Abstract: Methods and systems for generating a high resolution image for a specimen from a low resolution image of the specimen are provided. One system includes one or more computer subsystems configured for acquiring a low resolution image of a specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a deep convolutional neural network that includes one or more first layers configured for generating a representation of the low resolution image. The deep convolutional neural network also includes one or more second layers configured for generating a high resolution image of the specimen from the representation of the low resolution image. The second layer(s) include a final layer configured to output the high resolution image and configured as a sub-pixel convolutional layer.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: A multi-beam inspection system includes one or more particle beam sources to generate two or more particle beams, a set of particle control elements configured to independently direct the two or more particle beams to a sample, one or more detectors positioned to receive particles emanating from the sample in response to the two or more particle beams, and a controller communicatively coupled to the one or more detectors. The controller includes one or more processors to generate two or more inspection datasets associated with the particles received by the one or more detectors.

Abstract: Methods and systems for generating a high resolution image for a specimen from a low resolution image of the specimen are provided. One system includes one or more computer subsystems configured for acquiring a low resolution image of a specimen. The system also includes one or more components executed by the one or more computer subsystems. The one or more components include a deep convolutional neural network that includes one or more first layers configured for generating a representation of the low resolution image. The deep convolutional neural network also includes one or more second layers configured for generating a high resolution image of the specimen from the representation of the low resolution image. The second layer(s) include a final layer configured to output the high resolution image and configured as a sub-pixel convolutional layer.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: Methods and systems enabling ultra-high resolution topography measurements of patterned wafers are disclosed. Measurements obtained utilizing the ultra-high resolution metrology may be utilized to improve wafer metrology measurement accuracies. Additionally, measurements obtained utilizing the ultra-high resolution metrology may also be utilized to provide feedback and/or calibration control to improve fabrication and design of wafers.

Abstract: Methods and systems enabling ultra-high resolution topography measurements of patterned wafers are disclosed. Measurements obtained utilizing the ultra-high resolution metrology may be utilized to improve wafer metrology measurement accuracies. Additionally, measurements obtained utilizing the ultra-high resolution metrology may also be utilized to provide feedback and/or calibration control to improve fabrication and design of wafers.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: Systems and methods for providing improved scanner corrections are disclosed. Scanner corrections provided in accordance with the present disclosure may be referred to as wafer geometry aware scanner corrections. More specifically, wafer geometry and/or wafer shape signature information are utilized to improve scanner corrections. By removing the wafer geometry as one of the error sources that may affect the overlay accuracy, better scanner corrections can be obtained because one less contributing factor needs to be modeled.

Abstract: One embodiment relates to a method of automated microalignment using off-axis beam tilting. Image data is collected from a region of interest on a substrate at multiple beam tilts. Potential edges of a feature to be identified in the region are determined, and computational analysis of edge-related data is performed to positively identify the feature(s). Another embodiment relates to a method of automated detection of undercut on a feature using off-axis beam tilting. For each beam tilt, a determination is made of difference data between the edge measurement of one side and the edge measurement of the other side. An undercut on the feature is detected from the difference data. Other embodiments are also disclosed.

Abstract: One embodiment relates to an electron beam apparatus for automated imaging of a substrate surface. An electron source is configured to emit electrons, and a gun lens is configured to focus the electrons emitted by the electron source so as to form an electron beam. A condenser lens system is configured to receive the electron beam and to reduce its numerical aperture to an ultra-low numerical aperture. An objective lens is configured to focus the ultra-low numerical aperture beam onto the substrate surface. Other embodiments are also disclosed.

Abstract: The disclosure relates to a method and system of electron beam scanning for measurement, inspection or review. In accordance with one embodiment, the method includes a first scan on a region to collect first image data. The first image data is processed to determine information about a feature in the region. A scanning method is selected for imaging the feature. A second scan using the selected scanning method on the feature is then applied to collect second image data.

Abstract: One embodiment disclosed relates to an automated process for focusing a charged-particle beam in an apparatus onto an area of a substrate. A focusing parameter of the apparatus is set to a value, and intensity data is acquired from the area. The foregoing setting and acquiring steps are repeated for a range of values for the focusing parameter. A focusing sharpness measure is computed for each value of the focusing parameter based on noise in the acquired intensity data, and an in-focus value is determined for the focusing parameter based on the computed focusing sharpness measures. The focusing parameter of the apparatus may be, for example, an objective lens current, or a substrate bias voltage. The computation of the noise-based focusing sharpness measure may involve generating shifted or interleaved signals and calculating correlations between these signals. The focusing may be advantageously performed on an area lacking substantial edge information.

Abstract: A method of improving stability for CD-SEM measurements of photoresist, in particular 193 nm photoresist, and of reducing shrinkage of 193 nm photoresist during CD-SEM measurements. The photoresist is exposed to a dose of electrons or other stabilizing beam prior to or during CD measurement. One embodiment of the invention includes multiplexing of the SEM electron beam.

Abstract: One embodiment disclosed relates to a scanning electron beam apparatus. The apparatus includes an electron beam column, a scanning system, and a detection system. Circuitry in the apparatus is configured to store detected pixel data from each scan into one of the multiple frame buffers. A multi-frame data processor is configured to analyze the pixel data available in the multiple frame buffers. Another embodiment disclosed relates to a scanning electron beam apparatus having a data processor is configured to process the image data with a filter function having a filter strength, store results of the processing, and repeat the processing and the storing using various filter strengths. The results of the processing may comprise a critical dimension measurement at each filter strength.

Abstract: The disclosure relates to a method and system of electron beam scanning for measurement, inspection or review. In accordance with one embodiment, the method includes a first scan on a region to collect first image data. The first image data is processed to determine information about a feature in the region. A scanning method is selected for imaging the feature. A second scan using the selected scanning method on the feature is then applied to collect second image data.

Abstract: An apparatus for recording a series of images of a sample over a period of time while varying at least one image parameter. An electron microscope captures images of the sample and also varies the at least one image parameter. A controller triggers the electron microscope to sense multiple images of the sample and also controls the electron microscope to vary the at least one image parameter. An image recorder receives the sensed multiple images and also stores the sensed multiple images as the series of images. A display unit displays the series of images.