A quality control system may include a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors. The controller may implement two or more AI quality control (AIQC) modules associated with two or more process steps for fabricating a composite material, where each of the two or more AIQC modules is associated with a different one of the two or more process steps. A particular AIQC module may receive monitoring data associated with the particular process step for a workpiece, generate quality control data using a particular AI model, and update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.

A server for knowledge recommendation for defect review. The server includes a processor electronically coupled to an electronic storage device storing a plurality of knowledge files related to wafer defects. The processor is configured to execute a set of instruction to cause the server to: receive a request for knowledge recommendation for inspecting an inspection image from a defect classification server; search for a knowledge file in the electronic storage device that matches the inspection image; and transmit the search result to the defect classification server.

Data is received that is derived from each of a plurality of inspection camera modules forming part of a quality assurance inspection system. The data includes a feed of images of a plurality of objects passing in front of the respective inspection camera module. Thereafter, the received data is separately analyzed by each inspection camera module using at least one image analysis inspection tool. The results of the analyzing can be correlated for each inspection camera module on an object-by-object basis. The correlating can use timestamps for the images and/or detected unique identifiers within the images and can be performed by a cloud-based server and/or a local edge computer. Access to the correlated results can be provided to a consuming application or process.

To provide more test data during the manufacture of non-volatile memories and other integrated circuits, machine learning is used to generate virtual test values. Virtual test results are interpolated for one set of tests for devices on which the test is not performed based on correlations with other sets of tests. In one example, machine learning determines a correlation study between bad block values determined at die sort and photo-limited yield (PLY) values determined inline during processing. The correlation can be applied to interpolate virtual inline PLY data for all of the memory dies, allowing for more rapid feedback on the processing parameters for manufacturing the memory dies and making the manufacturing process more efficient and accurate. In another set of embodiments, the machine learning is used to extrapolate limited metrology (e.g., critical dimension) test data to all of the memory die through interpolated virtual metrology data values.

A product state estimation device includes: an examination result acquisition device that acquires an examination result related to a state of a product obtained through each shot by a die-casting machine; a time series data acquisition device that acquires time series data based on an output from a sensor that detects an operation state of the die-casting machine at each shot; a time series data manipulation device that performs manipulation that clips data of a predetermined time interval out of the time series data; an estimation model generation device that generates an estimation model by using a neural network with the examination result of the product and the manipulated time series data as learning data; and an estimation device that estimates information related to quality of the product based on the manipulated time series data obtained from a plurality of detection signals at each shot by using the estimation model.

A computing system may include a virtual cross metrology engine configured to construct a given virtual metrology model. The given virtual metrology model may take, as inputs, process parameters applied for the given step of a semiconductor fabrication process. The virtual cross metrology engine may also be configured to construct a subsequent virtual metrology model, and the subsequent step is performed after the given step in the semiconductor fabrication process. Doing so may include determining inputs for the subsequent virtual metrology model from a combination of the process parameters applied for the given step of the semiconductor fabrication process, process parameters applied for the subsequent step of the semiconductor fabrication process, and a wafer value for the given step of the semiconductor fabrication process that the given virtual metrology model is configured to predict.

A steel pipe collapse strength prediction model generation method, a steel pipe collapse strength prediction method, a steel pipe manufacturing characteristics determination method, and a steel pipe manufacturing method capable of highly accurately predicting the collapse strength of a steel pipe after forming or a coated steel pipe in consideration of the pipe-making strain during forming. Into a steel pipe collapse strength prediction model generated by the prediction model generation method, steel pipe manufacturing characteristics including the shape of a steel pipe to be predicted after forming, strength characteristics, and the pipe-making strain are input to predict the collapse strength after forming. Into a steel pipe collapse strength prediction model, steel pipe manufacturing characteristics including the shape of a coated steel pipe to be predicted after forming, strength characteristics, the pipe-making strain, and coating conditions are input to predict the collapse strength of the coated steel pipe.

Embodiments of the present disclosure provide systems and methods for enhancing the semiconductor manufacturing yield. Embodiments of the present disclosure provide a yield improvement system. The system comprises a training tool configured to generate training data based on receipt of one or more verified results of an inspection of a first substrate. The system also comprises a point determination tool configured to determine one or more regions on a second substrate to inspect based on the training data, weak point information for the second substrate, and an exposure recipe for a scanner of the second substrate.

When inspection data of a process inspection in a production line is input, a machine learning unit 420 of a prediction score calculation device 202 performs machine learning so as to output a prediction score of quality determination of a final inspection. In addition, a prediction score calculation unit 410 outputs a prediction score predicting the quality determination result of the final inspection from the inspection data of the process inspection using a machine learning model that has performed the machine learning. In addition, a threshold value determination unit 440 compares the prediction score calculated by the prediction score calculation unit 410 and determines a threshold value for predicting the quality determination from learning data, the prediction score, and cost data.

The present disclosure provides a system and a method for controlling a semiconductor manufacturing apparatus. The system includes an inspection unit capturing at least one image of a wafer, a sensor interface 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 tuning subsystem. The front-end subsystem receives the at least one input signal from the database server and performs a front-end process to generate a data signal. The calculation subsystem performs an artificial intelligence analytical process to determine, according to the data signal, whether damage marks have been caused by the semiconductor manufacturing apparatus and to generate an output signal. The message and tuning subsystem generates an alert signal and a feedback signal according to the output signal and transmits the alert signal to a user.