Abstract: A system and method for monitoring a process tool of an integrated circuit manufacturing system are disclosed. An exemplary method includes defining zones of an integrated circuit manufacturing process tool; grouping parameters of the integrated circuit manufacturing process tool based on the defined zones; and evaluating a condition of the integrated circuit manufacturing process tool based on the grouped parameters.

Abstract: System and method for implementing a VM APC platform are described. In one embodiment, the VM APC system comprises a process tool for processing a plurality of wafers, a metrology tool for measuring a sample wafer of the plurality of wafers and generating actual metrology data therefor, and a VM model for predicting metrology data for each of the plurality of wafers. The actual metrology data is received from the metrology tool and used to update the VM model. Key variables of the virtual metrology model are updated only in response to a determination that the VM model is inaccurate and parameters of the VM model are updated responsive to receipt of the actual metrology data for the sample wafer of the plurality of wafers. The system also includes an APC controller for receiving the predicted metrology data and the actual metrology data and controlling an operation of the process tool based on the received data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A light bar includes a metal substrate, an electronic component, and a plurality of light source. The metal substrate has a folding line and an opening portion, wherein the folding line extends along a longer side of the metal substrate, and the opening portion connects an end of the folding line from a gap of a first edge of the metal substrate. The metal substrate is bent along the folding line, and a fastening portion and a bearing portion are formed on two sides of the folding line, wherein a circuit is formed on the bearing portion, the electronic component is disposed on the bearing portion and connects the circuit, and a gap is formed between the fastening portion and the electronic component. The light sources are disposed on the metal substrate, and the electronic component and the light sources are electrically coupled with the circuit.

Abstract: A method includes: initializing first and second variables; operating equipment based on the variables; measuring first and second parameters; determining a new value for the first variable based on the first parameter, and calculating a new value for the second variable based on the second parameter and the current value of the second variable; and repeating the operating, measuring, determining and calculating. According to a different aspect, an apparatus includes a computer-readable medium storing a computer program. When executed, the program causes: initializing of first and second variables; operating equipment based on the variables; receiving measured first and second parameters; determining a new value for the first variable based on the first parameter, and calculating a new value for the second variable based on the second parameter and the current value of the second variable; and repeating the operating, measuring, determining and calculating.

Abstract: A MIMO optimizer is used to identify tunable process parameters for processing equipment.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: System and method for implementing multi-resolution advanced process control (“APC”) are described. One embodiment is a method comprising obtaining low resolution metrology data and high resolution metrology data related to a process module for performing a process on the wafer; modeling a process variable of the process as a function of the low resolution metrology data to generate a low-resolution process model; and modeling the process variable as a function of the high resolution metrology data to generate a high-resolution process model.

Abstract: 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.

Abstract: System and method for implementing a VM APC platform are described. In one embodiment, the VM APC system comprises a process tool for processing a plurality of wafers, a metrology tool for measuring a sample wafer of the plurality of wafers and generating actual metrology data therefor, and a VM model for predicting metrology data for each of the plurality of wafers. The actual metrology data is received from the metrology tool and used to update the VM model. Key variables of the virtual metrology model are updated only in response to a determination that the VM model is inaccurate and parameters of the VM model are updated responsive to receipt of the actual metrology data for the sample wafer of the plurality of wafers.

Abstract: 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.

Abstract: 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.

Abstract: A method includes: initializing first and second variables; operating equipment based on the variables; measuring first and second parameters; determining a new value for the first variable based on the first parameter, and calculating a new value for the second variable based on the second parameter and the current value of the second variable; and repeating the operating, measuring, determining and calculating. According to a different aspect, an apparatus includes a computer-readable medium storing a computer program. When executed, the program causes: initializing of first and second variables; operating equipment based on the variables; receiving measured first and second parameters; determining a new value for the first variable based on the first parameter, and calculating a new value for the second variable based on the second parameter and the current value of the second variable; and repeating the operating, measuring, determining and calculating.

Abstract: The architecture of the controller of the present invention is mainly composed of three portions, which are a model predictive controller, a local controller (also called stably assistant controller), and a coordinator. This modified model predictive control structure merges two control loops by a coordinator. The control mode can be switched into either of them or provide a combined control action by adjustments of the coordinator. When the system moves out the range of the database, the coordinator will automatically detect the fault and switch to the stably assistant controller to provide the basic control quality to ensure the safety.