Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: A method for process control using predictive long short term memory includes obtaining historical post-process measurements taken on prior products of the manufacturing process; obtaining historical in-process measurements taken on prior workpieces during the manufacturing process; training a neural network to predict each of the historical post-process measurements, in response to the corresponding historical in-process measurements and preceding historical post-process measurements; obtaining present in-process measurements on a present workpiece during the manufacturing process; predicting a future post-process measurement for the present workpiece, by providing the present in-process measurements and the historical post-process measurements as inputs to the neural network; and adjusting at least one controllable variable of the manufacturing process in response to the prediction of the future post-process measurement.

Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: Detecting mismatches among a plurality of manufacturing tools of the same type and operating on a first semiconductor wafer. A server receives metrological data and time series data applicable to the first semiconductor wafer from each of the plurality of manufacturing tools. The server applies a plurality of statistical tests to the metrological data and a data mining technique to the time series data. The server assigns a score to each result from applying the plurality of statistical test and the data mining technique to the plurality of manufacturing tools, the scores indicating a degree of mismatch among the plurality of manufacturing tools. Responsive to the sum of scores exceeding a predetermined threshold, the server declares a mismatch condition for each tool among the plurality of manufacturing tools.

Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: Controlling product production in multi-stage manufacturing process automatically generates by machine learning causal relationships between the processing conditions and the product quality based on product genealogy data and product quality data. Real time sensor data from sensors coupled to processing units in a manufacturing facility implementing the multi-stage manufacturing process are received, and control rules are instantiated based on the real time sensor data. An instantiated control rule firing causes an actuator to automatically set a processing variable to a set point specified in the control rule.

Abstract: A method, a computer program product, and a computer system for identifying significant and consumable-insensitive trace features. A computer computes a residual in a first regression of one or more secondary factors on a target. The computer computes residuals in a second regression of the one or more secondary factors on each of one or more trace features in one or more trace feature sets. The computer computes, for the one or more trace feature sets, coefficients of determination in a third regression of the residuals in the second regression on the residual in the first regression. The computer ranks the one or more trace feature sets by sorting the coefficient of determination. The computer determines, based on rankings of the one or more trace feature sets, significant trace feature sets.

Abstract: A method, a computer program product, and a computer system for identifying significant and consumable-insensitive trace features. A computer computes a residual in a first regression of one or more secondary factors on a target. The computer computes residuals in a second regression of the one or more secondary factors on each of one or more trace features in one or more trace feature sets. The computer computes, for the one or more trace feature sets, coefficients of determination in a third regression of the residuals in the second regression on the residual in the first regression. The computer ranks the one or more trace feature sets by sorting the coefficient of determination. The computer determines, based on rankings of the one or more trace feature sets, significant trace feature sets.

Abstract: An apparatus for performing run-to-run control and sampling optimization in a semiconductor manufacturing process includes at least one control module. The control module is operative: to determine a process output and corresponding metrology error associated with an actual metrology for a current processing run in the semiconductor manufacturing process; to determine a predicted process output and corresponding prediction error associated with a virtual metrology for the current processing run; and to control at least one parameter corresponding to a subsequent processing run as a function of the metrology error and the prediction error.

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 for performing enhanced wafer quality prediction in a semiconductor manufacturing process includes the steps of: obtaining data including at least one of tensor format wafer processing conditions, historical wafer quality measurements and prior knowledge relating to at least one of the semiconductor manufacturing process and wafer quality; building a hierarchical prediction model including at least the tensor format wafer processing conditions; and predicting wafer quality for a newly fabricated wafer based at least on the hierarchical prediction model and corresponding tensor format wafer processing conditions.

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: An apparatus for performing run-to-run control and sampling optimization in a semiconductor manufacturing process includes at least one control module. The control module is operative: to determine a process output and corresponding metrology error associated with an actual metrology for a current processing run in the semiconductor manufacturing process; to determine a predicted process output and corresponding prediction error associated with a virtual metrology for the current processing run; and to control at least one parameter corresponding to a subsequent processing run as a function of the metrology error and the prediction error.

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: An apparatus for performing enhanced wafer quality prediction in a semiconductor manufacturing process includes memory, for storing historical data relating to the semiconductor manufacturing process, and at least one processor in operative communication with the memory. The processor is operative: to obtain data including tensor format wafer processing conditions, historical wafer quality measurements and/or prior knowledge relating to at least one of the semiconductor manufacturing process and wafer quality; to build a hierarchical prediction model including at least the tensor format wafer processing conditions; and to predict wafer quality for a newly fabricated wafer based at least on the hierarchical prediction model and corresponding tensor format wafer processing conditions.

Abstract: A method for performing enhanced wafer quality prediction in a semiconductor manufacturing process includes the steps of: obtaining data including at least one of tensor format wafer processing conditions, historical wafer quality measurements and prior knowledge relating to at least one of the semiconductor manufacturing process and wafer quality; building a hierarchical prediction model including at least the tensor format wafer processing conditions; and predicting wafer quality for a newly fabricated wafer based at least on the hierarchical prediction model and corresponding tensor format wafer processing conditions.

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: 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.