公开(公告)号：US20160092618A1

公开(公告)日：2016-03-31

申请号：US14841365

申请日：2015-08-31

Applicant: Applied Materials, Inc.

Inventor： Subrahmanyam Venkata Rama KOMMISETTI ,  Haw Jyue LUO ,  Jimmy ISKANDAR ,  Hsincheng LAI ,  Parris HAWKINS

IPC: G06F17/50 ,  G06F7/556

CPC classification number: G06F17/5009 ,  G05B23/0221 ,  G05B23/024 ,  G05B2219/37537 ,  G06F7/556 ,  G06F2207/556

Abstract: Embodiments disclosed herein include methods for reducing or eliminating the impact of tuning disturbances during prediction of lamp failure. In one embodiment, the method comprises monitoring data of a lamp module for a process chamber using one or more physical sensors disposed at different locations within the lamp module, creating virtual sensors based on monitoring data of the lamp module, and providing a prediction model for the lamp module using the virtual sensors as inputs.

Abstract translation: 本文公开的实施例包括用于在灯故障预测期间减少或消除调谐干扰的影响的方法。 在一个实施例中，该方法包括使用布置在灯模块内的不同位置处的一个或多个物理传感器来监视处理室的灯模块的数据，基于灯模块的监视数据创建虚拟传感器，并且为 灯模块使用虚拟传感器作为输入。

公开(公告)号：US20150331980A1

公开(公告)日：2015-11-19

申请号：US14713943

申请日：2015-05-15

Applicant: Applied Materials, Inc.

Inventor： Jimmy ISKANDAR ,  Bradley D. SCHULZE ,  Parris HAWKINS

IPC: G06F17/50 ,  G06F17/30

Abstract: A method is provided for determining two or more context types having an associated fault to be modeled by the same multivariate model. The method includes selecting a fault and selecting two or more context types associated with the fault. The method further includes accessing data stored for the selected context types. The method further includes generating rankings of process data tags for each selected context type. Each ranking includes process data tags ranked according to relative contributions of each process data tag in the ranking to the fault. The method further includes classifying the context types into one or more classes based on the process data tags included in each ranking. The one or more classes include a first class of the context types. The method further includes deploying a multivariate model operable to monitor processing equipment for the selected fault for the first class of context types.

Abstract translation: 提供了一种用于确定具有由相同多变量模型建模的相关故障的两个或多个上下文类型的方法。 该方法包括选择故障并选择与故障相关联的两个或多个上下文类型。 该方法还包括访问为所选择的上下文类型存储的数据。 该方法还包括为每个所选择的上下文类型生成过程数据标签的排名。 每个排名包括根据每个过程数据标签在与故障排序中的相对贡献进行排序的过程数据标签。 该方法还包括基于每个排名中包括的过程数据标签将上下文类型分类为一个或多个类。 一个或多个类包括上下文类型的第一类。 该方法还包括部署多变量模型，其可操作以监视针对第一类上下文类型的所选择的故障的处理设备。