Abstract: A monitoring method for multi tools is disclosed. The method includes the steps of providing a plurality of measurement tools for measuring the testing points of standard wafers, calculating a vector for representing a measurement tool, calculating the angle between every two of the vectors and determining the measurement tools having the same performance or not. Thereby, the measurement tools can be efficiently grouped and the measuring stability of the measurement tool is analyzed.

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: There is disclosed a method and system for detecting a surface plasmon resonance associated with a fluid sample. The method includes the step of providing a piezoelectric substrate having at least two electrodes thereon, wherein at least one of said electrodes is coupled to a fluid sample. A light beam is transmitted toward the fluid sample to induce a oscillation frequency in the piezoelectric substrate. The oscillation frequency from said electrodes is then measured during transmittance of the light to detect the surface plasmon resonance associated with the fluid sample.

Abstract: 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 pro

Abstract: A method for determining manufacturing tool production quality includes providing a table with manufacturing process data. The table is analyzed and a contingency table is established. The contingency table comprises several manufacturing tools, manufacturing processes, and the number of occurrences of bad lots. Split the contingency table up into a plurality of sub-tables. Use Cochran-Mantel-Haenszel test for determining the number of bad lots produced by the manufacturing tools and getting a plurality of statistics. Translate the statistics into a plurality of P-values. Sort the P-values for examining data automatically. Draw a line chart for detecting substandard manufacturing tools. As a result, users can diagnose the quality of the manufacturing tools.

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 fault detection and classification (FDC) method for wafer acceptance test (WAT) parameters includes the following steps. A plurality of fault detection and classification parameters is collected. A plurality of wafer acceptance test parameters that are corresponded by the fault detection and classification parameters is collected. The fault detection and classification parameters are grouped. A contingency table of the wafer acceptance test parameters corresponding to the fault detection and classification parameters is built. A probability model of the contingency table is built. Finally, a safety range of the probability model is determined.

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.