Abstract: The present invention includes a computing system determining a best alias rule in a semiconductor manufacturing process. The computing system obtains an original rule and candidate alias rules based on sampled data from the semiconductor manufacturing process. The computing system compares the original rule to the candidate alias rules. The computing system ranks the candidate alias rules according to the comparison. The computing system filters the ranked candidate alias rules. A user selects one rule among the filtered candidate alias rules based on knowledge of the semiconductor manufacturing process.

Abstract: A system, method and computer program product for predicting at least one feature of at least one product being manufactured. The system receives, from at least one sensor installed in equipment performing one or more manufacturing process steps, at least one measurement of the feature of the product being manufactured. The system selects one or more of the received measurement of the feature of the product. The system estimates additional measurements of the feature of the product at a current manufacturing process step. The system creates a computational model for predicting future measurements of the feature of the product, based on the selected measurement and the estimated additional measurements. The system predicts the future measurements of the feature of the product based on the created computational model. The system outputs the predicted future measurements of the feature of the product.

Abstract: A method, a system and a computer program product suitable for use in a manufacturing environment comprising a multiplicity of nominally identical independent tools. A computing device generates a multi dimensional array of process trace data derived from at least one of the independent tools, wherein, the array includes data representing a first dimension comprising a list of steps in a manufacturing recipe and data representing a second dimension comprising a list of a set of sensors generating measurements from at least one of the independent tools. The computing device conducts an analysis on at least one preselected subset of the multi dimensional array for the purpose of evaluating at least one operating characteristic of at least one of the independent tools. The computing device presents results of the analysis via a set of hierarchically linked and browseable graphics.

Abstract: A system for determining a group of semiconductor manufacturing process steps with a similar influence on individual semiconductor products. The system generates a first table including time stamps for the individual semiconductor products. The system creates a second table including Q-times based on the first table. The Q-times refers to time differences between every pair of the time stamps. The system forms a dependency table by grouping the Q-times with similar dependencies together. The system identifies groups of the similar dependencies. The system extracts semiconductor process steps belonging to the groups.

Abstract: A computer-based measurement monitoring system and method for monitoring multi-stage processes capable of producing multi-orderable data and identifying, at any stage in the monitored process, unacceptable deviations from an expected value at particular stages of the process, and communicating same detected deviation to facilitate corrective action in the process. The system and method consolidate data obtained at various stages of the process, arrange the measurement data in a multi-orderable data framework, compare the multi-orderable framework data with expected parameter values corresponding to the various stages, and detects-unacceptable deviations from the expected values. The unacceptable deviations are communicated to responsible personnel, and the system and method provides same personnel with supplemental information useful in diagnosing the root cause of the problem leading to the detected deviations.

Abstract: There are provided a system, method and computer program product for detecting foreign materials in a semiconductor manufacturing process. The manufacturing process uses a plurality of semiconductor manufacturing tools. The system categorizes at least one monitoring wafer according to one or more categories. The system supplies the categorized monitoring wafer to a semiconductor manufacturing tool. The system observes a level of contamination on the categorized monitoring wafer. The system compares the level of contamination to a threshold. The system cleans the tool in a response to determining that the level of contamination is larger than the threshold. The system determines which category of the wafer leaves a highest level of contamination on the tool. The system identifies a root cause of the highest level of contamination on the tool.

Abstract: A system, method and/or computer program product for analyzing a functionality of at least two manufactured products obtain a first characteristic of a first manufactured product. The system acquires a second characteristic of a second manufactured product. The system identifies a common feature between the first characteristic and the second characteristic. The system identifies a distinguishable feature between the first characteristic and the second characteristic. The system determines a cause of a deviation of a functionality in the first manufactured product or the second manufactured product or both manufactured products based on the identified common feature or the identified distinguishable feature or both features.

Abstract: A method and system for evaluating a performance of a semiconductor manufacturing tool while manufacturing microelectronic devices are disclosed. At least one report is generated based on executions of at least one statistical test. The report includes at least one heat map having rows that correspond to sensors, columns that correspond to trace data obtained during recipe steps, and cells at the intersection of the rows and the columns. At least one sensor in the tool obtains trace data of a recipe step while manufacturing at least one microelectronic device. A computing device analyzes the obtained trace data to determine a level of operational significance found in the data and assigns a score to the trace data that indicates a level of operational significance. Then, the computing device places the score in a corresponding cell of the heat map. A user uses the cell for evaluating the tool performance.

Abstract: A system, a method and a computer program product for identifying incompatible manufacturing tools. The system receives measurements of products that were subject to a manufacturing process involving a plurality of manufacturing tools. The measurements pertain to a performance characteristic of each product. The system evaluates whether each manufacturing tool implemented in a sequential manufacturing process individually performs normally based on the received measurements. In response to evaluating each manufacturing tool implemented in said manufacturing process individually performs normally, the system evaluates whether a first combination of the manufacturing tools together in sequential manufacturing process perform normally based on the received measurements.

Abstract: There are provided a system, method and computer program product for detecting foreign materials in a semiconductor manufacturing process. The manufacturing process uses a plurality of semiconductor manufacturing tools. The system categorizes at least one monitoring wafer according to one or more categories. The system supplies the categorized monitoring wafer to a semiconductor manufacturing tool. The system observes a level of contamination on the categorized monitoring wafer. The system compares the level of contamination to a threshold. The system cleans the tool in a response to determining that the level of contamination is larger than the threshold. The system determines which category of the wafer leaves a highest level of contamination on the tool. The system identifies a root cause of the highest level of contamination on the tool.

