A method includes acquiring, by a vibro-acoustic sensor in a vicinity of a machine, vibro-acoustic signals due to an operation of the machine. The acquired vibro-acoustic signals are analyzed so as to estimate at least two operational parameters of the machine, selected from a group of the operational parameters consisting of an uptime factor, a production speed factor, and a production quality factor of the machine. An overall equipment effectiveness (OEE) of the machine is computed, the computed OEE including at least one of a minimum and a product of the at least two estimated operational parameters.

Anomaly detection and remedial recommendation techniques for improving the quality and yield of microelectronic products are provided. In one aspect, a method for quality and yield improvement via anomaly detection includes: collecting time series sensor data during individual steps of a semiconductor manufacturing process; calculating anomaly scores for each of the individual steps using a predictive model; and implementing changes to the semiconductor manufacturing process based on the anomaly scores. A system for quality and yield improvement via anomaly detection is also provided.

A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.

A device includes a catalog storage unit that stores a catalog having versatility; a catalog control unit that specifies the catalog corresponding to a target machine tool to be subjected to state determination based on machine tool information including an item of facility type and acquires the catalog from the catalog storage unit; a feature quantity extraction unit that extracts a feature quantity from sensor data detected from the target machine tool; a sensor data processing unit that performs state determination of the target machine tool based on the feature quantity distribution included in the acquired catalog and the feature quantity extracted from the sensor data; a feature quantity tuning unit that performs tuning of the feature quantity distribution by mapping the extracted feature quantity to the feature quantity distribution; and a catalog updating unit that updates the catalog based on the feature quantity distribution after tuning.

A method of measuring quality in a machining operation of a device includes measuring lost motion for at least one axis of the device during the machining operation, calculating a spindle lag of a spindle of the device during the machining operation, determining a lost motion deviation, determining a spindle lag deviation, and identifying a machining defect based on the lost motion deviation coinciding with the spindle lag deviation.

A method of measuring quality in a machining operation of a device includes measuring lost motion for at least one axis of the device during the machining operation, calculating a spindle lag of a spindle of the device during the machining operation, determining a lost motion deviation, determining a spindle lag deviation, and identifying a machining defect based on the lost motion deviation coinciding with the spindle lag deviation.

A production system, a drive and an operating method provide the functions of quality measurement and mechanism diagnosis. The production system has a hierarchical processing structure. The drive includes a real system driving module and a virtual system driving module. The real system driving module generates a mechanism real operation parameter information. In a system identification mode, the drive creates at least one virtual mechanism model. The drive generates a mechanism stimulation operation parameter information according to a processing strategy of a controller, the mechanism real operation parameter information and the at least one virtual mechanism model. The controller performs a quality measurement operation, performs a mechanism diagnosis operation and/or adjusts the processing strategy according to the mechanism real operation parameter information and the mechanism stimulation operation parameter information.

To provide a manufacturing process analysis method for specifying a hindering factor that causes a variation in product performance and for stabilizing product performance. A manufacturing process analysis method comprises: a step for collecting product data indicating the quality of a product and process data indicating manufacturing conditions of a product; a step for standardizing the process data so that the data are converted into an intermediate function; a step for performing principal component analysis on the intermediate function to derive a principal component load amount and a principal component score of the process data; a step for applying cluster analysis to the principal component score to classify manufacturing process lots into a plurality of groups; a step for determining relative merit of each group on the basis of product data soundness corresponding to the principal component score belonging to the group; and a step for specifying a hindering factor that contributes to the relative merit of the group.

A production process analysis method for stabilizing the quality of the products or services. A production process analysis method includes: a step for identifying a good lot included in a group determined to be the most excellent with respect to each of a plurality of states constituting a production process; a step for classifying, in the case where at least one good lot is not shared among the plurality of states, the plurality of states into an arbitrarily selected selection state and other non-selection states, and determining again a highest-ranking group in the non-selection state that includes the good lot in the selection state as the most excellent group; and a step for identifying factors that characterize the group determined as the most excellent.

A computer-implemented method of automatically generating inspection templates of a plurality of known good fasteners to identify fasteners at an inspection station is provided. The method includes providing a data entry mechanism to provide content needed to identify a plurality of unidentified mixed fasteners. The method also includes storing the content in a database, extracting the content from the database and creating the inspection templates from the extracted content. Each of the templates including a fastener profile and a set of features. Each of the features includes a range of acceptable values. Each of the templates has a fastener identification code associated therewith.