract their dominant features using KPCA. Finally, a random forest is used to classify the features extracted by KPCA and determine the defect categories. The results have indicated that the grayscale variance-based pixel neighborhood could effectively locate the joint and that KPCA could identify effective joint features. The accuracy of the random forest classification has reached 0.91, offering a novel solution for joint quality monitoring in microelectronics manufacturing.
机译：微电子包装中粘合接头的监测通常是手动和离线进行的。然而，由于有限的个人感知和经验，这种方法效率低下并且精度低。本文提出了一种具有改进水平集和随机林的混合级联方法，以定位和自动分类微电子包装中的有缺陷的关节。引入基于灰度方差的像素邻域以精确定位接头，并且使用改进的灰度投影来删除冗余非极佳区域。我们使用改进的级别集算法来分段接头缺陷，并使用KPCA提取其主导特征。最后，随机森林用于分类KPCA提取的功能并确定缺陷类别。结果表明，基于灰度方差的像素邻域可以有效地定位关节，并且KPCA可以识别有效的关节特征。随机森林分类的​​准确性已达到0.91，为微电子制造业的联合质量监测提供了一种新的解决方案。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号