Abstract: A system for determining a group of semiconductor manufacturing process steps with a similar influence on individual semiconductor products. The system generates a first table including time stamps for the individual semiconductor products. The system creates a second table including Q-times based on the first table. The Q-times refers to time differences between every pair of the time stamps. The system forms a dependency table by grouping the Q-times with similar dependencies together. The system identifies groups of the similar dependencies. The system extracts semiconductor process steps belonging to the groups.

Abstract: A system, method and/or computer program product for analyzing a functionality of at least two manufactured products obtain a first characteristic of a first manufactured product. The system acquires a second characteristic of a second manufactured product. The system identifies a common feature between the first characteristic and the second characteristic. The system identifies a distinguishable feature between the first characteristic and the second characteristic. The system determines a cause of a deviation of a functionality in the first manufactured product or the second manufactured product or both manufactured products based on the identified common feature or the identified distinguishable feature or both features.

Abstract: The present invention includes a computing system determining a best alias rule in a semiconductor manufacturing process. The computing system obtains an original rule and candidate alias rules based on sampled data from the semiconductor manufacturing process. The computing system compares the original rule to the candidate alias rules. The computing system ranks the candidate alias rules according to the comparison. The computing system filters the ranked candidate alias rules. A user selects one rule among the filtered candidate alias rules based on knowledge of the semiconductor manufacturing process.

Abstract: A method and system for evaluating a performance of a semiconductor manufacturing tool while manufacturing microelectronic devices are disclosed. At least one report is generated based on executions of at least one statistical test. The report includes at least one heat map having rows that correspond to sensors, columns that correspond to trace data obtained during recipe steps, and cells at the intersection of the rows and the columns. At least one sensor in the tool obtains trace data of a recipe step while manufacturing at least one microelectronic device. A computing device analyzes the obtained trace data to determine a level of operational significance found in the data and assigns a score to the trace data that indicates a level of operational significance. Then, the computing device places the score in a corresponding cell of the heat map. A user uses the cell for evaluating the tool performance.

Abstract: A computer-based measurement monitoring system and method for monitoring multi-stage processes capable of producing multi-orderable data and identifying, at any stage in the monitored process, unacceptable deviations from an expected value at particular stages of the process, and communicating same detected deviation to facilitate corrective action in the process. The system and method consolidate data obtained at various stages of the process, arrange the measurement data in a multi-orderable data framework, compare the multi-orderable framework data with expected parameter values corresponding to the various stages, and detects unacceptable deviations from the expected values. The unacceptable deviations are communicated to responsible personnel, and the system and method provides same personnel with supplemental information useful in diagnosing the root cause of the problem leading to the detected deviations.

Abstract: A fabrication history a group of wafers is provided, having a record for each wafer of the manufacturing events that did or did not occur in its fabrication, and having the measured value of a given target. A binary decision rule is formed based on the fabrication history, the rule being that if a wafer has a particular pattern of manufacturing events in its fabrication history then the statistic of the given fabrication target for that wafer is a first value; otherwise, the statistic is a second value having at least a given distance from the first value. The pattern of manufacturing events in the binary decision rule is identified in the generation of the binary decision rule. The identified pattern is significant with respect to the given target.

Abstract: A fabrication history of a group of wafers is provided, having a record for each wafer of the manufacturing events that did or did not occur in its fabrication, and having the measured value of a given target. A binary decision rule is formed based on the fabrication history, the rule being that if a wafer has a particular pattern of manufacturing events in its fabrication history then the statistic of the given fabrication target for that wafer is a first value; otherwise, the statistic is a second value having at least a given distance from the first value. The pattern of manufacturing events in the binary decision rule is identified in the generation of the binary decision rule. The identified pattern is significant with respect to the given target.

Abstract: A fabrication history a group of wafers is provided, having a record for each wafer of the manufacturing events that did or did not occur in its fabrication, and having the measured value of a given target. A binary decision rule is formed based on the fabrication history, the rule being that if a wafer has a particular pattern of manufacturing events in its fabrication history then the statistic of the given fabrication target for that wafer is a first value; otherwise, the statistic is a second value having at least a given distance from the first value. The pattern of manufacturing events in the binary decision rule is identified in the generation of the binary decision rule. The identified pattern is significant with respect to the given target.

Abstract: A fabrication history of a group of wafers is provided, having a record for each wafer of the manufacturing events that did or did not occur in its fabrication, and having the measured value of a given target. A binary decision rule is formed based on the fabrication history, the rule being that if a wafer has a particular pattern of manufacturing events in its fabrication history then the statistic of the given fabrication target for that wafer is a first value; otherwise, the statistic is a second value having at least a given distance from the first value. The pattern of manufacturing events in the binary decision rule is identified in the generation of the binary decision rule. The identified pattern is significant with respect to the given target.

Abstract: During wafer fabrication, a transportable enclosure, such as a Standard Manufacturing InterFace (SMIF) pod encloses a nascent product, such as a semiconductor wafer, to protect the wafer against contamination during manufacture, storage or transportation. However chemical vapors emitted inside the pod can accumulate in the air and degrade wafers during subsequent fabrication. In order to absorb the vapors inside a closed pod, a vapor removal element typically including an activated carbon absorber, covered by a particulate-filtering vapor-permeable barrier, and covered by a guard plate with holes is disposed within the enclosure. A vapor removal element is disposed closely adjacent to each respective wafer. Alternatively, a single vapor removal element is located inside the enclosure. In certain instances, a fan or thermo-buoyant circulation causes any vapors located inside the enclosure to a vapor removal element for removal.