Abstract:
The present invention may include performing a first measurement on a wafer of a first lot of wafers via an omniscient sampling process, calculating a first set of process tool correctables utilizing one or more results of the measurement performed via an omniscient sampling process, randomly selecting a set of field sampling locations of the wafer of a first lot of wafers, calculating a second set of process tool correctables by applying an interpolation process to the randomly selected set of field sampling locations, wherein the interpolation process utilizes values from the first set of process tool correctables for the randomly selected set of field sampling locations in order to calculate correctables for fields of the wafer of the first lot not included in the set of randomly selected fields, and determining a sub-sampling scheme by comparing the first set of process tool correctables to the second set of correctables.
Abstract:
The present invention may include performing a first measurement process on a wafer of a lot of wafers, wherein the first measurement process includes measuring one or more characteristics of a plurality of targets distributed across one or more fields of the wafer, determining a set of process tool correctables for a residual larger than a selected threshold level utilizing a loss function, wherein the loss function is configured to fit a model for one or more process tools, as a function of field position, to one or more of the measured characteristics of the plurality of targets, wherein the set of process tool correctables includes one or more parameters of the model that act to minimize the difference between a norm of the residual and the selected threshold, and utilizing the determined process tool correctables to monitor or adjust one or more processes of the process tools.
Abstract:
A method of characterizing a process by selecting the process to characterize, selecting a parameter of the process to characterize, determining values of the parameter to use in a test matrix, specifying an eccentricity for the test matrix, selecting test structures to be created in cells on a substrate, processing the substrate through the process using in each cell the value of the parameter as determined by the eccentric test matrix, measuring a property of the test structures in the cells, and developing a correlation between the parameter and the property.
Abstract:
The present invention may include measuring tool induced shift (TIS) on at least one wafer of a lot of wafers via an omniscient sampling process, randomly generating a plurality of sub-sampling schemes, each of the set of randomly generated sub-sampling schemes having the same number of sampled fields, measuring TIS at each location of each of the randomly generated sub-sampling schemes, approximating a set of TIS values for each of the randomly generated sub-sampling schemes utilizing the TIS measurements from each of the randomly generated sub-sampling schemes, wherein each set of TIS values for each of the randomly generated sub-sampling schemes is calculated utilizing an interpolation process configured to approximate a TIS value for each location not included in a randomly generated sub-sampling scheme, and determining a selected sub-sampling scheme by comparing each of the calculated sets of TIS values to the measured TIS of the omniscient sampling process.
Abstract:
The present invention may include performing a first measurement on a wafer of a first lot of wafers via an omniscient sampling process, calculating a first set of process tool correctables utilizing one or more results of the measurement performed via an omniscient sampling process, randomly selecting a set of field sampling locations of the wafer of a first lot of wafers, calculating a second set of process tool correctables by applying an interpolation process to the randomly selected set of field sampling locations, wherein the interpolation process utilizes values from the first set of process tool correctables for the randomly selected set of field sampling locations in order to calculate correctables for fields of the wafer of the first lot not included in the set of randomly selected fields, and determining a sub-sampling scheme by comparing the first set of process tool correctables to the second set of correctables.
Abstract:
Apparatus and methods are provided for predicting a plurality of unknown parameter values (e.g. overlay error or critical dimension) using a plurality of known parameter values. In one embodiment, the method involves training a neural network to predict the plurality of parameter values. In other embodiments, the prediction process does not depend on an optical property of a photolithography tool. Such predictions may be used to determine wafer lot disposition.