Abstract: In some embodiments, a computer-implemented method of improving industrial process control by finding significant multi-variate correlations within a plurality of variables representing sensor data is provided. A computing system obtains time series data streams for the plurality of variables. The computing system generates pairwise correlation values between the variables of the plurality of variables. The computing system determines a variable of interest from the plurality of variables, and performs a graph search to determine one or more significant multi-variate correlations between variables from the plurality of variables and the variable of interest. Variables are filtered from the graph search using a heuristic based on the pairwise correlation values. The multi-variate correlations are provided to support the industrial process control.

Abstract: In some embodiments, a computer-implemented method for managing semiconductor manufacturing data is provided. A computing system receives an incoming data record generated by a semiconductor fabrication plant. The incoming data record includes at least one measured value and a set of context values. The computing system determines one or more context documents that represent the set of context values of the incoming data record. The computing system stores, in a data store, a decomposed fab process record that includes a representation of the at least one measured value and the one or more context documents.

Abstract: In some embodiments, a computer-implemented method of controlling a semiconductor manufacturing process is provided. A computing system generates predicted metrology values for a current run and a next run by providing metrology forecast inputs to a metrology forecast model. The computing system generates an updated recipe for executing at least one semiconductor manufacturing process step using the predicted metrology values for the current run and the next run.

Abstract: A method for detecting anomalies in a physical system generates from a set of physics rules and a process graph representing the system a set of candidate physics models that assign physics rules to portions of the process graph representing sensors. Candidate physics models are rejected if an error between the models and sensor data exceed a predetermined error tolerance. Supervised learning is used to train a machine learning model to predict an error between the physics models and the sensor data. The predicted error and predicted sensor measurements from the physics models are then used to detect anomalies using unsupervised learning on a distribution of error between the predicted sensor measurements and the sensor data.

Abstract: A method for analyzing time series sensor data of a physical system represented by a process graph retrieves sensor data streams from stored sensor time series data. Each of the sensor data streams comprises a sequence of time-value pairs and is associated with a sensor identifier, a time offset, and a sampling period. A metric data stream is produced from the retrieved sensor data streams in accordance with a stored physics model of the physical system. Producing the metric data stream includes i) synchronizing the sensor data streams by adjusting time offsets of the sensor data streams and adding interpolated values and times to the sensor data streams to produce synchronized streams with equal sampling periods; and ii) performing a point-wise computation over values of the sensor data streams in accordance with the physics model.