Abstract: In a metrology sampling method with a sampling rate decision scheme, a mean absolute percentage error (MAPE) and a maximum absolute percentage error (MaxErr) of visual metrology values of all workpieces in a set of determinative samples (DS), and various index values that can detect various status changes of a process tool (such as maintenance operation, parts changing, parameter adjustment, etc.), and/or information abnormalities of the process tool (such as abnormal process data, parameter drift/shift, abnormal metrology data, etc.) appearing in a manufacturing process are applied to develop an automated sampling decision (ASD) scheme for reducing a workpiece sampling rate while VM accuracy is still sustained.

Abstract: A virtual metrology based method for predicting machining quality of a machine tool is provided. In this method, each product accuracy item is correlated with operation paths of the machine tool. During a modeling stage, the machine tool is operated to process workpiece samples, and sample sensing data of the workpiece samples associated with the operation paths are collected during the operation of the machine tool. The sample sensing data of each workpiece sample is de-noised and converted into the sample feature data corresponding to each feature type. The workpiece samples are measured with respect to the product accuracy item and integrated into the feature data for building a predictive model, thereby obtaining quality predicted data for each product accuracy item. During a usage stage, accuracy item values of a workpiece are predicted using the feature data during processing the workpiece in accordance with the predictive models.

Abstract: In a metrology sampling method, various index values that can detect various status changes of a process tool (such as maintenance operation, parts changing, parameter adjustment, etc.), and/or information abnormalities of the process tool (such as abnormal process data, parameter drift/shift, abnormal metrology data, etc.) appear in a manufacturing process are applied to develop an intelligent sampling decision (ISD) scheme for reducing sampling rate while VM accuracy is still sustained. The indices includes a reliance Index (RI), a global similarity index (GSI), a process data quality index (DQIX) and a metrology data quality index (DQIy).

Abstract: A manufacturing execution system (MES) with virtual-metrology capabilities and a manufacturing system including the MES are provided. The MES is built on a middleware architecture (such as an object request broker architecture), and includes an equipment manager, a virtual metrology system (VMS), a statistical process control (SPC) system, an alarm manager and a scheduler. The manufacturing system includes a first process tool, a second process tool, a metrology tool, the aforementioned MES, a first R2R (Run-to-Run) controller and a second R2R controller.

Abstract: A method for screening samples for building a prediction model and a computer program product thereof are provided. When a set of new sample data is added to a dynamic moving window (DMW), a clustering step is performed with respect to all of the sets of sample data within the window for grouping the sets of sample data with similar properties as one group. If the number of the sets of sample data in the largest group is greater than a predetermined threshold, it means that there are too many sets of sample data with similar properties in the largest group, and the oldest sample data in the largest group can be deleted; if smaller than or equal to a predetermined threshold, it means that the sample data in the largest group are quite unique, and should be kept for building or refreshing the prediction model.

Abstract: A method for searching, analyzing, and optimizing process parameters and a computer product thereof are provided. At first, sets of process data that are generated when a process tool processes workpieces are obtained respectively, each set of process data including process parameters. Then, sets of metrology data measured by a metrology tool are obtained, wherein the sets of metrology data are corresponding to the sets of the process data in a one-to-one manner, each workpiece having at least one measurement point, each set of metrology data including at least one actual measurement value of at least one measurement item at the at least one measurement point. Thereafter, critical parameters are selected from the process parameters. Then, values of the critical parameters are adjusted to enable predicted measurement values of the measurement points of one workpiece to meet a quality target value.

Abstract: A bin allocation method of point light sources for constructing light source sets is provided. In this method, a matching matrix corresponding to each light source set product is provided for showing feasible combinations of bin codes of the point light sources which can be used for constructing the light source sets. Then, for reducing computation loading, the original matching matrix is reduced to a simplified matching matrix according to effective inventories of point light sources. Thereafter, the simplified matching matrix of each light source set product is applied to search for low exchangeable bin codes of the point tight sources among the light source set products. Then, the point light sources with the low exchangeable bin codes are precedently used for constructing the light source set products.

