A method and device for predicting a defect. The method includes determining a sequence between a plurality of sub-models by modeling a production process into the plurality of sub-models, mapping production process data into each of the plurality of sub-models, determining, by a corresponding sub-model, output data comprising defect information on a potential defect occurring in a corresponding step, for each of the plurality of sub-models, predicting information associated with a defect in the production process based on the output data corresponding to each of the plurality of sub-models, and inputting the output data of each of the sub-models to a subsequent sub-model of the corresponding sub-model, based on the sequence.

A method of the present disclosure includes (a) retrieving from a memory a plurality of measured values of the factor variable, and a label indicating good or bad of the quality corresponding to each of the plurality of measured values, (b) dividing a factor variable space defined by the factor variable into a plurality of grids by equally dividing a range determined by a maximum value and a minimum value of the plurality of measured values for each factor variable, (c) setting a plurality of candidate areas each of which includes one grid or a plurality of adjacent grids, and deriving, for each of the plurality of candidate areas, a good density based on the label associated with the measured value that is within the candidate area, and (d) selecting one of the plurality of candidate areas as the factor variable area, based on the good density.

A method includes receiving, by a processing device, data indicative of one or more defects of a substrate processing in a substrate processing system using a process recipe, the data having a data type. The method further includes processing the data using a trained machine learning model that outputs information about the one or more defects. The method further includes determining one or more possible root causes for the one or more defects based at least in part on the information. The method further includes outputting a sequence of maintenance operations to be performed on the substrate processing system based on the one or more possible root causes for the one or more defects.

This disclosure relates to a method of machining articles from a disc comprising superhard material, such as polycrystalline diamond (PCD) or polycrystalline cubic boron nitride (PCBN). The method includes providing a disc having a diameter of no more than 100 mm and a thickness of no more than 10 mm, providing a nesting pattern, scanning the disc to identify and locate any flaws in the disc and subsequently creating a machining program that takes into account said flaws.