Abstract: A method includes performing a semiconductor fabrication process on a plurality of substrates. The plurality of substrates are divided into a first subset and a second subset. A rework process is performed on the second subset of the plurality of substrates but not on the first subset. A respective mean value of at least one exposure parameter for a lithography process is computed for each respective one of the first and second subsets of the plurality of substrates. A scanner overlay correction and a mean correction are applied to expose a second plurality of substrates on which the rework process has been performed. The mean correction is based on the computed mean values.

Abstract: Among other things, systems and techniques are provided for detecting defects on a wafer based upon non-correctable error data yielded from a scan of the wafer to determine a topology of the wafer. The non-correctable error data is reconstructed to generate a non-correctable error image map, which is transformed to generate a projection. In some embodiments, the non-correctable error image map is transformed via a feature extraction transform such as a Hough transform or a Radon transform. In some embodiments, the projection is compared to a set of rules to identify a signature in the non-correctable error image map indicative of a defect.

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 method of optimizing a design for manufacture (DFM) simulation. The method includes receiving an integrated circuit (IC) design data having a feature, receiving a process data having a parameter or a plurality of parameters, performing the DFM simulation, and optimizing the DFM simulation. The performing the DFM simulation includes generating a simulation output data using the IC design data and the process data. The optimizing the DFM simulation includes generating a performance index of the parameter or the plurality of parameters by the DFM simulation. The optimizing the DFM simulation includes adjusting the parameter or the plurality of parameters at outer loop, middle loop, and the inner loop. The optimizing the DFM simulation also includes locating a nadir of the performance index of the parameter or the plurality of parameters over a range of the parameter or the plurality of parameters.

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: A method includes performing a semiconductor fabrication process on a plurality of substrates. The plurality of substrates are divided into a first subset and a second subset. A rework process is performed on the second subset of the plurality of substrates but not on the first subset. A respective mean value of at least one exposure parameter for a lithography process is computed for each respective one of the first and second subsets of the plurality of substrates. A scanner overlay correction and a mean correction are applied to expose a second plurality of substrates on which the rework process has been performed. The mean correction is based on the computed mean values.

Abstract: A system and method of automatically detecting failure patterns for a semiconductor wafer process is provided. The method includes receiving a test data set collected from testing a plurality of semiconductor wafers, forming a respective wafer map for each of the wafers, determining whether each respective wafer map comprises one or more respective objects, selecting the wafer maps that are determined to comprise one or more respective objects, selecting one or more object indices for selecting a respective object in each respective selected wafer map, determining a plurality of object index values in each respective selected wafer map, selecting an object in each respective selected wafer map, determining a respective feature in each of the respective selected wafer, classifying a respective pattern for each of the respective selected wafer maps and using the respective wafer fingerprints to adjust one or more parameters of the semiconductor fabrication process.

Abstract: A method for analyzing abnormalities in a semiconductor processing system provides performing an analysis of variance on a production history associated with each of a plurality of tools at each of a plurality of process steps for each of a plurality of processed wafers, and key process steps are identified. A regression analysis on a plurality of measurements of the plurality of wafers at each process step is performed and key measurement parameters are identified. An analysis of covariance on the key measurement parameters and key process steps, and the key process steps are ranked based on an f-ratio, therein ranking an abnormality of the key process steps. Further, the plurality of tools associated with each of the key process steps are ranked based on an orthogonal t-ratio associated with an analysis of covariance, therein ranking an abnormality each tool associated with the key process steps.

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: 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 describes a method of optimizing a design for manufacture (DFM) simulation. The method includes receiving an integrated circuit (IC) design data having a feature, receiving a process data having a parameter or a plurality of parameters, performing the DFM simulation, and optimizing the DFM simulation. The performing the DFM simulation includes generating a simulation output data using the IC design data and the process data. The optimizing the DFM simulation includes generating a performance index of the parameter or the plurality of parameters by the DFM simulation. The optimizing the DFM simulation includes adjusting the parameter or the plurality of parameters at outer loop, middle loop, and the inner loop. The optimizing the DFM simulation also includes locating a nadir of the performance index of the parameter or the plurality of parameters over a range of the parameter or the plurality of parameters.

Abstract: A system and method of automatically detecting failure patterns for a semiconductor wafer process is provided. The method includes receiving a test data set collected from testing a plurality of semiconductor wafers, forming a respective wafer map for each of the wafers, determining whether each respective wafer map comprises one or more respective objects, selecting the wafer maps that are determined to comprise one or more respective objects, selecting one or more object indices for selecting a respective object in each respective selected wafer map, determining a plurality of object index values in each respective selected wafer map, selecting an object in each respective selected wafer map, determining a respective feature in each of the respective selected wafer, classifying a respective pattern for each of the respective selected wafer maps and using the respective wafer fingerprints to adjust one or more parameters of the semiconductor fabrication process.

Abstract: A system and method for aligning a probe, such as a wafer-level test probe, with wafer contacts is disclosed. An exemplary method includes receiving a wafer containing a plurality of alignment contacts and a probe card containing a plurality of probe points at a wafer test system. A historical offset correction is received. Based on the historical offset correct, an orientation value for the probe card relative to the wafer is determined. The probe card is aligned to the wafer using the orientation value in an attempt to bring a first probe point into contact with a first alignment contact. The connectivity of the first probe point and the first alignment contact is evaluated. An electrical test of the wafer is performed utilizing the aligned probe card, and the historical offset correction is updated based on the orientation value.

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 system and method of automatically detecting failure patterns for a semiconductor wafer process is provided. The method includes receiving a test data set collected from testing a plurality of semiconductor wafers, forming a respective wafer map for each of the wafers, determining whether each respective wafer map comprises one or more respective objects, selecting the wafer maps that are determined to comprise one or more respective objects, selecting one or more object indices for selecting a respective object in each respective selected wafer map, determining a plurality of object index values in each respective selected wafer map, selecting an object in each respective selected wafer map, determining a respective feature in each of the respective selected wafer, classifying a respective pattern for each of the respective selected wafer maps and using the respective wafer fingerprints to adjust one or more parameters of the semiconductor fabrication process.

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.