Abstract: A virtual metrology based method for predicting machining quality of a machine tool is provided. In this method, each product accuracy item is correlated with operation paths of the machine tool. During a modeling stage, the machine tool is operated to process workpiece samples, and sample sensing data of the workpiece samples associated with the operation paths are collected during the operation of the machine tool. The sample sensing data of each workpiece sample is de-noised and converted into the sample feature data corresponding to each feature type. The workpiece samples are measured with respect to the product accuracy item and integrated into the feature data for building a predictive model, thereby obtaining quality predicted data for each product accuracy item. During a usage stage, accuracy item values of a workpiece are predicted using the feature data during processing the workpiece in accordance with the predictive models.

Abstract: An advanced process control (APC) system, an APC method, and a computer program product, which, when executed, performs an APC method are provided for incorporating virtual metrology (VM) into APC. The present inventions uses a reliance index (RI) and a global similarity index (GSI) to adjust at least one controller gain of a run-to-run (R2R) controller when the VM value of a workpiece is adopted to replace the actual measurement value of the workpiece. The RI is used for gauging the reliability of the VM value, and the GSI is used for assessing the degree of similarity between the set of process data for generating the VM value and all the sets of historical process data used for building the conjecturing model.

Abstract: A baseline predictive maintenance method for a target device (TD) and a computer program product thereof are provided. Fresh samples which are generated when the target device produces workpieces just after maintenance are collected, and a new workpiece sample which is generated when the target device produces a new workpiece is collected. A plurality of modeling samples are used to build a TD baseline model in accordance with a conjecturing algorithm, wherein the modeling samples include the new workpiece sample and the fresh samples. A TD healthy baseline value for the new workpiece is computed by the TD baseline model, and a device health index (DHI), a baseline error index (BEI) and baseline individual similarity indices (ISIB) are computed, thereby achieving the goals of fault detection and classification (FDC) and predictive maintenance (PdM).

Abstract: A method for forecasting a WIP (work in process) output schedule and a computer program product thereof are provided. A plurality of sets of historical WIP data regarding a product generated in respective historical periods are first collected, in which the product has a maximum historical production cycle. Thereafter, a predetermined time is used to divide the maximum historical production cycle into intervals. Then, the quantities of historical WIPs appearing in the respective intervals are computed in accordance with output times of the historical WIPs recorded in each of the sets of historical WIP data, thereby obtaining output probability density data series. If the number of the historical periods is greater than or equal to a minimum model-building number, a predicted output probability density data series of a next period following the historical periods is conjectured by using the output probability density data series in accordance with a prediction algorithm.

Abstract: A virtual production control system (VPCS), and a virtual production control method and a computer program product thereof are provided. At first, the VPCS processes historical work-in-process (WIP) information and a current shipping plan sent from a supplier side, thereby obtaining a plurality of sets of WIP input/output historical data and a goods output schedule. Then, the VPCS performs an integer programming (IP) method to find the latest output schedule in accordance to the current shipping plan; uses a genetic algorithm (GA) to fit the historical distributed-parameters; adopts a neural network (NN) method to predict the future distributed-parameters of production; and finally utilizes a Petri Nets to simulate and obtain a latest feasible input schedule and a latest feasible output schedule.

Abstract: An advanced process control (APC) system, an APC method, and a computer program product, which, when executed, performs an APC method are provided for incorporating virtual metrology (VM) into APC. The present inventions uses a reliance index (RI) and a global similarity index (GSI) to adjust at least one controller gain of a run-to-run (R2R) controller when the VM value of a workpiece is adopted to replace the actual measurement value of the workpiece. The RI is used for gauging the reliability of the VM value, and the GSI is used for assessing the degree of similarity between the set of process data for generating the VM value and all the sets of historical process data used for building the conjecturing model.

