The disclosure relates to a method and a device for producing a product and to a computer program product. The product is produced in at least one production step. A quality control check is optionally carried out after at least one of the production steps to determine a quality index of the product in question. To save on the quality control check, a quality indicator of the product in question is determined using production data. The production data are advantageously provided by sensors. The quality indicator of the product in question may be calculated using an adaptive algorithm. The adaptive algorithm may be taught and/or improved using quality indices of a quality control unit and the corresponding production data. The adaptive algorithm may be taught with the aid of a further computing unit, in particular in a cloud.

Embodiments provide systems and methods for utilizing acoustic sensors to detect defects via online or in situ monitoring of additive manufacturing (AM) processes. Sensors may capture acoustic waves associated with AM manufacturing operations. The acoustic emissions in combination with other sensing data, such as cameras or thermometers, may be used to characterize the state of the AM process, such as to detect a defect has occurred or confirm a defect has not occurred. When defects are detected, the AM process may be stopped to prevent further processing of a defective part. When defects are predicted as likely to occur, operational parameters of the AM device or process may be adjusted to mitigate the occurrence of a defect. The techniques disclosed herein enable detection of defects that occur underneath the surface of the part being manufactured, as well as correct issues with the AM device or process before a defect occurs.

A system, method and software for generating and receiving information about the AC, DC, RF, voltage, and other characteristics and information provided by components in a system. The information can provide insight into the operational characteristics and functionality of the components, as well as the process and system the components are being used within. This information may be used for preventative maintenance of the components, and to detect changes, issues, failures, events, problems, etc. in the process and system.

A test artifact for additive manufacturing is provided. The artifact comprises an additively manufactured singular continuous body between 100 cc and 6 cc in bounding box volume and between 50 cc and 3 cc in solid body. The body comprises at least three fiducials positioned for identification of locations in cross-section after sectioning of the artifact and at least three geometries that are unique from each other when exposed in cross section.

There is provided a configuration that includes: a main controller configured to, when executing a process recipe including a specific step of executing a sub-recipe, control a process controller to execute the sub-recipe a predetermined number of times to perform a predetermined process to a substrate: and a device management controller configured to collect device data during an execution of the process, recipe and store the device data in a storage part. The device management controller is further configured to: search the storage part; acquire the device data in a designated step among respective steps constituting the sub-recipe for a number of times of execution of the sub-recipe; calculate a first standard deviation of the device data acquired for the number of times of execution; and compare the first standard deviation with a threshold value and generate an alarm when the first standard deviation exceeds the threshold value.

The present disclosure provides systems and methods for controlling a semiconductor manufacturing equipment. The control system includes an inspection unit capturing a set of images of the semiconductor manufacturing equipment, a sensor interface receiving the set of images and generating at least one input signal for a database server, and a control unit. The control unit includes a front end subsystem, a calculation subsystem, and a message and feedback subsystem. The calculation subsystem receives the data signal from the front end subsystem, wherein the calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether a malfunction has occurred in the semiconductor manufacturing equipment and to generate an output signal. The message and feedback subsystem generates an alert signal and a feedback signal according to the output signal, and the alert signal is transmitted to a user of the semiconductor manufacturing equipment.

In a workpiece production method a plurality of nominally similar workpieces are produced in a production process on one production machine. The order or time of production of some of the workpieces on the production machine is recorded. Some of the workpieces recorded are measured at two or more inspection stations. Dimensions or points of one workpiece are measured at one of the inspection stations, and corresponding dimensions or points of another of the workpieces are measured at another of the inspection stations. The results of the measurements of corresponding dimensions or points made at the two or more inspection stations are analysed together, taking account of the order or time of production of the workpieces. An output signal is produced based on the analysing of the results together. The output signal indicates performance of the production machine or of one or more of the inspection stations.

A method for qualifying a manufacturing process for a first component including a common geometric feature includes obtaining manufacturing data for the common geometric feature. The manufacturing data is associated with one or more qualified second components. The one or more qualified second components are different than the first component. Each of the one or more qualified second components include the common geometric feature. The method further includes modeling the manufacturing process for the common geometric feature of the one or more qualified second components using the manufacturing data, modeling the manufacturing process for the common geometric feature of the first component, obtaining manufacturing process parameters for the manufacturing process for the common geometric feature of the one or more qualified second components, and qualifying the manufacturing process for the common geometric feature of the first component.

In order to specify an automated method and an apparatus enabling a quick, reliable and reproducible quality assurance and being easily integrated in existing machines, a cutting machine is specified, comprising: a conveyor (10) for conveying flexible material (100); a machining unit (20) for cutting the flexible material (100) into material parts (200); a recognition unit (30) for detecting the flexible material (100) and/or at least one cut material part (200), the recognition unit (30) being arranged in the conveying direction after the machining unit (20), and a control unit (50) configured to generate a quality result and to control the cutting machine based on information of the recognition unit (30) and/or on marking information.

Provided is a method for monitoring a plasma-related process in a plasma tool. The method includes measuring data associated with the plasma-related process using a plurality of sensors while executing the plasma-related process on a wafer. Respective data measured by each sensor of the plurality of sensors are input into a respective individual estimation method to output a respective individual wafer state of the wafer, which results in a plurality of individual wafer states. The respective individual estimation method is configured to estimate the respective individual wafer state using at least the respective data. The plurality of individual wafer states is input into an integrated estimation method to output an integrated wafer state of the wafer. The integrated estimation method is configured to estimate the integrated wafer state using at least the plurality of individual wafer states.