摘要:
The present disclosure provides a method of fabricating a semiconductor device. The method includes collecting a plurality of manufacturing data sets from a plurality of semiconductor processes, respectively. The method includes normalizing each of the manufacturing data sets in a manner so that statistical differences among the manufacturing data sets are reduced. The method includes establishing a database that includes the normalized manufacturing data sets. The method includes normalizing the database in a manner so that the manufacturing data sets in the normalized database are statistically compatible with a selected one of the manufacturing data sets. The method includes predicting performance of a selected one of the semiconductor processes by using the normalized database. The selected semiconductor process corresponds to the selected manufacturing data set. The method includes controlling a semiconductor processing machine in response to the predicted performance.
摘要:
A method of extending advanced process control (APC) models includes constructing an APC model table including APC model parameters of a plurality of products and a plurality of work stations. The APC model table includes empty cells and cells filled with existing APC model parameters. Average APC model parameters of the existing APC model parameters are calculated, and filled into the empty cells as initial values. An iterative calculation is performed to update the empty cells with updated values.
摘要:
The present disclosure provides a method of fabricating a semiconductor device. The method includes collecting a plurality of manufacturing data sets from a plurality of semiconductor processes, respectively. The method includes normalizing each of the manufacturing data sets in a manner so that statistical differences among the manufacturing data sets are reduced. The method includes establishing a database that includes the normalized manufacturing data sets. The method includes normalizing the database in a manner so that the manufacturing data sets in the normalized database are statistically compatible with a selected one of the manufacturing data sets. The method includes predicting performance of a selected one of the semiconductor processes by using the normalized database. The selected semiconductor process corresponds to the selected manufacturing data set. The method includes controlling a semiconductor processing machine in response to the predicted performance.
摘要:
In accordance with an embodiment, a method for exception handling comprises accessing an exception type for an exception, filtering historical data based on at least one defined criterion to provide a data train comprising data sets, assigning a weight to each data set, and providing a current control parameter. The data sets each comprise a historical condition and a historical control parameter, and the weight assigned to each data set is based on each historical condition. The current control parameter is provided using the weight and the historical control parameter for each data set.
摘要:
System and method for implementing wafer acceptance test (“WAT”) advanced process control (“APC”) are described. In one embodiment, the method comprises performing a key process on a sample number of wafers of a lot of wafers; performing a key inline measurement related to the key process to produce metrology data for the wafers; predicting WAT data from the metrology data using an inline-to-WAT model; and using the predicted WAT data to tune a WAT APC process for controlling a tuning process or a process APC process.
摘要:
System and method for data mining and feature tracking for fab-wide prediction and control are described. One embodiment is a system comprising a database for storing raw wafer manufacturing data; a data mining module for processing the raw wafer manufacturing data to select the best data therefrom in accordance with at least one of a plurality of knowledge-, statistic-, and effect-based processes; and a feature tracking module associated with the data mining module and comprising a self-learning model wherein a sensitivity of the self-learning model is dynamically tuned to meet real-time production circumstances, the feature tracking module receiving the selected data from the data mining module and generating prediction and control data therefrom; wherein the prediction and control data are used to control future processes in the wafer fabrication facility.
摘要:
System and method for data mining and feature tracking for fab-wide prediction and control are described. One embodiment is a system comprising a database for storing raw wafer manufacturing data; a data mining module for processing the raw wafer manufacturing data to select the best data therefrom in accordance with at least one of a plurality of knowledge-, statistic-, and effect-based processes; and a feature tracking module associated with the data mining module and comprising a self-learning model wherein a sensitivity of the self-learning model is dynamically tuned to meet real-time production circumstances, the feature tracking module receiving the selected data from the data mining module and generating prediction and control data therefrom; wherein the prediction and control data are used to control future processes in the wafer fabrication facility.
摘要:
System and method for implementing multi-resolution advanced process control (“APC”) are described. One embodiment is a method including obtaining low resolution metrology data and high resolution metrology data related to a process module for performing a process on the wafer. A process variable of the process is modeled as a function of the low resolution metrology data to generate a low-resolution process model and the process variable is modeled as a function of the high resolution metrology data to generate a high-resolution process model. The method further includes calibrating the low resolution process model; combining the calibrated low resolution process model with the high resolution process model to generate a multi-resolution process model that models the process variable as a function of both the low resolution metrology data and the high resolution metrology data; and analyzing a response of the multi-resolution process model and the low and high resolution metrology data to control performance of a process module.
摘要:
System and method for implementing multi-resolution advanced process control (“APC”) are described. One embodiment is a method for fabricating ICs from a semiconductor wafer comprising performing a first process on the semiconductor wafer; taking a first measurement indicative of an accuracy with which the first process was performed; and using the first measurement to generate metrology calibration data, wherein the metrology calibration data includes an effective portion and a non-effective portion. The method further comprises removing the non-effective portion from the metrology calibration data and modeling the effective portion with a metrology calibration model; combining the metrology calibration model with a first process model to generate a multi-resolution model, wherein the first process model models an input-output relationship of the first process; and analyzing a response of the multi-resolution model and second measurement data to control performance a second process.
摘要:
One embodiment is a method for fabricating ICs from a semiconductor wafer. The method includes performing a first process on the semiconductor wafer; taking a first measurement indicative of an accuracy with which the first process was performed; and using the first measurement to generate metrology calibration data, wherein the metrology calibration data includes an effective portion and a non-effective portion. The method further includes removing the non-effective portion from the metrology calibration data and modeling the effective portion with a metrology calibration model; combining the metrology calibration model with a first process model to generate a multi-resolution model, wherein the first process model models an input-output relationship of the first process; and analyzing a response of the multi-resolution model and second measurement data to control performance a second process.