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    • 1. 发明申请
    • METHOD OF SEARCHING FOR KEY SEMICONDUCTOR OPERATION WITH RANDOMIZATION FOR WAFER POSITION
    • 搜索用于主要半导体操作的方法用于放置位置的随机化
    • US20110153660A1
    • 2011-06-23
    • US13040633
    • 2011-03-04
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIANCHENG-HAO CHEN
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIANCHENG-HAO CHEN
    • G06F17/30
    • G05B19/41875G05B2219/32221G05B2219/45031H01L22/20Y02P90/22
    • A method of searching for the key semiconductor operation with randomization for wafer position, comprising: recording the wafer position and the wafer yields of a plurality of wafer ID respectively corresponding to a plurality of semiconductor operations; establishing a matrix model which describes the matrix set for wafer yields of the plurality of wafer ID; analyzing the matrix model, further computing the matrix set for wafer yields of the wafer ID, thereby acquiring the weightings of the randomized wafer positions in such semiconductor operations; and searching for a key semiconductor operation among the plurality of semiconductor operations; herein, by using a local regression model to estimate the wafer position effect, computing the weighting of the position effect in each semiconductor operation based on the estimated position effect and the randomized wafer yield, higher weighting thereof indicates the key semiconductor operation having greater position effect in the aforementioned semiconductor process.
    • 一种用于晶片位置的随机化搜索密钥半导体操作的方法,包括:分别记录分别对应于多个半导体操作的多个晶片ID的晶片位置和晶片产量; 建立描述多个晶片ID的晶片产量的矩阵集合的矩阵模型; 分析矩阵模型,进一步计算晶片ID的晶片产量的矩阵集,从而在这种半导体操作中获得随机晶片位置的权重; 以及在所述多个半导体操作中搜索密钥半导体操作; 在本文中,通过使用局部回归模型来估计晶片位置效应,基于估计的位置效应和随机晶片产量计算每个半导体操作中的位置效应的加权,其较高的加权指示具有较大位置效应的关键半导体操作 在上述半导体工艺中。
    • 4. 发明申请
    • METHOD OF SEARCHING FOR KEY SEMICONDUCTOR OPERATION WITH RANDOMIZATION FOR WAFER POSITION
    • 搜索用于主要半导体操作的方法用于放置位置的随机化
    • US20100093114A1
    • 2010-04-15
    • US12330846
    • 2008-12-09
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIANCHENG-HAO CHEN
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIANCHENG-HAO CHEN
    • H01L21/02
    • G05B19/41875G05B2219/32221G05B2219/45031Y02P90/22
    • A method of searching for the key semiconductor operation with randomization for wafer position, comprising: recording the wafer position and the wafer yields of a plurality of wafer ID respectively corresponding to a plurality of semiconductor operations; establishing a matrix model which describes the matrix set for wafer yields of the plurality of wafer ID; analyzing the matrix model, further computing the matrix set for wafer yields of the wafer ID, thereby acquiring the weightings of the randomized wafer positions in such semiconductor operations; and searching for a key semiconductor operation among the plurality of semiconductor operations; herein, by using a local regression model to estimate the wafer position effect, computing the weighting of the position effect in each semiconductor operation based on the estimated position effect and the randomized wafer yield, higher weighting thereof indicates the key semiconductor operation having greater position effect in the aforementioned semiconductor process.
    • 一种用于晶片位置的随机化搜索密钥半导体操作的方法,包括:分别记录分别对应于多个半导体操作的多个晶片ID的晶片位置和晶片产量; 建立描述多个晶片ID的晶片产量的矩阵集合的矩阵模型; 分析矩阵模型,进一步计算晶片ID的晶片产量的矩阵集,从而在这种半导体操作中获得随机晶片位置的权重; 以及在所述多个半导体操作中搜索密钥半导体操作; 在本文中,通过使用局部回归模型来估计晶片位置效应,基于估计的位置效应和随机晶片产量计算每个半导体操作中的位置效应的加权,其较高的加权指示具有较大位置效应的关键半导体操作 在上述半导体工艺中。
    • 9. 发明申请
    • METHOD FOR DETECTING VARIANCE IN SEMICONDUCTOR PROCESSES
    • 用于检测半导体工艺中的变化的方法
    • US20110257932A1
    • 2011-10-20
    • US13170229
    • 2011-06-28
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIAN
    • YIJ CHIEH CHUCHUN CHI CHENYUN-ZONG TIAN
    • G06F19/00
    • G05B23/0221G05B2219/37224
    • A method of detecting variance by regression model has the following steps. Step 1 is preparing the FDC data and WAT data for analysis. Step 2 is figuring out what latent variable effect of WAT data by Factor Analysis Step 3 is utilizing Principal Component Analysis to reduce the number of FDC variables to a few independent principal components. Step 4 is demonstrating how the tools and FDC data affect WAT data by Analysis of covariance model, and constructing interrelationship among FDC, WAT and tools. The interrelationship can point out which parameter effect WAT significantly. By the method, when WAT abnormal situation happened, it is easier for engineers to trace where the problem is.
    • 通过回归模型检测方差的方法具有以下步骤。 步骤1正在准备FDC数据和WAT数据进行分析。 第2步是通过因子分析步骤3来确定WAT数据的潜在变量效应是否利用主成分分析将FDC变量的数量减少到少数独立的主成分。 第4步演示了工具和FDC数据如何通过分析协方差模型影响WAT数据,并构建FDC,WAT和工具之间的相互关系。 相互关系可以显着地指出哪个参数效应WAT。 通过这种方法,当WAT异常情况发生时,工程师更容易追踪问题的位置。