The invention relates to a method and system for monitoring a plurality of critical assets (102a-102n) associated with a production/process management system using one or more edge devices (104a-104n). The one or more edge devices (104a-104n) track operations of the plurality of critical assets (102a-102n), which comprises obtaining consolidated information related to the operations of the plurality of critical assets (102a-102n). The one or more edge devices (104a-104n) then derive insights corresponding to the plurality of critical assets (102a-102n) based on the consolidated information using descriptive analytics and an AI/ML model (114) and derive a set of actionable insights to optimize the operations. The derived insights are then rendered in a real-time consolidated view, to enable a user to take immediate actions and decisions in relation to the plurality of critical assets (102a-102n).

To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

A method includes identifying sets of sensor data associated with wafers processed via wafer processing equipment and identifying sets of metrology data associated with the wafers processed via the wafer processing equipment. The method further includes generating sets of aggregated sensor-metrology data, each of the sets of aggregated sensor-metrology data including a respective set of sensor data and a respective set of metrology data. The method further includes causing, based on the sets of aggregated sensor-metrology data, performance of a corrective action associated with the wafer processing equipment.

A sensor data stream is provided consisting of feature vectors acquired by sensors of rotating equipment, similar feature vectors are aggregated in microclusters. For newly arriving feature vectors, a correlation distance measure between the new feature vector and each microcluster is calculated. If there is no microcluster in range, then a new microcluster is created. Otherwise, the feature vector is assigned to the best fitting microcluster, and the necessary statistical information is incorporated into the aggregation contained in the microcluster. In other words, similar feature vectors are aggregated in the same microclusters. The microclusters thus provide a generic summary structure that captures the necessary statistical information of the incorporated feature vectors. At the same time, the loss of accuracy is quite small. Clustering the sensor data stream with microclusters has the benefit that the computational complexity can be reduced significantly.

A method includes receiving first data indicative of a range of values of a quality parameter of a type of manufacturing chamber component. Each value in the range of values meets one or more threshold criteria. The method further includes providing the first data to a physics-based model of a manufacturing chamber. The method further includes receiving, from the physics-based model, second data indicating a relationship between values of the quality parameter and predicted conditions in the manufacturing chamber. The method further includes determining, based on the relationship between values of the quality parameter and the predicted conditions, whether a new manufacturing chamber component of the manufacturing chamber component type is to be installed in the manufacturing chamber.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

End users subscribe to use information handling systems having a selected of available performance characteristics defined by a keyboard and touchpad configuration selected to build the information handling systems. A manufacturer meets subscriptions with information handling systems built from an inventory of new keyboards, deployed keyboards of information handling system in use by subscribers, and separated keyboards taken from returned information handling systems and re-used. End user subscriptions are met in part by building replacement information handling systems with separated keyboards having a useful life remaining that aligns with end user keyboard usage patterns tracked over time, benchmarked performance metrics and end user subscription performance characteristics.

A factor analysis apparatus includes: an acquisition unit that acquires, from factor analysis data, time-series data of an objective variable representing a result of an event and time-series data of an explanatory variable representing a factor of an event; a criterion-value setting unit that sets, based on the time-series data of the objective variable, a plurality of objective-variable criterion values; an influence degree calculation unit that learns the set plurality of objective-variable criterion values and the acquired time-series data of the explanatory variable, generates a relational expression between the objective-variable criterion value and the explanatory variable for each of the objective-variable criterion values, and extracts, from the generated relational expression, a coefficient of the explanatory variable and the explanatory variable corresponding to the coefficient; and an output unit that outputs the extracted coefficient as an influence degree, and outputs an explanatory variable name associated with the extracted explanatory variable.

A control module is adapted to control technical equipment by processing batch-run data from the technical equipment. The control module operates according to parameters that are obtained by a parameter module. The module receives a reference plurality of multi-variate reference time series with data values from sources that are related to the equipment. There are time series with measurement values and time series with data that describes particular manufacturing operations during a batch-run time interval. The module splits the time interval into phases by determining transitions between the particular manufacturing operations, and divides the time series into particular phase-specific partial series. For each phase separately, and for the phase-specific partial series in combination, the module differentiates phase-specific time series into relevant partial time series or non-relevant partial time series and set the parameters accordingly.

To determine a quality indicator of production batch-run of a production process, a computer compares time-series with multi-source data from a reference batch-run and time-series with multi-source data from the production batch-run. Before comparing, the computer converts multi-variate time-series to uni-variate time-series, by first multiplying data values of source-specific uni-variate time-series with source-specific factors from a conversion factor vector and second summing up the multiplied data values according to discrete time points. The source-specific factors of the conversion factor vector are obtained earlier by processing reference data, including the determination of characteristic portions of the time-series, converting, aligning by time-warping and evaluating displacement in time between characteristic portions before alignment and after alignment.