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 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 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.

Proactive management of semiconductor substrate tools. A machine learning model is used to predict future performance characteristics for such tools. In some examples, the model can diagnose issues with tools or with ambient conditions of the tools' environment. In some examples, the model can recommend one or more remedial actions to maintain adequate performance of the substrate tool.

This disclosure relates to a device, a method, and a program capable of removing erroneous data from learning data used for machine learning used in natural language processing, for example. The method includes storing a forward direction learned model of a discrete series converter. The model is trained based on a plurality of pairs of discrete series of texts. Each pair comprises a first discrete series indicates an input of discrete series. A second discrete series indicates an output of discrete series. The first discrete series and the second discrete series are correctly associated. The method further includes converting the first discrete series to the second discrete series, and generating a quality score using the forward direction learned model, using a second learning pair of discrete series texts including an error in relationship.

A method for regulating an injection molding process in which process setting variables of an injection molding machine are controlled via a regulating module, which receives data from a process-internal sensor system of the injection molding machine, data on the fabricating sequence of the injection molding component parts from an external sensor system and/or data on the quality of a fabricated injection molded component part from an online component part control, evaluates these data in a quality prognosis module and, as a function of the data evaluation, performs a change in the process setting variables of the injection molding machine, because of the change in the process setting values, the working point of the injection molding machine being changed so that the quality features of the injection molded component parts fabricated using the changed working point lie within the specified tolerances of the quality of the injection molded component parts.

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.

To carry out a container inspection task, a sensor device optically detects sensor data and camera images relating to the container parts, and a real-time evaluation device evaluates spatially resolved sensor data in real time using a machine learning container inspection model which includes a set of parameters which are set to values which were learned as a result of a machine learning method. A set of container part features based on a machine learning method is predetermined, and the detected, spatially resolved sensor data are evaluated in relation to a plant inspection task different from the container inspection task, based on the predetermined set of container part characteristics, a plant inspection variable being determined depending on the inspection result of the carried out plant inspection task, which is provided for the control and/or regulation of the container treatment plant.