Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

A substrate processing condition setting method includes acquiring, causing, and setting. In the acquiring, a plurality of estimation processing results are acquired by inputting a plurality of processing conditions to a trained model that is subjected to machine training based on a training processing condition and a processing result obtained by processing a substrate under the training processing condition. In the causing, a display section is caused to display an image based on the estimation processing results. In the setting, one processing condition corresponding to one estimation processing result of the estimation processing results is set, as an actual processing condition in substrate processing, based on the image displayed on the display section.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

A system may include a first semiconductor processing station configured to deposit a material on a first semiconductor wafer, a second semiconductor processing station configured perform measurements indicative of a thickness of the material after the material has been deposited on the first semiconductor wafer, and a controller. The controller may be configured to receive the measurements from the second station; provide an input based on the measurements to a trained model that is configured to generate an output that adjusts an operating parameter of the first station such that the thickness of the material is closer to a target thickness; and causing the first station to deposit the material on a second wafer using the operating parameter as adjusted by the output.

Broadly speaking, the present techniques provide a method and system for controlling a wafer production process in real-time using a trained machine learning, ML, model. Advantageously, the ML model uses multiple sensed parameters to determine a state of a plasma used in the wafer production process, and this can be used to adjust at least one control parameter of a plasma reactor used in the wafer production process to reduce process variability.

A system for estimating an abnormality includes an industrial device that controls one or more jigs such that the one or more jigs press an object to perform a work process, and processing circuitry that acquires operation data that is related to an operation of the industrial device and is measured at multiple time points after the object is pressed by the one or more jigs, and perform an estimation estimating an abnormality based on the operation data acquired.

An additive manufacturing system comprises an apparatus arranged to distribute layer of metallic powder across a build plane and a power source arranged to emit a beam of energy at the build plane and fuse the metallic powder into a portion of a part. The system includes a processor configured to steer the beam of energy across the build plane and receive data generated by one or more sensors that detect electromagnetic energy emitted from the build plane when the beam of energy fuses the metallic powder. The received data is converted into one or more parameters that indicate one or more conditions at the build plane while the beam of energy fuses the metallic powder. The one or more parameters are used as input into a machine learning algorithm to detect one or more defects in the fused metallic powder.

Provided is a computer-implemented method for determining at least one quality attribute for at least one defect of interest, including the steps: a. providing an input data set including the at least one defect of interest; b. determining the at least one quality attribute for the at least one defect of interest using a classification algorithm based on the input data set; and c. providing the determined at least one quality attribute and/or additional output information as output. Further, a computing unit and a computer program product are provided.

The optimization support device includes a first conversion unit that converts an operating condition parameter indicating an operating condition of a process for producing a product into a state parameter indicating a state of the process, and a second conversion unit that converts the state parameter into a quality parameter indicating a quality of the product.

A manufacturing condition output apparatus of an embodiment is a manufacturing condition output apparatus which outputs a manufacturing condition of a product. The manufacturing condition output apparatus outputs change degree information which is information regarding degrees of change of values regarding defect probabilities for a plurality of variables relating to manufacturing of the product from model information of a model generated through machine learning on a basis of manufacturing data of the product and inspection result data of the product, as a manufacturing condition.