Methods, systems, and computer programs are presented for predicting the performance of semiconductor manufacturing equipment operations. One method includes an operation for obtaining machine-learning (ML) models, each model related to predicting a performance metric for an operation of a semiconductor manufacturing tool. Further, each ML model utilizes features defining inputs for the ML model. The method further includes an operation for receiving a process definition for manufacturing a product with the semiconductor manufacturing tool. One or more ML models are utilized to estimate a performance of the process definition used in the semiconductor manufacturing tool. Additionally, the method includes presenting, on a display, results showing the estimate of the performance of the manufacturing of the product. In some aspects, the use of hybrid models improves the predictive accuracy of the system by augmenting the capabilities of data-driven models with the reinforcement provided by the physics-based models.

A computer-implemented method to control technical equipment that performs a production batch-run of a production process, the technical equipment providing data in a form of time-series from a set of data sources, the data sources being related to the technical equipment, includes: accessing a reference time-series with data from a previously performed batch-run of the production process, the reference time-series being related to a parameter for the technical equipment; and while the technical equipment performs the production batch-run: receiving a production time-series with data, identifying a sub-series of the reference time-series, and comparing the received time-series and the sub-series of the reference time-series, to provide an indication of similarity or non-similarity, in case of similarity, controlling the technical equipment during a continuation of the production batch-run, by using the parameter as control parameter.

A machining control system includes: a numerical control device controlling a machine tool; and a robot control device communicating with the numerical control device and controlling a robot having a plurality of drive axes. The numerical control device includes: a coordinate position command generation unit generating a coordinate position command specifying a target coordinate position at each time of a leading end part of the robot, based on a machining program; and a communication unit sending the current target coordinate position to the robot control device. The robot control device includes: a target drive position calculation unit calculating a target drive position of each of the plurality of drive axes to position the leading end part at the target coordinate position; and a drive command generation unit generating a drive command to each of the drive axes to position the drive axes at the calculated target drive position.

The program code, when executed by a processor, causes the processor to input fabrication data including a plurality of parameters associated with a semiconductor fabricating process to a framework to generate a first class for analyzing the fabrication data, to extract a first parameter targeted for analysis and a second parameter associated with the first parameter from the plurality of parameters and generate a second class for analyzing the first parameter as a sub class of the first class, to modify the first parameter and the second parameter into a data structure having a format appropriate to store in the second class, so as to be stored in the second class, to perform data analysis on the first parameter and the second parameter, to transform the first parameter and the second parameter into corresponding tensor data, and to input the tensor data to the machine learning model.

A computer-implemented method for quality inspection of a component of a manufacturing device includes obtaining operational data relating to operation of the manufacturing device. The operational data includes a time series of one or more physical properties of the manufacturing device. Status data relating to a component of the manufacturing device is obtained. The status data includes events relating to and/or characteristic properties relevant for utilization of the component within the manufacturing device. The computer-implemented method includes labelling one or more subsets of the operational data by associating one or more of the events and/or characteristic properties to the one or more subsets and providing the one or more subsets as labelled training data for training a machine learning model. The machine learning model serves for outputting a quality indicator based on the labelled training data input. The trained machine learning model is provided for quality inspection.

An inspection system may receive inspection datasets from a defect inspection system associated with inspection of one or more samples, where an inspection dataset of the plurality of inspection datasets associated with a defect includes values of two or more signal attributes and values of one or more context attributes. An inspection system may further label each of the inspection datasets with a class label based on respective positions of each of the inspection datasets in a signal space defined by the two or more signal attributes, where each class label corresponds to a region of the signal space. An inspection system may further segment the inspection datasets into two or more defect groups by training a classifier with the values of the context attributes and corresponding class labels for the inspection datasets, where the two or more defect groups are identified based on the trained classifier.

A method that is used for self-learning manufacturing scheduling for a flexible manufacturing system that is used to produce at least a product is provided. The manufacturing system consists of processing entities that are interconnected through handling entities. The manufacturing scheduling will be learned by a reinforcement learning system on a model of the flexible manufacturing system. The model represents at least a behavior and a decision making of the flexible manufacturing system. The model is realized as a petri net.

An order of the processing entities and the handling entities is interchangeable, and therefore, the whole arrangement is very flexible.

A system and computer-implemented method for training a machine learnable model to estimate a relative scale of objects in an image. A feature extractor and a scale estimator comprising a machine learnable model part are provided. The feature extractor may be pretrained, while the scale estimator may be trained by the system and method to transform feature maps generated by the feature extractor into relative scale estimates of objects. For that purpose, the scale estimator may be trained on training data in a specific yet non-supervised manner which may not require scale labels. During inference, the scale estimator may be applied to several image patches of an image. The resulting patch-level scale estimates may be combined into a scene geometry map which may be indicative of a geometry of a scene depicted in the image.

An abnormality detector includes a signal output unit for detecting a sign of an abnormality based on a physical quantity acquired from a manufacturing machine and outputting a signal; and a machine learning device including state observation unit for observing, as a state variable representing a present state of the environment, physical quantity data indicating the physical quantity related to an operation of the manufacturing machine from the manufacturing machine; a label data acquisition unit for acquiring, as label data, operation state data indicating an operation state of the manufacturing machine; a learning unit for learning the operation state of the manufacturing machine with respect to the physical quantity, using the state variable and the label data; and an estimation result output unit for estimating the operation state of the manufacturing machine using a learning result by the learning unit and outputting an estimation result.

A method including receiving a data packet over a network, the data packet having a size. The method also includes parsing the data packet into a header and a body. The method also includes identifying a protocol type from the header and the size. The method also includes identifying a signal characteristic of signal data in the body. The method also includes identifying a classification of a source sensor which generated the data packet based on the protocol type and the signal characteristic. The method also includes generating a metadata file based on the source sensor. The method also includes labeling the data packet with the metadata file to form a labeled data packet.