Abstract: A system and method for generating a quality parameter value of a semiconductor device wafer (SDW), during fabrication thereof, the method including designating a plurality of measurement site sets (MSSs) on the SDW, each of the MSSs including a first measurement-orientation site (FMS) and a second measurement-orientation site (SMS), the FMS and the SMS being different measurement sites on the SDW, generating a first measurement-orientation quality parameter dataset (FMQPD) by measuring features formed within each the FMS of at least one of the MSSs in a first measurement orientation, generating a second measurement-orientation quality parameter dataset (SMQPD) by measuring features formed within each the SMS of the at least one of the MSSs in a second measurement orientation and generating at least one tool-induced-shift (TIS)-ameliorated quality parameter value (TAQPV), at least partially based on the FMQPD and the SMQPD.

Abstract: A method includes generating a sequence of images of a semiconductor wafer from first and second detectors. The first detector images the wafer with first imaging parameters and the second detector images the wafer with second imaging parameters. The first or second imaging parameters are varied across the sequence of images to provide variation at sites across the wafer. The method includes providing a metric indicative of center-of-symmetry variations of first and second target features as a function of the varied imaging parameters based on the sequence of images. The method includes identifying a landscape of values of the varied image parameters for which the metric are below a predefined limit. The method includes generating a recipe for metrology measurements in which the varied imaging parameters are set to values within the landscape. The method includes generating metrology measurements from a production wafer based on the recipe.

Abstract: A metrology system may include an optical metrology sub-system to generate optical metrology measurements of optical metrology based on features of the optical metrology targets associated with at least one optical pitch and an additional metrology sub-system to generate additional metrology measurements of the optical metrology targets, where the additional metrology measurements have a higher resolution than the optical metrology measurements, and where the additional metrology sub-system further measures deviations of the optical metrology targets from a reference design. The system may further include a controller to generate accuracy measurements for the optical metrology targets based on the measurements, identify variations of a lithography process based on the deviations, correlate the accuracy measurements to the variations, and adjust at least one of the optical metrology sub-system, a lithography tool, or the reference design based on the correlations.

Abstract: A metrology system may include a metrology tool to selectively perform metrology measurements in a static mode in which one or more metrology targets on a sample are stationary during a measurement or a scanning mode in which one or more metrology targets are in motion during a measurement, and a controller communicatively coupled to the translation stage and at least one of the one or more detectors. The controller may receive locations of metrology targets on the sample to be inspected, designate the metrology targets for inspection with the static mode or the scanning mode, direct the metrology tool to perform metrology measurements on the metrology targets in the static mode or the scanning mode based on the designation, and generate metrology data for the sample based on the metrology measurements on the metrology targets.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A multi-column metrology tool may include two or more measurement columns distributed along a column direction, where the two or more measurement columns simultaneously probe two or more measurement regions on a sample including metrology targets. A measurement column may include an illumination sub-system to direct illumination to the sample, a collection sub-system including a collection lens to collect measurement signals from the sample and direct it to one or more detectors, and a column-positioning sub-system to adjust a position of the collection lens. A measurement region of a measurement column may be defined by a field of view of the collection lens and a range of the positioning system in the lateral plane. The tool may further include a sample-positioning sub-system to scan the sample along a scan path different than the column direction to position metrology targets within the measurement regions of the measurement columns for measurements.

Abstract: A multi-column metrology tool may include two or more measurement columns distributed along a column direction, where the two or more measurement columns simultaneously probe two or more measurement regions on a sample including metrology targets. A measurement column may include an illumination sub-system to direct illumination to the sample, a collection sub-system including a collection lens to collect measurement signals from the sample and direct it to one or more detectors, and a column-positioning sub-system to adjust a position of the collection lens. A measurement region of a measurement column may be defined by a field of view of the collection lens and a range of the positioning system in the lateral plane. The tool may further include a sample-positioning sub-system to scan the sample along a scan path different than the column direction to position metrology targets within the measurement regions of the measurement columns for measurements.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller, where the controller includes one or more processors. The one or more processors may be configured receive a plurality of training images captured at one or more focal positions. The one or more processors may further generate a machine learning classifier based on the plurality of training images captured at one or more focal positions. The one or more processors may further receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features of a target specimen. The one or more processors may further determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier.

Abstract: A method for metrology includes directing at least one illumination beam to illuminate a semiconductor wafer on which at least first and second patterned layers have been deposited in succession, including a first target feature in the first patterned layer and a second target feature in the second patterned layer, overlaid on the first target feature. A sequence of images of the first and second target features is captured while varying one or more imaging parameters over the sequence. The images in the sequence are processed in order to identify respective centers of symmetry of the first and second target features in the images and measure variations in the centers of symmetry as a function of the varying image parameters. The measured variations are applied in measuring an overlay error between the first and second patterned layers.

Abstract: A multi-column metrology tool may include two or more measurement columns distributed along a column direction, where the two or more measurement columns simultaneously probe two or more measurement regions on a sample including metrology targets. A measurement column may include an illumination sub-system to direct illumination to the sample, a collection sub-system including a collection lens to collect measurement signals from the sample and direct it to one or more detectors, and a column-positioning sub-system to adjust a position of the collection lens. A measurement region of a measurement column may be defined by a field of view of the collection lens and a range of the positioning system in the lateral plane. The tool may further include a sample-positioning sub-system to scan the sample along a scan path different than the column direction to position metrology targets within the measurement regions of the measurement columns for measurements.

