Abstract: The present disclosure describes a system and a method for an ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Abstract: The present disclosure describes a system and a method for an ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Abstract: A method for monitoring a transport vehicle is provided. The method includes the operations as follows. A transport vehicle is scanned by a monitor during the transport vehicle is operated on a rail to acquire a vehicle pattern of the transport vehicle. The vehicle pattern of the transport vehicle is analyzed. An abnormal transport vehicle is determined based on the vehicle pattern. The monitor is placed nearby the rail. A method for transport vehicle maintenance is also provided.

Abstract: The present disclosure describes a system and a method for an ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Abstract: The present disclosure describes a system and a method for a ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Abstract: The present disclosure describes a system and a method for a ion implantation (IMP) process. The system includes an ion implanter configured to scan an ion beam over a target for a range of angles, a tilting mechanism configured to support and tilt the target, an ion-collecting device configured to collect a distribution and a number of ejected ions from the ion beam scan over the target, and a control unit configured to adjust a tilt angle based on a correction angle determined based on the distribution and number of ejected ions.

Abstract: A method and system for removing control action effects from inline measurement data for tool condition monitoring is disclosed. An exemplary method includes determining a control action effect that contributes to an inline measurement, wherein the inline measurement indicates a wafer characteristic of a wafer processed by a process tool; and evaluating the inline measurement without the control action effect contribution to determine a condition of the process tool.

Abstract: A system, a method, and a non-transitory computer readable storage medium for controlling an ion implanter are disclosed herein. The system includes a sample module and a control module. The sample module is configured to generate a summarized value from process data of the ion implanter, and the process data correspond to a control parameter. The control module is configured to tune a control parameter, and the control module performs an ion implantation by releasing tools of the ion implanter in accordance with the control parameter when the summarized value meets a predetermined stability requirement.

Abstract: The present disclosure provides various methods for tool condition monitoring, including systems for implementing such monitoring. An exemplary method includes receiving data associated with a process performed on wafers by an integrated circuit manufacturing process tool; and monitoring a condition of the integrated circuit manufacturing process tool using the data. The monitoring includes evaluating the data based on an abnormality identification criterion, an abnormality filtering criterion, and an abnormality threshold to determine whether the data meets an alarm threshold. The method may further include issuing an alarm when the data meets the alarm threshold.

Abstract: The present disclosure provides various methods for tuning process parameters of a process tool, including systems for implementing such tuning. An exemplary method for tuning process parameters of a process tool such that the wafers processed by the process tool exhibit desired process monitor items includes defining behavior constraint criteria and sensitivity adjustment criteria; generating a set of possible tool tuning process parameter combinations using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with a sensitivity of the process monitor items to each process parameter, the behavior constraint criteria, and the sensitivity adjustment criteria; generating a set of optimal tool tuning process parameter combinations from the set of possible tool tuning process parameter combinations; and configuring the process tool according to one of the optimal tool tuning process parameter combinations.

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: A method of automatically determining process parameters for processing equipment includes processing at least one first substrate in the processing equipment at a first time; and processing at least one second substrate in the processing equipment at a second time. The method includes collecting data on process monitors for the at least one first substrate; and the at least one second substrate. The method includes receiving the data by a multiple-input-multiple-output (MIMO) optimization system. The method includes revising a sensitivity matrix, by a MIMO optimizer, using the data and an adaptive-learning algorithm, wherein the adaptive-learning algorithm revises the sensitivity matrix based on a learning parameter which is related to a rate of change of the processing equipment over time. The method includes determining a set of process parameters for the processing equipment by the MIMO optimizer, wherein the MIMO optimizer uses the revised sensitivity matrix to determine the process parameters.

Abstract: A system, a method, and a non-transitory computer readable storage medium for controlling an ion implanter are disclosed herein. The system includes a sample module and a control module. The sample module is configured to generate a summarized value from process data of the ion implanter, and the process data correspond to a control parameter. The control module is configured to tune a control parameter, and the control module performs an ion implantation by releasing tools of the ion implanter in accordance with the control parameter when the summarized value meets a predetermined stability requirement.

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: The present disclosure provides various methods for tuning process parameters of a process tool, including systems for implementing such tuning. An exemplary method for tuning process parameters of a process tool such that the wafers processed by the process tool exhibit desired process monitor items includes defining behavior constraint criteria and sensitivity adjustment criteria; generating a set of possible tool tuning process parameter combinations using process monitor item data associated with wafers processed by the process tool, sensitivity data associated with a sensitivity of the process monitor items to each process parameter, the behavior constraint criteria, and the sensitivity adjustment criteria; generating a set of optimal tool tuning process parameter combinations from the set of possible tool tuning process parameter combinations; and configuring the process tool according to one of the optimal tool tuning process parameter combinations.

Abstract: A method of automatically determining process parameters for processing equipment includes processing at least one first substrate in the processing equipment at a first time; and processing at least one second substrate in the processing equipment at a second time. The method includes collecting data on process monitors for the at least one first substrate; and the at least one second substrate. The method includes receiving the data by a multiple-input-multiple-output (MIMO) optimization system. The method includes revising a sensitivity matrix, by a MIMO optimizer, using the data and an adaptive-learning algorithm, wherein the adaptive-learning algorithm revises the sensitivity matrix based on a learning parameter which is related to a rate of change of the processing equipment over time. The method includes determining a set of process parameters for the processing equipment by the MIMO optimizer, wherein the MIMO optimizer uses the revised sensitivity matrix to determine the process parameters.

Abstract: The present disclosure provides various methods for tool condition monitoring, including systems for implementing such monitoring. An exemplary method includes receiving data associated with a process performed on wafers by an integrated circuit manufacturing process tool; and monitoring a condition of the integrated circuit manufacturing process tool using the data. The monitoring includes evaluating the data based on an abnormality identification criterion, an abnormality filtering criterion, and an abnormality threshold to determine whether the data meets an alarm threshold. The method may further include issuing an alarm when the data meets the alarm threshold.

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

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: A method and system for removing control action effects from inline measurement data for tool condition monitoring is disclosed. An exemplary method includes determining a control action effect that contributes to an inline measurement, wherein the inline measurement indicates a wafer characteristic of a wafer processed by a process tool; and evaluating the inline measurement without the control action effect contribution to determine a condition of the process tool.