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    • 1. 发明授权
    • Fuzzy control method for adjusting a semiconductor machine
    • 用于调整半导体机器的模糊控制方法
    • US08010212B2
    • 2011-08-30
    • US12241568
    • 2008-09-30
    • Yi Feng LeeTzu-Cheng LinChun Chi ChenYun-Zong Tian
    • Yi Feng LeeTzu-Cheng LinChun Chi ChenYun-Zong Tian
    • G05B13/02
    • G05B13/0275G06N99/005Y10S706/904
    • A method of fuzzy control for adjusting a semiconductor machine comprising: providing measurement values from first the “parameter of a pre-semiconductor manufacturing process”, second the “parameter of the semiconductor manufacturing process”, and third the “operation parameter of the semiconductor manufacturing process”; performing a fuzzy control to define two inputs and one output corresponding to the measurement values, wherein the difference between the first and third values, and the difference between the second and third values, forms the two inputs, then from the two inputs one target output is calculated by fuzzy inference; finally, determining if the target output is in or out of an acceptable range. Whereby the target output is the “machine control parameter of the semiconductor manufacturing process” and when within an acceptable range is used for adjusting the semiconductor machine.
    • 一种用于调整半导体机器的模糊控制方法,包括:首先从“半导体制造工艺的参数”提供测量值,第二个“半导体制造工艺的参数”,第三个“半导体制造的操作参数 处理”; 执行模糊控制以定义对应于测量值的两个输入和一个输出,其中第一和第三值之间的差异以及第二和第三值之间的差异形成两个输入,然后从两个输入一个目标输出 通过模糊推理计算; 最后,确定目标输出是否在可接受的范围之内。 由此,目标输出是“半导体制造工序的机器控制参数”,并且在可接受范围内用于调整半导体机器时。
    • 3. 发明授权
    • Method for predicting cycle time
    • 预测周期时间的方法
    • US08090668B2
    • 2012-01-03
    • US12243301
    • 2008-10-01
    • Yi Feng LeeChun Chi ChenYun-Zong TianTsung-Wei Lin
    • Yi Feng LeeChun Chi ChenYun-Zong TianTsung-Wei Lin
    • G06F15/18G06E1/00
    • G06F17/30598G06Q10/06
    • A method for predicting cycle time comprises the steps of: collecting a plurality of known sets of data; using a clustering method to classify the known sets of data into a plurality of clusters; using a decision tree method to build a classification rule of the clusters; building a prediction model of each cluster; preparing data predicted set of data; using the classification rule to determine that to which clusters the predicted set of data belongs; and using the prediction model of the cluster to estimate the objective cycle time of the predicted set of data. Therefore, engineers can beforehand know the cycle time that one lot of wafers spend in the forward fabrication process, which helps engineers to properly arrange the following fabrication process of the lot of wafer.
    • 一种用于预测周期时间的方法包括以下步骤:收集多个已知的数据集; 使用聚类方法将已知的数据集合分类成多个聚类; 使用决策树方法构建集群的分类规则; 构建每个群集的预测模型; 准备数据预测数据集; 使用分类规则来确定预测的数据集合属于哪个集群; 并使用群集的预测模型来估计预测数据集的目标周期时间。 因此,工程师可以事先知道大量晶圆在正向制造过程中花费的周期时间,这有助于工程师正确布置晶圆批次的以下制造工艺。
    • 7. 发明授权
    • Method for evaluating efficacy of prevention maintenance for a tool
    • 评估工具预防维护功效的方法
    • US08195431B2
    • 2012-06-05
    • US12566974
    • 2009-09-25
    • Yi Feng LeeChun Chi ChenShih Chang KaoYun-Zong TianWei Jun Chen
    • Yi Feng LeeChun Chi ChenShih Chang KaoYun-Zong TianWei Jun Chen
    • G06F15/00G06F19/00
    • G06Q10/00
    • A method for evaluating efficacy of prevention maintenance for a tool includes the steps of: choosing a tool which has been maintained preventively and choosing a productive parameter of the tool; collecting values of the productive parameter generated from the tool during a time range for building a varying curve of the productive parameter versus time, modifying the varying curve with a moving average method; transforming the varying curve into a Cumulative Sum chart; and judging whether the values of the productive parameter generated from the tool after the prevention maintenance are more stable, compared with the values of the productive parameter generated from the tool before the prevention maintenance, according to the Cumulative Sum chart. Thereby, if the varying of the values of the productive parameter after the prevention maintenance isn't stable, then the efficacy of this prevention maintenance for the tool is judged not good.
    • 一种用于评估工具的预防维护功效的方法包括以下步骤:选择已预先维护的工具并选择工具的生产参数; 在用于构建生产参数对时间的变化曲线的时间范围内收集从工具产生的生产参数的值,以移动平均法修改变化曲线; 将变化曲线转换为累计总和图; 并且根据累计总和图来判断在防止维护之后从工具生成的生产参数的值是否比预防维护之前从工具产生的生产参数的值更稳定。 因此,如果防止维护后的生产参数的值的变化不稳定,则对该工具的该防止维护的功效被判断为不好。
    • 8. 发明授权
    • Method for planning a semiconductor manufacturing process based on users' demands using a fuzzy system and a genetic algorithm model
    • 使用模糊系统和遗传算法模型根据用户需求规划半导体制造过程的方法
    • US08170964B2
    • 2012-05-01
    • US12471711
    • 2009-05-26
    • Wei Jun ChenChun Chi ChenYun-Zong TianYi Feng LeeTsung-Wei Lin
    • Wei Jun ChenChun Chi ChenYun-Zong TianYi Feng LeeTsung-Wei Lin
    • G07B15/00
    • G06N3/126G06N7/02
    • A method for planning a semiconductor manufacturing process based on users' demands includes the steps of: establishing a genetic algorithm model and inputting data; establishing a fuzzy system and setting one output parameter representing percent difference of each cost function in neighbor generations; setting to have a modulation parameter corresponding to each input parameter for adjusting fuzzy sets of the output parameter; executing genetic algorithm actions; executing fuzzy inference actions; eliminating chromosomes that produce output parameter smaller than a defined lower limit, and the remaining chromosomes that produces the largest output parameter is defined as the optimum chromosome, wherein the genetic algorithm actions stops being executed upon the optimum chromosome; then determining whether or not a defined number of generations has been reached, if yes, executing the optimum chromosome of the last generation; if no, continuing executing the genetic algorithm actions, thereby finding the optimum semiconductor manufacturing process for users.
    • 一种基于用户需求的半导体制造过程规划方法,包括以下步骤:建立遗传算法模型并输入数据; 建立一个模糊系统,并设置一个输出参数,代表相邻代的每个成本函数的百分比差; 设置为具有对应于每个输入参数的调制参数,用于调整输出参数的模糊集合; 执行遗传算法动作; 执行模糊推理动作; 消除产生小于规定下限的输出参数的染色体,将产生最大输出参数的剩余染色体定义为最佳染色体,其中遗传算法动作停止在最佳染色体上执行; 然后确定是否已经达到定义数量的世代,如果是,则执行最后一代的最佳染色体; 如果否,继续执行遗传算法动作,从而为用户找到最佳的半导体制造过程。
    • 10. 发明授权
    • Method for detecting variance in semiconductor processes
    • 检测半导体工艺方差的方法
    • US08649990B2
    • 2014-02-11
    • 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。