Abstract: Techniques are provided for classifying runs of a recipe within a manufacturing environment. Embodiments monitor a plurality of runs of a recipe to collect runtime data from a plurality of sensors within a manufacturing environment. Qualitative data describing each semiconductor devices produced by the plurality of runs is determined. Embodiments characterize each run into a respective group, based on an analysis of the qualitative data, and generate a data model based on the collected runtime data. A multivariate analysis of additional runtime data collected during at least one subsequent run of the recipe is performed to classify the at least one subsequent run into a first group. Upon classifying the at least one subsequent run, embodiments output for display an interface depicting a ranking sensor types based on the additional runtime data and the description of relative importance of each sensor type for the first group within the data model.

Abstract: Embodiments presented herein provide techniques for managing data in manufacturing systems. One embodiment includes receiving a set of data from a plurality of devices operating in a manufacturing environment. A portion of the set of data is written by a data management application to both a relational database and a distributed storage cluster that includes a plurality of storage nodes in a distributed computing environment. Upon receiving a query to extract a subset of data from the set of data, the query is analyzed to determine attributes of the query. Based, in part on the analysis, one of the relational database and the distributed storage cluster is selected for processing the query.

Abstract: Techniques are provided for classifying runs of a recipe within a manufacturing environment. Embodiments monitor a plurality of runs of a recipe to collect runtime data from a plurality of sensors within a manufacturing environment. Qualitative data describing each semiconductor devices produced by the plurality of runs is determined. Embodiments characterize each run into a respective group, based on an analysis of the qualitative data, and generate a data model based on the collected runtime data. A multivariate analysis of additional runtime data collected during at least one subsequent run of the recipe is performed to classify the at least one subsequent run into a first group. Upon classifying the at least one subsequent run, embodiments output for display an interface depicting a ranking sensor types based on the additional runtime data and the description of relative importance of each sensor type for the first group within the data model.

Abstract: Embodiments presented herein provide techniques for managing data in manufacturing systems. One embodiment includes receiving a set of data from a plurality of devices operating in a manufacturing environment. A portion of the set of data is written by a data management application to both a relational database and a distributed storage cluster that includes a plurality of storage nodes in a distributed computing environment. Upon receiving a query to extract a subset of data from the set of data, the query is analyzed to determine attributes of the query. Based, in part on the analysis, one of the relational database and the distributed storage cluster is selected for processing the query.