会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 53. 发明申请
    • DEFECT CLASSIFICATION METHOD AND APPARATUS, AND DEFECT INSPECTION APPARATUS
    • 缺陷分类方法和装置,以及缺陷检查装置
    • US20080075352A1
    • 2008-03-27
    • US11779905
    • 2007-07-19
    • HISAE SHIBUYAShunji MaedaAkira Hamamatsu
    • HISAE SHIBUYAShunji MaedaAkira Hamamatsu
    • G06K9/00
    • G06T7/0004G01N21/9501G01N21/956G06K9/03G06K9/6254G06K9/6282G06T2207/30148
    • A defect classification method to classify defects by using a classifier having a binary tree structure based on features of defects extracted from detected signals acquired from a defect inspection apparatus includes a classifier construction process for constructing the classifier by setting a branch condition including defect classes respectively belonging to groups located on both sides of the branch point, a feature to be used for branching, and a discriminant reference, for each branch point in the structure based on instruction of defect classes and feature data respectively associated therewith beforehand. The process includes a priority order specification process for previously specifying target classification performance of purity and accuracy for each defect class, whole and in worst case, with priority order, and an evaluation process for evaluating whether the specified target classification performance under the branching condition is satisfied and displaying a result of evaluation, every item.
    • 通过使用基于从缺陷检查装置获取的检测信号提取的缺陷的特征的具有二叉树结构的分类器对缺陷进行分类的缺陷分类方法包括:分类器构造处理,用于通过设置包括分别属于的缺陷类的分支条件来构建分类器 基于分支点两侧的组,用于分支的特征,以及基于与预先分别相关联的缺陷类别和特征数据的指示的结构中的每个分支点的判别参考。 该处理包括优先顺序指定处理,用于以优先级顺序预先指定每个缺陷类的全部和最坏情况下的纯度和精度的目标分类性能,以及用于评估分支条件下的指定目标分类性能是否为 满意并显示评估结果,每个项目。
    • 55. 发明申请
    • Method and apparatus for detecting pattern defects
    • 检测图案缺陷的方法和装置
    • US20060215902A1
    • 2006-09-28
    • US11319271
    • 2005-12-29
    • Hisae ShibuyaAkira HamamatsuYuji Takagi
    • Hisae ShibuyaAkira HamamatsuYuji Takagi
    • G06K9/00
    • G06K9/6221G01N21/9501G01N21/95607G01N2021/8854G01N2021/95676
    • With the objective of achieving defect kind training in a short period of time to teach classification conditions of defects detected as a result of inspecting a thin film device, according to one aspect of the present invention, there is provided a visual inspection method, and an apparatus therefor, comprising the steps of: detecting defects based on inspection images acquired by optical or electronic defect detection means, and at the same time calculating features of the defects; and classifying the defects according to classification conditions set beforehand, wherein said classification condition setting step further includes the steps of: collecting defect features over a large number of defects acquired beforehand from the defect detection step; sampling defects based on the distribution of the collected defect features over the large number of defects; and setting defect classification conditions based on the result of reviewing the sampled defects.
    • 为了在短时间内实现缺陷种类训练,目的在于教导检查薄膜装置的检测缺陷的分类条件,根据本发明的一个方面,提供一种目视检查方法, 其装置包括以下步骤:基于由光学或电子缺陷检测装置获取的检查图像检测缺陷,同时计算缺陷的特征; 并根据预先设定的分类条件对缺陷进行分类,其中所述分类条件设置步骤还包括以下步骤:从缺陷检测步骤预先获取的大量缺陷中收集缺陷特征; 基于收集的缺陷特征分布在大量缺陷上的采样缺陷; 并根据检查采样缺陷的结果设置缺陷分类条件。
    • 60. 发明授权
    • Method and device for monitoring the state of a facility
    • 监控设施状态的方法和装置
    • US08682824B2
    • 2014-03-25
    • US13383841
    • 2010-07-28
    • Hisae ShibuyaShunji Maeda
    • Hisae ShibuyaShunji Maeda
    • G06N5/00
    • G05B23/021G06N99/005
    • This invention provides method for detecting advance signs of anomalies, event signals outputted from the facility are used to create a separate mode for each operating state, a normal model is created for each mode, the sufficiency of learning data for each mode is checked, a threshold is set according to the results of said check, and anomaly identification is performed using said threshold. Also, for diagnosis, a frequency matrix is created in advance, with result events on the horizontal axis and cause events on the vertical axis, and the frequency matrix is used to predict malfunctions. Malfunction events are inputted as result events, and quantized sensor signals having anomaly measures over the threshold are inputted as cause events.
    • 本发明提供了用于检测异常的提前符号的方法,从设施输出的事件信号用于为每个操作状态创建单独的模式,为每个模式创建正常模型,检查每种模式的学习数据的充分性, 根据所述检查的结果设定阈值,使用所述阈值进行异常识别。 另外,为了进行诊断,预先创建频率矩阵,结果事件在水平轴上,导致垂直轴上的事件,频率矩阵用于预测故障。 作为结果事件输入故障事件,并且将具有超过阈值的异常测量的量化的传感器信号作为原因事件输入。