A data processing method includes: obtaining a defect type of a sample set in response to a first input of a user on a first interface, the sample set including samples, each sample having a first parameter used to represent a defect degree of the sample with regard to the defect type and a second parameter used to represent device informations of sample production devices through which the sample passes; calculating yield purity indexes of sample production devices on the samples based on first parameters and second parameters of the samples, so as to obtain influencing parameters of the sample production devices, an influencing parameter of each sample production device being used to represent an influence degree to which the sample production device affects an occurrence of the defect type on the samples; and displaying the influencing parameters of the sample production devices on a second interface.

A system for inspecting and validating processes performed on a continuous web of fabric in an automated apparel manufacturing environment. The continuous web of fabric can move in a step wise fashion across a work area where tooling can perform one or more processes on the continuous web of fabric. At least one projector is provided to display an image onto the continuous web of fabric the image including a first image related to an article to be manufactured and a second image related to a reference grid. The continuous web of fabric and the first and second images are viewed by a camera, and data related to the viewed first and second images and the continuous web of fabric can be sent to a computer implemented control center which can analyze the data to determine whether a deviation or error exists regarding the manufacturing process.

Wafers that begin as flat surfaces during a semiconductor manufacturing process may become warped or bowed as layers and features are added to an underlying substrate. This warpage may be detected between manufacturing processes by rotating the wafer adjacent to a displacement sensor. The displacement sensor may generate displacement data relative to a baseline measurement to identify areas of the wafer that bow up or down. The displacement data may then be mapped to locations on the wafer relative to an alignment feature. This mapping may then be used to adjust parameters in subsequent semiconductor processes, including adjusting how a carrier head on a polishing process holds or applies pressure to the wafer as it is polished. A model may be trained to provide control signals for a polishing/cleaning process, or to generate metrology data.

A method for manufacturing a plurality of components, each of the plurality of components including at least one electrical feedthrough, in which a functional element is fastened in a feedthrough opening in a base body by way of an electrically insulating material, an information being acquired in association with each of the plurality of components, the method comprising the steps of: providing, in one of a plurality of manufacturing steps of the method, each of the plurality of components or one of a plurality of pre-stages of each of the plurality of components with an information store, at least one of (i) the information being stored in the information store, and (ii) an identifier is stored in the information store and the information is stored in a database in association with the identifier.

A method includes receiving one or more fingerprint dimensions to be used to generate a fingerprint. The method further includes receiving trace data associated with a manufacturing process. The method further includes applying the one or more fingerprint dimensions to the trace data to generate at least one feature. The method further includes generating the fingerprint based on the at least one feature. The method further includes causing, based on the fingerprint, performance of a corrective action associated with one or more manufacturing processes.

A computer-implemented method for providing a trained function for performing a workpiece quality control includes receiving a plurality of training machining datasets, wherein different training high-frequency machining datasets are representative for the quality of different workpieces, transforming the plurality of training machining datasets into the time-frequency domain to generate a plurality of training time-frequency domain datasets, and training a function based on the plurality of training time-frequency domain datasets, wherein the function is based on an autoencoder. The autoencoder has input layers, output layers and a hidden layer. The plurality of training time-frequency domain datasets are provided to the input layers and the output layers during training, and a trained autoencoder function is outputted.

A semiconductor analysis system includes a machining device that machines semiconductor wafer to prepare a thin film sample for observation, a transmission electron microscope device that acquires a transmission electron microscope image of the thin film sample, and a host control device that controls the machining device and the transmission electron microscope device. The host control device evaluates the thin film sample based on the transmission electron microscope image, updates machining conditions based on an evaluation result of the thin film sample, and outputs the updated machining conditions to the machining device.

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.

The present disclosure discloses an Industrial Internet of Things (IIoT) with a dual independent platform, which comprises a user platform, a service platform, a management platform, a sensor network platform and an object platform that interact in turn. The service platform adopts centralized layout, and the management platform and the sensor network platform adopt independent layout. The present disclosure also discloses a control method of the IIoT with the dual independent platform. The present disclosure builds the IIoT based on the five platform structure, in which the sensor network platform and the management platform are arranged independently, and each corresponding platform includes a plurality of independent sub-platforms, so that the independent sensor network platform and the management platform can be used for each production line device to form an independent data processing channel and transmission channel, and reduce the data processing capacity and transmission capacity of each platform.

A production system for producing products from raw materials by a production process with several steps has a number of production facilities that perform the steps and a control device. The control device determines a control target value by referring to information about group combinations specified in accordance with the relative merits of the manufacturing condition routes followed by respective lots during the production process. The relative merits are determined on the basis of quality items of the lots, classified for inter-step combinations of groups, which are classified on the basis of manufacturing conditions at the steps.