Abstract: A server, a system and a method for automatic virtual metrology (AVM) are disclosed. The AVM system comprises a model-creation server and a plurality of AVM servers. The model-creation server is used to construct the first set of virtual metrology (VM) models (of a certain equipment type) including a VM conjecture model, a RI (Reliance Index) model, a GSI (Global Similarity Index) model, a DQIx (Process Data Quality Index) model, and a DQIy (Metrology Data Quality Index) model. In the AVM method, the model-creation server also can fan out or port the first set of VM models generated to other AVM servers of the same process apparatus (equipment) type, and each individual fan-out-acceptor's AVM server can perform automatic model refreshing processes so as to gain and maintain its VM models' accuracy.

Abstract: A manufacturing execution system (MES) with virtual-metrology capabilities and a manufacturing system including the MES are provided. The MES is built on a middleware architecture (such as an object request broker architecture), and includes an equipment manager, a virtual metrology system (VMS), a statistical process control (SPC) system, an alarm manager and a scheduler. The manufacturing system includes a first process tool, a second process tool, a metrology tool, the aforementioned MES, a first R2R (Run-to-Run) controller and a second R2R controller.

Abstract: A virtual production control system (VPCS), and a virtual production control method and a computer program product thereof are provided. At first, the VPCS processes historical work-in-process (WIP) information and a current shipping plan sent from a supplier side, thereby obtaining a plurality of sets of WIP input/output historical data and a goods output schedule. Then, the VPCS performs an integer programming (IP) method to find the latest output schedule in accordance to the current shipping plan; uses a genetic algorithm (GA) to fit the historical distributed-parameters; adopts a neural network (NN) method to predict the future distributed-parameters of production; and finally utilizes a Petri Nets to simulate and obtain a latest feasible input schedule and a latest feasible output schedule.

Abstract: A server, a system and a method for automatic virtual metrology (AVM) are disclosed. The AVM system comprises a model-creation server and a plurality of AVM servers. The model-creation server is used to construct the first set of virtual metrology (VM) models (of a certain equipment type) including a VM conjecture model, a RI (Reliance Index) model, a GSI (Global Similarity Index) model, a DQIx (Process Data Quality Index) model, and a DQIy (Metrology Data Quality Index) model. In the AVM method, the model-creation server also can fan out or port the first set of VM models generated to other AVM servers of the same process apparatus (equipment) type, and each individual fan-out-acceptor's AVM server can perform automatic model refreshing processes so as to gain and maintain its VM models' accuracy.

Abstract: A dual-phase virtual metrology method is disclosed for considering both promptness and accuracy by generating dual-phase virtual metrology (VM) values, wherein a Phase-I conjecture step emphasizes promptness by immediately calculating the Phase-I virtual metrology value (VMI) of a workpiece once the entire process data of the workpiece are completely collected; and a Phase-II conjecture step intensifies accuracy, which does not re-calculate the Phase-II virtual metrology values (VMII) of all the workpieces in the cassette until an actual metrology value (required for tuning or re-training purposes) of a selected workpiece in the same cassette is collected. Besides, the accompanying reliance index (RI) and global similarity index (GSI) of each VMI and VMII are also generated.

Abstract: A method for evaluating reliance level of a virtual metrology system is disclosed. In this method, a reliance index (RI) and a RI threshold value are calculated by analyzing the process data of production equipment, thereby determining if the virtual metrology result is reliable. Besides, in this method, a global similarity index (GSI) and individual similarity indexes (ISI) are also provided for defining the degree of similarity between the current set of process data and all of the sets of historical process data used for establishing the conjecture model, thereby assisting the RI in gauging the degree of reliance and locating the key parameter(s) that cause major deviation.

Abstract: A quality prognostics system and a quality prognostics method for predicting the product quality during manufacturing processes are disclosed, wherein the current production tool parameters sensed during the manufacturing process and several previous quality data collected from the measurement tool are utilized to predict the future product quality, and a conjecture modeling step and prediction modeling step are performed respectively. The conjecture modeling step itself also can be applied for the purpose of virtual metrology. Further, a self-searching step and a self-adjusting step are performed for searching the best combination of various parameters/functions used by the conjecture algorithm or prediction algorithm; and meeting the requirements of new equipment parameters and conjecture/prediction accuracy.