A method of detecting and classifying anomalies during semiconductor processing includes executing a wafer recipe a semiconductor processing system to process a semiconductor wafer; monitoring sensor outputs from a sensors that monitor conditions associated with the semiconductor processing system; providing the sensor outputs to models trained to identify when the conditions associated with the semiconductor processing system indicate a fault in the semiconductor wafer; receiving an indication of a fault from at least one of the models; and generating a fault output in response to receiving the indication of the fault.

Methods, systems, and non-transitory computer readable medium are described for sensor metrology data integration. A method includes receiving sets of sensor data and sets of metrology data. Each set of sensor data includes corresponding sensor values associated with producing corresponding product by manufacturing equipment and a corresponding sensor data identifier. Each set of metrology data includes corresponding metrology values associated with the corresponding product manufactured by the manufacturing equipment and a corresponding metrology data identifier. The method further includes determining common portions between each corresponding sensor data identifier and each corresponding metrology data identifier. The method further includes, for each of the sensor-metrology matches, generating a corresponding set of aggregated sensor-metrology data and storing the sets of aggregated sensor-metrology data to train a machine learning model. The trained machine learning model is capable of generating one or more outputs for performing a corrective action associated with the manufacturing equipment.

A method of monitoring an electrical machine, wherein the method includes: a) obtaining temperature measurement values of the temperature at a plurality of locations of the electrical machine, b) obtaining estimated temperatures at the plurality of locations given by a thermal model of the electrical machine, the thermal model including initial weight parameter values, c) minimizing a difference between the temperature measurement values and the estimated temperatures by finding optimal weight parameter values, d) storing the initial weight parameter values to thereby obtain a storage of used weight parameter values, and updating the optimal weight parameter values as new initial weight parameter values, and repeating steps a)-d) over and over during operation of the electrical machine.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

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.

Techniques for automatically determining, without user input, one or more sources of a variation in the behavior of a target process element operating to control a process in a process plant include using a process element alignment map to determine process elements upstream of the target process element in the process; performing a data analysis on data corresponding to the upstream elements with respect to the target element to determine behavior time offsets, strengths of impact, and impact delays; and determining the source(s) based on the data analysis outputs. Techniques may include automatically defining the process element alignment map by obtaining and processing data from a plurality of diagrams or data sources of the process and/or plant. Furthermore, the techniques may be performed during plant run-time by any high-volume, high density device such as centralized or embedded big data appliances, controllers, field or I/O devices, and/or by an unsupervised application.

A quality management apparatus includes an acquisition unit and an extraction unit. The acquisition unit acquires, about products to be managed, a rate of occurrence of a malfunction on an occurrence period basis, an operation condition of the products, and a manufacturing condition for the products. The extraction unit classifies the rate of occurrence into layers under the operation condition, and extracts, for each layer under the operation condition, a relationship between the rate of occurrence and the manufacturing condition.

There is provided a monitoring device including: an input unit that receives input of process data related to a plant; a model generation unit that generates a model representing a relationship between the process data based on the input process data; a determination unit that determines an operation state of the plant in a first determination mode using the model generated based on a forecast value of the process data input by a person or in a second determination mode using the model generated based on an actually measured value of the process data; and a display unit that displays a determination result by the determination unit.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

In order to provide a method for predicting process deviations in an industrial-method plant, for example a painting plant, by means of which process deviations are predictable simply and reliably, it is proposed according to the invention that the method should comprise the following: automatic generation of a prediction model; prediction of process deviations during operation of the industrial-method plant, using the prediction model.