Embodiments relate to a device for correcting a robotic arm, including: a first robotic arm positioned in a vacuum transmission chamber; a first jig wafer comprising a first wafer body and a first jig positioned on a front surface of the first wafer body; a first distance measuring sensor positioned at a center position of a back surface of the first wafer body and configured to detect whether a center of the first jig wafer is aligned with a center of a wafer chuck; a second distance measuring sensor positioned on the front surface of the first wafer body and on an outside of the first jig and configured to detect a lifting height of the first robotic arm when the first robotic arm controls a pick-and-place operation the first jig wafer on an upper surface of the wafer chuck.

A method includes receiving, from one or more sensors associated with manufacturing equipment, current trace data associated with producing, by the manufacturing equipment, a plurality of products. The method further includes performing signal processing to break down the current trace data into a plurality of sets of current component data mapped to corresponding component identifiers. The method further includes providing the plurality of sets of current component data and the corresponding component identifiers as input to a trained machine learning model. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data and causing, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment.

A method is disclosed that includes the operations below: determining, by a processing unit, that arrival times of a lot arrived at N process stages are less than processing times of the lot predetermined to be processed at the N process stages, N being a positive integer; comparing, by the processing unit, idle times of multiple tools in the N process stages; and processing the lot with a first tool of the tools at each one of the N process stages, wherein the first tool of the tools has a shortest idle time.

A method of preparing a self-aligned via on a semiconductor workpiece includes using an integrated sequence of processing steps executed on a common manufacturing platform hosting a plurality of processing modules including one or more film-forming modules, one or more etching modules, and one or more transfer modules. The integrated sequence of processing steps include receiving the workpiece into the common manufacturing platform, the workpiece having a pattern of metal features in a dielectric layer wherein exposed surfaces of the metal features and exposed surfaces of the dielectric layer together define an upper planar surface; selectively etching the metal features to form a recess pattern by recessing the exposed surfaces of the metal features beneath the exposed surfaces of the dielectric layer using one of the one or more etching modules; and depositing an etch stop layer over the recess pattern using one of the one or more film-forming modules.

A method of generating training spectra for training of a neural network includes generating a plurality of theoretically generated initial spectra from an optical model, sending the plurality of theoretically generated initial spectra to a feedforward neural network to generate a plurality of modified theoretically generated spectra, sending an output of the feedforward neural network and empirically collected spectra to a discriminatory convolutional neural network, determining that the discriminatory convolutional neural network does not discriminate between the modified theoretically generated spectra and empirically collected spectra, and thereafter, generating a plurality of training spectra from the feedforward neural network.

The present application relates to the technical field of semiconductors, and in particular, to a wafer scheduling method and a wafer scheduling apparatus for an etching equipment. The wafer scheduling method includes: obtaining a wafer processing request, where the wafer processing request includes at least process information of wafers and an equipment processing parameter of the etching equipment; responding to the wafer processing request, and determining a wafer scheduling parameter corresponding to the process information and the equipment processing parameter, based on the process information, the equipment processing parameter, and a preset wafer scheduling policy, where the wafer scheduling parameter is used to determine a transfer time for transferring the wafers to the etching equipment for processing; and performing wafer scheduling processing on the wafers by using the wafer scheduling parameter. In this way, the wafer processing productivity of the etching equipment can be improved.

The present disclosure provides a method and an apparatus of controlling a semiconductor manufacturing device, a medium and a semiconductor manufacturing device. The method of controlling a semiconductor manufacturing device includes: receiving a control instruction, and cutting off or turning on a first airflow path; and when the first airflow path is cut off based on the control instruction, driving an air pumping terminal of an air pumping pipe to be connected to an air outlet terminal of an air intake pipe, and turning on a negative pressure generating device and pumping air from the air intake pipe; or when the first airflow path is turned on based on the control instruction, driving the air pumping terminal to be disconnected from the air outlet terminal of the air intake pipe, and turning off the negative pressure generating device and stopping pumping.

A method includes receiving first metrology data associated with first substrates produced by manufacturing equipment. The method further includes training a first machine learning model with data input including the first metrology data to generate a first trained machine learning model. The first trained machine learning model is capable of reducing dimensionality of second metrology data associated with second substrates produced by second manufacturing equipment to perform corrective actions associated with the second manufacturing equipment.

A wafer defect test apparatus in which a defect prediction performance is improved and a simulation time is shortened is provided. The wafer defect test apparatus comprises a wafer variable generator which receives a first structural measurement data and a first process condition data of a first wafer, and a second structural measurement data and a second process condition data of a second wafer, generates a first process variable and a second process variable based on the first structural measurement data and the first process condition data, and generates a third process variable and a fourth process variable based on the second structural measurement data and the second process condition data, an abnormal wafer index generating circuit which generates a first wafer vector of the first process variable and second process variable, generates a second wafer vector of the third process variable and fourth process variable, calculates a first Euclidean distance between the first wafer vector and the second wafer vector, calculates a first Cosine distance between the first wafer vector and the second wafer vector, and generates a first abnormal wafer index of the first wafer based on a product of the first Euclidean distance and the first Cosine distance, and a prediction model generating circuit which receives a first characteristic variable which is a test result of the first wafer, and generates a wafer defect prediction model through a regression based on the first process variable, the second process variable, the first characteristic variable, and the first abnormal wafer index.

A tool is equipped with a torque sensor, suited for a tightening task of a fastening component of a device such as a fluid control system that requires a large number of fastening components for assembly and has a narrow space for access to the fastening components, and capable of automatically detecting a tightening torque. The tool includes a torque sensor capable of detecting a tightening torque for tightening a fastening component acting on a bit. The torque sensor initiates measurement of the tightening torque when the tightening torque detected exceeds a set threshold value, completes measurement when the tightening torque detected falls below the set threshold value and a set time elapses, and outputs torque-related data formed on the basis of measurement data from measurement initiation to measurement completion and including a measurement time. The torque-related data includes a peak value of the measurement data.