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: An apparatus, a system and a method are disclosed. An exemplary apparatus includes a wafer processing chamber. The apparatus further includes radiant heating elements disposed in different zones and operable to heat different portions of a wafer located within the wafer processing chamber. The apparatus further includes sensors disposed outside the wafer processing chamber and operable to monitor energy from the radiant heating elements disposed in the different zones. The apparatus further includes a computer configured to utilize the sensors to characterize the radiant heating elements disposed in the different zones and to provide a calibration for the radiant heating elements disposed in the different zones such that a substantially uniform temperature profile is maintained across a surface of the wafer.

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: An apparatus, a system and a method are disclosed. An exemplary apparatus includes a wafer processing chamber. The apparatus further includes radiant heating elements disposed in different zones and operable to heat different portions of a wafer located within the wafer processing chamber. The apparatus further includes sensors disposed outside the wafer processing chamber and operable to monitor energy from the radiant heating elements disposed in the different zones. The apparatus further includes a computer configured to utilize the sensors to characterize the radiant heating elements disposed in the different zones and to provide a calibration for the radiant heating elements disposed in the different zones such that a substantially uniform temperature profile is maintained across a surface of the wafer.

Abstract: An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.

Abstract: An apparatus, a system and a method are disclosed. An exemplary apparatus includes a wafer processing chamber. The apparatus further includes radiant heating elements disposed in different zones and operable to heat different portions of a wafer located within the wafer processing chamber. The apparatus further includes sensors disposed outside the wafer processing chamber and operable to monitor energy from the radiant heating elements disposed in the different zones. The apparatus further includes a computer configured to utilize the sensors to characterize the radiant heating elements disposed in the different zones and to provide a calibration for the radiant heating elements disposed in the different zones such that a substantially uniform temperature profile is maintained across a surface of the wafer.

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: An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.

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: An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.

Abstract: The present disclosure describes a semiconductor manufacturing apparatus. The apparatus includes a processing chamber designed to perform a process to a wafer; an electrostatic chuck (E-chuck) configured in the processing chamber and designed to secure the wafer, wherein the E-chuck includes an electrode and a dielectric feature formed on the electrode; a tuning structure designed to hold the E-chuck to the processing chamber by clamping forces, wherein the tuning structure is operable to dynamically adjust the clamping forces; a sensor integrated with the E-chuck and sensitive to the clamping forces; and a process control module for controlling the tuning structure to adjust the clamping forces based on pre-measurement data from the wafer and sensor data from the sensor.

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

Abstract: An apparatus, a system and a method are disclosed. An exemplary apparatus includes a wafer processing chamber. The apparatus further includes radiant heating elements disposed in different zones and operable to heat different portions of a wafer located within the wafer processing chamber. The apparatus further includes sensors disposed outside the wafer processing chamber and operable to monitor energy from the radiant heating elements disposed in the different zones. The apparatus further includes a computer configured to utilize the sensors to characterize the radiant heating elements disposed in the different zones and to provide a calibration for the radiant heating elements disposed in the different zones such that a substantially uniform temperature profile is maintained across a surface of the wafer.

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 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 comprises computing respective regression models for each of a plurality of failure bins based on a plurality of failures identified during wafer electrical tests. Each regression model outputs a wafer yield measure as a function of a plurality of device performance variables. For each failure bin, sensitivity of the wafer yield measure to each of the plurality of device performance variables is determined, and the device performance variables are ranked with respect to sensitivity of the wafer yield measure. A subset of the device performance variables which have highest rankings and which have less than a threshold correlation with each other are selected. The wafer yield measures for each failure bin corresponding to one of the selected subset of device performance variables are combined, to provide a combined wafer yield measure. At least one new process parameter value is selected to effect a change in the one device performance variable, based on the combined wafer yield measure.

Abstract: An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.

Abstract: An embodiment is a method for semiconductor processing control. The method comprises identifying a key process stage from a plurality of process stages based on a parameter of processed wafers, forecasting a trend for a wafer processed by the key process stage and some of the plurality of process stages based on the parameter, and dispatching the wafer to one of a first plurality of tools in a tuning process stage. The one of the first plurality of tools is determined based on the trend.