Abstract: A method for metrology includes directing at least one illumination beam to illuminate a semiconductor wafer on which at least first and second patterned layers have been deposited in succession, including a first target feature in the first patterned layer and a second target feature in the second patterned layer, overlaid on the first target feature. A sequence of images of the first and second target features is captured while varying one or more imaging parameters over the sequence. The images in the sequence are processed in order to identify respective centers of symmetry of the first and second target features in the images and measure variations in the centers of symmetry as a function of the varying image parameters. The measured variations are applied in measuring an overlay error between the first and second patterned layers.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured to receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A system and method for generating a quality parameter value of a semiconductor device wafer (SDW), during fabrication thereof, the method including designating a plurality of measurement site sets (MSSs) on the SDW, each of the MSSs including a first measurement-orientation site (FMS) and a second measurement-orientation site (SMS), the FMS and the SMS being different measurement sites on the SDW, generating a first measurement-orientation quality parameter dataset (FMQPD) by measuring features formed within each the FMS of at least one of the MSSs in a first measurement orientation, generating a second measurement-orientation quality parameter dataset (SMQPD) by measuring features formed within each the SMS of the at least one of the MSSs in a second measurement orientation and generating at least one tool-induced-shift (TIS)-ameliorated quality parameter value (TAQPV), at least partially based on the FMQPD and the SMQPD.

Abstract: A method for metrology includes directing at least one illumination beam to illuminate a semiconductor wafer on which at least first and second patterned layers have been deposited in succession, including a first target feature in the first patterned layer and a second target feature in the second patterned layer, overlaid on the first target feature. A sequence of images of the first and second target features is captured while varying one or more imaging parameters over the sequence. The images in the sequence are processed in order to identify respective centers of symmetry of the first and second target features in the images and measure variations in the centers of symmetry as a function of the varying image parameters. The measured variations are applied in measuring an overlay error between the first and second patterned layers.

Abstract: A system for use with a misregistration metrology tool (MMT), the system including a database including a plurality of process variation (PV) categories and a corresponding plurality of parameter sets and a process variation accommodation engine (PVAE) including a measurement site process variation category associator (MSPVCA) operative to associate a measurement site being measured by the MMT, at least partially based on an MMT output relating to the measurement site, with a measurement site process variation category (MSPVC), the MSPVC being one of the plurality of PV categories, a measurement site parameter set retriever (MSPSR) operative to retrieve a measurement site parameter set (MSPS) corresponding to the MSPVC and a measurement site parameter set communicator (MSPSC) operative to communicate the MSPS to the MMT.

Abstract: A metrology system includes one or more through-focus imaging metrology sub-systems communicatively coupled to a controller having one or more processors configured receive a plurality of training images captured at one or more focal positions. The one or more processors may generate a machine learning classifier based on the plurality of training images. The one or more processors may receive one or more target feature selections for one or more target overlay measurements corresponding to one or more target features. The one or more processors may determine one or more target focal positions based on the one or more target feature selections using the machine learning classifier. The one or more processors may receive one or more target images captured at the one or more target focal positions, the target images including the one or more target features of the target specimen, and determine overlay based thereon.

Abstract: A metrology system may include a metrology tool to selectively perform metrology measurements in a static mode in which one or more metrology targets on a sample are stationary during a measurement or a scanning mode in which one or more metrology targets are in motion during a measurement, and a controller communicatively coupled to the translation stage and at least one of the one or more detectors. The controller may receive locations of metrology targets on the sample to be inspected, designate the metrology targets for inspection with the static mode or the scanning mode, direct the metrology tool to perform metrology measurements on the metrology targets in the static mode or the scanning mode based on the designation, and generate metrology data for the sample based on the metrology measurements on the metrology targets.

Abstract: Metrology systems and methods are provided, which derive metrology target position on the wafer and possibly the target focus position during the movement of the wafer on the system's stage. The positioning data is derived before the target arrives its position (on-the-fly), sparing the time required in the prior art for the acquisition stage and increasing the throughput of the systems and methods. The collection channel may be split to provide for an additional moving-imaging channel comprising at least one TDI (time delay and integration) sensor with an associated analysis unit configured to derive wafer surface information, positioning and/or focusing information of the metrology targets with respect to the objective lens, during wafer positioning movements towards the metrology targets. Additional focusing-during-movement module and possibly feedbacking derived position and/or focus information to the stage may enhance the accuracy of the stopping of the stage.

Abstract: Metrology systems and methods are provided, which derive metrology target position on the wafer and possibly the target focus position during the movement of the wafer on the system's stage. The positioning data is derived before the target arrives its position (on-the-fly), sparing the time required in the prior art for the acquisition stage and increasing the throughput of the systems and methods. The collection channel may be split to provide for an additional moving-imaging channel comprising at least one TDI (time delay and integration) sensor with an associated analysis unit configured to derive wafer surface information, positioning and/or focusing information of the metrology targets with respect to the objective lens, during wafer positioning movements towards the metrology targets. Additional focusing-during-movement module and possibly feedbacking derived position and/or focus information to the stage may enhance the accuracy of the stopping of the stage.