Abstract: A method of adjusting wafer process sequence includes steps of collecting production parameters for a plurality of lots; selecting a plurality of key parameters from the production parameters, wherein the key parameters at least includes a processing sequence; defining a formula to obtain an epsilon value; categorizing the lots into groups according to the epsilon value and the minimum point number by using density-based spatial clustering of application with noise (DBSCAN); and adjusting the processing sequences of the lots in the groups. Thereby, the lots with the same process recipe can be continuously or simultaneously sent into a machine, thereby reducing replacement of process recipes or shortening machine idle time.

Abstract: 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.

Abstract: 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 opti

Abstract: 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.

Abstract: A method for planning a production schedule of equipment includes: receiving information about a material replacement of the equipment; and determining a target production schedule of the equipment according to the information about the material replacement of the equipment, wherein the target production schedule includes an idle period, and during the idle period, the equipment stops manufacturing under a normal state.

Abstract: A method for finding the correlation between tool PM (prevention maintenance) and the product yield of the tool is disclosed. The method uses a moving average method to magnify a curve trend that is formed by the product yield data that is captured during a predetermined days before PM and after PM. The magnified curve trend is shown by a Cumulative sum chart. The Cumulative sum chart is analyzed for informing related workers of the effect between the tool PM and the product yield, so as to accurately estimate PM timing. Thereby, via the method, the effect between the tool PM and the product yield may be determined, which serves as an important reference for workers to execute further PM.

Abstract: 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.

Abstract: A method for predicting and warning of WAT value includes the steps as follows. A key process is selected and a WAT value after finishing the key process is used as a predictive goal. A predicting model is built. One batch or plural batches of predictive wafers are prepared, and a Fault Detection and Classification data (FDC data) and a metrology data from the predictive wafers of the key process are collected. The FDC data and the metrology data collected from the predictive wafers are inputted into the predicting model for processing a normal predicting procedure, and a predictive WAT value by the predicting model is outputted. The present invention can accurately predict the WAT value, effectively monitor some specific defective wafers and continuously perform the improvement for the specific defective wafer.

Abstract: 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.

Abstract: A method for monitoring fabrication parameters comprises steps of: obtaining a normal parameter variance curve and a comparing parameter variance curve; defining a plurality of normal parameter points on the normal parameter variance curve; defining a plurality of comparing parameter points on the comparing parameter variance curve; finding out the corresponding comparing parameter points nearest to the normal parameter points; calculating the distances between the normal parameter points and the corresponding comparing parameter points thereof; summing up the distances so as to receive a total distance; and determining whether or not the total distance exceeds a limit. Via this arrangement, when fabrication parameter of tool is abnormal, it can be efficiently and immediately determined.

Abstract: A method of adjusting wafer process sequence includes steps of collecting production parameters for a plurality of lots; selecting a plurality of key parameters from the production parameters, wherein the key parameters at least includes a processing sequence; defining a formula to obtain an epsilon value; categorizing the lots into groups according to the epsilon value and the minimum point number by using density-based spatial clustering of application with noise (DBSCAN); and adjusting the processing sequences of the lots in the groups. Thereby, the lots with the same process recipe can be continuously or simultaneously sent into a machine, thereby reducing replacement of process recipes or shortening machine idle time.

Abstract: A method for finding the correlation between tool PM (prevention maintenance) and the product yield of the tool is disclosed. The method uses a moving average method to magnify a curve trend that is formed by the product yield data that is captured during a predetermined days before PM and after PM. The magnified curve trend is shown by a Cumulative sum chart. The Cumulative sum chart is analyzed for informing related workers of the effect between the tool PM and the product yield, so as to accurately estimate PM timing. Thereby, via the method, the effect between the tool PM and the product yield may be determined, which serves as an important reference for workers to execute further PM.

Abstract: A method for monitoring fabrication parameters comprises steps of: obtaining a normal parameter variance curve and a comparing parameter variance curve; defining a plurality of normal parameter points on the normal parameter variance curve; defining a plurality of comparing parameter points on the comparing parameter variance curve; finding out the corresponding comparing parameter points nearest to the normal parameter points; calculating the distances between the normal parameter points and the corresponding comparing parameter points thereof; summing up the distances so as to receive a total distance; and determining whether or not the total distance exceeds a limit. Via this arrangement, when fabrication parameter of tool is abnormal, it can be efficiently and immediately determined.

Abstract: 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 opti

Abstract: A method of detecting variance by regression model is disclosed. Said method comprising: preparing the FDC and WAT data for analysis, figuring out what latent variable effect WAT by Factor Analysis, utilizing Principal Component Analysis to reduce the number of FDC variables to a few independent principal components, demonstrating how the tool and FDC affect WAT 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.

Abstract: 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.

Abstract: A machine fault detection method is applied to a plurality of machines. The machines are used for processing at least one wafer-in-process (WIP). The method includes the flowing steps. A statistical database of the wafer-in-process is provided. An association rules is used to search and survey the statistical database in order to calculate a support degree and a reliability degree. A threshold is selected to determine whether the support degree and the reliability degree have surpassed the threshold or not. When the support degree and the reliability degree have surpassed the threshold, a root cause error in the statistical database corresponded by the support degree and the reliability degree is determined. When the support degree and the reliability degree have not surpassed the threshold, the above steps are repeated.

Abstract: 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.

Abstract: A system and method for monitoring a manufacturing process are provided. A wafer is provided. Process parameters of a manufacturing machine are in-situ measured and recorded if the wafer is processed in the manufacturing machine. A wafer measured value of the wafer is measured after the wafer has been processed. The process parameters are transformed into a process summary value. A two dimensional orthogonal chart with a first axis representing the wafer measured value and a second axis representing the process summary value is provided. The two dimensional orthogonal chart includes a close-loop control limit. A visualized point representing the wafer measured value and the process summary value is displayed on the two dimensional orthogonal chart.