公开(公告)号：US08452439B2

公开(公告)日：2013-05-28

申请号：US13048282

申请日：2011-03-15

申请人： Sunny Wu ,  Chun-Hsien Lin ,  Kun-Ming Chen ,  Dung-Yian Hsieh ,  Hui-Ru Lin ,  Jo Fei Wang ,  Jong-I Mou ,  I-Ching Chu

发明人： Sunny Wu ,  Chun-Hsien Lin ,  Kun-Ming Chen ,  Dung-Yian Hsieh ,  Hui-Ru Lin ,  Jo Fei Wang ,  Jong-I Mou ,  I-Ching Chu

IPC分类号： G06F19/00 ,  G01N37/00 ,  G06F11/30 ,  G21C17/00

CPC分类号： H01L22/20 ,  G01R31/2894 ,  H01L22/14

摘要： A method comprises computing respective regression models for each of a plurality of failure bins based on a plurality of failures identified during wafer electrical tests. Each regression model outputs a wafer yield measure as a function of a plurality of device performance variables. For each failure bin, sensitivity of the wafer yield measure to each of the plurality of device performance variables is determined, and the device performance variables are ranked with respect to sensitivity of the wafer yield measure. A subset of the device performance variables which have highest rankings and which have less than a threshold correlation with each other are selected. The wafer yield measures for each failure bin corresponding to one of the selected subset of device performance variables are combined, to provide a combined wafer yield measure. At least one new process parameter value is selected to effect a change in the one device performance variable, based on the combined wafer yield measure. The at least one new process parameter value is to be used to process at least one additional wafer.

摘要翻译： 一种方法包括基于在晶片电测试期间识别的多个故障来计算多个故障仓中的每一个的相应回归模型。 每个回归模型输出作为多个设备性能变量的函数的晶片产量测量。 对于每个故障仓，确定晶片产量测量对多个器件性能变量中的每一个的灵敏度，并且相对于晶片产量测量的灵敏度对器件性能变量进行排序。 选择具有最高排名并且彼此具有小于阈值相关性的设备性能变量的子集。 组合对应于所选择的设备性能变量子集之一的每个故障仓的晶片产量测量，以提供组合晶片产量测量。 选择至少一个新的过程参数值，以基于组合的晶片产量测量来实现一个器件性能变量的变化。 至少一个新的过程参数值将用于处理至少一个附加晶片。

公开(公告)号：US20120239178A1

公开(公告)日：2012-09-20

申请号：US13048282

申请日：2011-03-15

申请人： Sunny Wu ,  Chun-Hsien Lin ,  Kun-Ming Chen ,  Dung-Yian Hsieh ,  Hui-Ru Lin ,  Jo Fei Wang ,  Jong-I Mou ,  I-Ching Chu

发明人： Sunny Wu ,  Chun-Hsien Lin ,  Kun-Ming Chen ,  Dung-Yian Hsieh ,  Hui-Ru Lin ,  Jo Fei Wang ,  Jong-I Mou ,  I-Ching Chu

IPC分类号： G06F19/00

CPC分类号： H01L22/20 ,  G01R31/2894 ,  H01L22/14

摘要： A method comprises computing respective regression models for each of a plurality of failure bins based on a plurality of failures identified during wafer electrical tests. Each regression model outputs a wafer yield measure as a function of a plurality of device performance variables. For each failure bin, sensitivity of the wafer yield measure to each of the plurality of device performance variables is determined, and the device performance variables are ranked with respect to sensitivity of the wafer yield measure. A subset of the device performance variables which have highest rankings and which have less than a threshold correlation with each other are selected. The wafer yield measures for each failure bin corresponding to one of the selected subset of device performance variables are combined, to provide a combined wafer yield measure. At least one new process parameter value is selected to effect a change in the one device performance variable, based on the combined wafer yield measure. The at least one new process parameter value is to be used to process at least one additional wafer.

摘要翻译： 一种方法包括基于在晶片电测试期间识别的多个故障来计算多个故障仓中的每一个的相应回归模型。 每个回归模型输出作为多个设备性能变量的函数的晶片产量测量。 对于每个故障仓，确定晶片产量测量对多个器件性能变量中的每一个的灵敏度，并且相对于晶片产量测量的灵敏度对器件性能变量进行排序。 选择具有最高排名并且彼此具有小于阈值相关性的设备性能变量的子集。 组合对应于所选择的设备性能变量子集之一的每个故障仓的晶片产量测量，以提供组合晶片产量测量。 选择至少一个新的过程参数值，以基于组合的晶片产量测量来实现一个器件性能变量的变化。 至少一个新的过程参数值将用于处理至少一个附加晶片。