A method including: obtaining one or more models configured for predicting a process metric of a manufacturing process based on inputting process data; and using a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determining a performance indication of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.

Process recipe data associated a process to be performed for a substrate at a process chamber is provided as input to a trained machine learning model. A set of process recipe settings for the process that minimizes scratching on one or more surfaces of the substrate is determined based on one or more outputs of the machine learning model. The process is performed for the substrate at the process chamber in accordance with the determined set of process recipe settings.

One or more processors generate a feature set describing evolution of a state space of a manufacturing system from time series data of sensors measuring values of control parameters and exogenous parameters of the manufacturing system, and measuring values of feature parameters of components produced by the manufacturing system. The one or more processors also generate from the feature set predicted values of at least one of the feature parameters, and alter at least one of the control parameters according to the feature set and the predicted values to drive the predicted values toward a target value or target values.

Example embodiments are disclosed of systems and methods for predicting failure probabilities of future product tests of a testing sequence based on outcomes of prior tests. Predictions are made by a machine-learning-based model (MLM) trained with a set of test-result sequence records (TRSRs) including test values and pass/fail indicators (PRIs) of completed tests. Within training epochs over the set, iterations are carried out over each TRSR. Each iteration involves sub-iterations carried out successively over test results of the TRSR. Each sub-iteration involves (i) inputting to the MLM values of a given test and those of tests earlier in the sequence while masking those later in the sequence, (ii) computing probabilities of test failures for the masked tests found later in the sequence than the given test, and (iii) applying the PFIs of test results later in the sequence than the given test as ground-truths to update parameters of the MLM.

The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies in the second production lot using the predictive distribution of the product characteristics data and the process quality inspection data from the second production lot; if no anomalies are detected in the second production lot, updating the Gaussian process regression model using the process quality inspection data from the second production lot; setting target values for one or more values in the process quality inspection data based on the predictive distribution of the product characteristic; and adjusting settings of one or more manufacturing steps based on the target values.

A quality control device that controls quality of a product manufactured through a plurality of processes, includes a prediction model generation unit that generates a prediction model to predict quality of a product with respect to unknown process data by performing learning using known process data obtained from the plurality of processes and a measured value of quality of the product with respect to the known process data as learning data; a quality prediction unit that derives a predictive value of quality of each of a plurality of products, which are manufactured after the prediction model is generated, on the basis of the prediction model using process data of the plurality of products as input data; and an inspection target decision unit that decides the product for which the predictive value having the smallest margin with respect to a preset standard is obtained as an inspection target, among the plurality of predictive values of quality obtained by the quality prediction unit.

A process control system according to one embodiment of the present invention comprises: a first system for generating thickness information about an internal defect layer included in a carbon steel product; and a second system which receives the thickness information about the internal defect layer from the first system through a network, and which controls an etching process for removing at least a part of the internal defect layer from the carbon steel product by using the thickness information about the internal defect layer, wherein the first system provides the second system with a calculation module necessary for the second system to control the etching process, and the second system provides the first system with the information necessary for the first system to update the calculation module.

A computing system may include a virtual cross metrology engine configured to construct a given virtual metrology model. The given virtual metrology model may take, as inputs, process parameters applied for the given step of a semiconductor fabrication process. The virtual cross metrology engine may also be configured to construct a subsequent virtual metrology model, and the subsequent step is performed after the given step in the semiconductor fabrication process. Doing so may include determining inputs for the subsequent virtual metrology model from a combination of the process parameters applied for the given step of the semiconductor fabrication process, process parameters applied for the subsequent step of the semiconductor fabrication process, and a wafer value for the given step of the semiconductor fabrication process that the given virtual metrology model is configured to predict.

A system and method of electric arc welding that includes a welding apparatus having an electric arc welder torch with sensors to determine the absolute position of the torch tip and the relative position of the torch tip to the weld joint during automatic welding. Combining absolute and relative positional data can be used to adjust the path of the robot during automated or robotic welding in response to variations in the weld joint.

A low toughness workpiece cutting apparatus, a low toughness workpiece manufacturing method and a low toughness workpiece manufacturing program for predicting an occurrence of defect and/or non-occurrence of defect before a cutting process of low toughness material. A defect prediction device is provided with a storage device, a processor and an interface. The storage device stores tool data that represent physical characteristics and a shape of a tool, cutting data that represent a group of parameters of a cutting process to be performed to a workpiece by use of the tool and material data that represent physical characteristics and a shape of the workpiece. The processor performs an analysis of deformation of the workpiece due to a cutting force and an analysis of fracture due to the deformation, and performs a prediction of an occurrence of defect and/or a non-occurrence of defect of the workpiece due to the cutting process.