Patent application number | Description | Published |
20090141770 | TIME DOMAIN DIGITAL TEMPERATURE SENSING SYSTEM AND METHOD THEREOF - A digital temperature sensing system and method for converting a test temperature into a digital output signal are disclosed. The system comprises a temperature-to-time circuit for generating a thermally sensitive time signal of which a width varies with the test temperature; an adjustable time reference circuit for generating a time reference signal of which a width changes with the digital set value; a time comparator for generating a time comparison signal according to a width difference between the thermally sensitive time signal and the time reference signal; a logic control circuit for adjusting the digital set value of the adjustable time reference circuit according to the time comparison signal so that the width of the thermally sensitive time signal and the width of time reference signal are close enough or substantially equal. | 06-04-2009 |
20090197354 | SYSTEM AND METHOD FOR MONITORING MANUFACTURING PROCESS - 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. | 08-06-2009 |
20090259332 | FUZZY CONTROL METHOD FOR ADJUSTING A SEMICONDUCTOR MACHINE - 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. | 10-15-2009 |
20090276182 | MACHINE FAULT DETECTION METHOD - 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. | 11-05-2009 |
20090327173 | METHOD FOR PREDICTING CYCLE TIME - 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. | 12-31-2009 |
20100004882 | FAULT DETECTION AND CLASSIFICATION METHOD FOR WAFER ACCEPTANCE TEST PARAMETERS - 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. | 01-07-2010 |
20100010763 | METHOD FOR DETECTING VARIANCE IN SEMICONDUCTOR PROCESSES - 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. | 01-14-2010 |
20100205127 | METHOD FOR PLANNING A SEMICONDUCTOR MANUFACTURING PROCESS BASED ON USERS' DEMANDS - 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. | 08-12-2010 |
20100223027 | MONITORING METHOD FOR MULTI TOOLS - 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. | 09-02-2010 |
20100233830 | METHOD FOR MONITORING FABRICATION PARAMETER - 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. | 09-16-2010 |
20100234978 | METHOD FOR FINDING THE CORRELATION BETWEEN THE TOOL PM AND THE PRODUCT YIELD - 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. | 09-16-2010 |
20100268501 | Method for assessing data worth for analyzing yield rate - A method for assessing data worth for analyzing yield rate includes: getting measured data with data points that corresponds to control variables of semiconductor manufacturing; transforming the data points into a distance matrix with matrix distances corresponding to differences of the data points under the control variables; expressing sample differences recorded in the distance matrix by two-dimension vectors and calculating similarity degrees of the two-dimension vectors and the distance matrix so as to take loss information as a conversion error value; calculating discriminant ability of the transformed two-dimension data and expressing the discriminant ability by an error rate of discriminant; and taking the conversion error value and the error rate of discriminant as penalty terms and calculating a quality score corresponding to the measured data. Thereby, before analyzing the yield rate of semiconductor manufacturing, analysts can determine whether data includes information affecting the yield rate based on the quality score. | 10-21-2010 |
20110010132 | METHOD FOR EVALUATING EFFICACY OF PREVENTION MAINTENANCE FOR A TOOL - 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. | 01-13-2011 |
20110093226 | FAULT DETECTION AND CLASSIFICATION METHOD FOR WAFER ACCEPTANCE TEST PARAMETERS - 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. | 04-21-2011 |
20110153660 | METHOD OF SEARCHING FOR KEY SEMICONDUCTOR OPERATION WITH RANDOMIZATION FOR WAFER POSITION - 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. | 06-23-2011 |
20110257932 | METHOD FOR DETECTING VARIANCE IN SEMICONDUCTOR PROCESSES - 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. | 10-20-2011 |
20130054653 | METHOD OF CONSTRUCTING ETCHING PROFILE DATABASE - A method of constructing a database for etching profile is disclosed. First, a standard etching group including a standard etching structure and a deviated etching group including a deviated etching structure are provided. Second, a remote sensing (RS) step is carried out to collect a standard RS data belonging to the standard etching group and a deviated RS data belonging to the deviated etching group. Then, the RS data is analyzed to infer feature parameters of the etching groups. Next, a deviated physical parameter is verified. Later, the correlation between the feature parameters and the deviated physical parameter is calculated to construct an etching profile database including the standard RS data and the deviated RS data. The etching profile database may facilitate the prediction of an unknown etching profile. | 02-28-2013 |