Abstract: Scanner aberration impact modeling in a semiconductor manufacturing process, which may facilitate co-optimization of multiple scanners. Scanner aberration impact modeling may include executing a calibrated model and controlling a scanner based on output from the model. The model is configured to receive patterning system aberration data. The model is calibrated with patterning system aberration calibration data and corresponding patterning process impact calibration data. New patterning process impact data may be determined, based on the model, for the received patterning system aberration data. The model includes a hyperdimensional function configured to correlate the received patterning system aberration data with the new patterning process impact data.

Abstract: Scanner aberration impact modeling in a semiconductor manufacturing process, which may facilitate co-optimization of multiple scanners. Scanner aberration impact modeling may include executing a calibrated model and controlling a scanner based on output from the model. The model is configured to receive patterning system aberration data. The model is calibrated with patterning system aberration calibration data and corresponding patterning process impact calibration data. New pattering process impact data may be determined, based on the model, for the received patterning system aberration data. The model includes a hyperdimensional function configured to correlate the received patterning system aberration data with the new pattering process impact data.

Abstract: Methods for optimizing an aspect of a patterning process based on defects. For example, a method of source and mask optimization of a patterning process includes obtaining a location on a substrate having a threshold probability of having a defect; defining an defect ambit around the location to include a portion of a pattern on the substrate and one or more evaluation points associated with the portion of the pattern; determining a value of a first cost function based on a defect metric associated with the defect; determining a first guide function for the first cost function, wherein the first guide function is associated with a performance metric of the patterning process at the one or more evaluation locations within the defect ambit; and adjusting a source and/or a mask characteristic based on the value of the first cost function, and the first guide function.

Abstract: Methods, software, and systems are disclosed for determining mask patterns. The determination can include obtaining a mask pattern including sub-resolution assist features (SRAFs) each having constant widths. The widths are set as continuous variables and so can be optimized along with other variables during a mask optimization process of the mask pattern. Based on their population and/or statistics, the optimized continuous widths are then discretized to a limited number of global width levels. Further mask optimization be performed with the SRAFs having discretized optimized global width levels, where the width assigned to an individual SRAF may be adjusted to a different level of the global width levels.

Abstract: Methods for optimizing an aspect of a patterning process based on defects. For example, a method of source and mask optimization of a patterning process includes obtaining a location on a substrate having a threshold probability of having a defect; defining an defect ambit around the location to include a portion of a pattern on the substrate and one or more evaluation points associated with the portion of the pattern; determining a value of a first cost function based on a defect metric associated with the defect; determining a first guide function for the first cost function, wherein the first guide function is associated with a performance metric of the patterning process at the one or more evaluation locations within the defect ambit; and adjusting a source and/or a mask characteristic based on the value of the first cost function, and the first guide function.

Abstract: Methods and systems for determining a mask rule check violation (MRC) associated with a mask feature using a detector having geometric properties corresponding to the MRC. The detector (e.g., elliptical shaped) is configured to include a curved portion to detect a curvature violation, include an enclosed area (e.g., a fully enclosed area or a partially enclosed area having an opening), include a predefined orientation axis configured to guide relative positioning of the detector with a mask feature, and include a length to detect a critical dimension violation. The orientation axis of the detector is aligned with a normal axis at a location on the mask feature. Based on the orientation axis aligned with the normal axis of the mask feature, an MRC violation is determined corresponding to a region of the mask feature that intersects the enclosed area.

Abstract: A method for determining a wavefront parameter of a patterning process. The method includes obtaining a reference performance (e.g., a contour, EPE, CD) of a reference apparatus (e.g., a scanner), a lens model for a patterning apparatus configured to convert a wavefront parameter of a wavefront to actuator movement, and a lens fingerprint of a tuning apparatus (e.g., a to-be-matched scanner). Further, the method involves determining the wavefront parameter (e.g., a wavefront parameter such as tilt, offset, etc.) based on the lens fingerprint of the tuning apparatus, the lens model, and a cost function, wherein the cost function is a difference between the reference performance and a tuning apparatus performance.

Abstract: A method for determining a patterning device pattern. The method includes obtaining (i) an initial patterning device pattern having at least one feature, and (ii) a desired feature size of the at least one feature, obtaining, based on a patterning process model, the initial patterning device pattern and a target pattern for a substrate, a difference value between a predicted pattern of the substrate image by the initial patterning device and the target pattern for the substrate, determining a penalty value related the manufacturability of the at least one feature, wherein the penalty value varies as a function of the size of the at least one feature, and determining the patterning device pattern based on the initial patterning device pattern and the desired feature size such that a sum of the difference value and the penalty value is reduced.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: Scanner aberration impact modeling in a semiconductor manufacturing process, which may facilitate co-optimization of multiple scanners. Scanner aberration impact modeling may include executing a calibrated model and controlling a scanner based on output from the model. The model is configured to receive patterning system aberration data. The model is calibrated with patterning system aberration calibration data and corresponding patterning process impact calibration data. New patterning process impact data may be determined, based on the model, for the received patterning system aberration data. The model includes a hyperdimensional function configured to correlate the received patterning system aberration data with the new patterning process impact data.

Abstract: A method for determining a wavefront parameter of a patterning process. The method includes obtaining a reference performance (e.g., a contour, EPE, CD) of a reference apparatus (e.g., a scanner), a lens model for a patterning apparatus configured to convert a wavefront parameter of a wavefront to actuator movement, and a lens fingerprint of a tuning apparatus (e.g., a to-be-matched scanner). Further, the method involves determining the wavefront parameter (e.g., a wavefront parameter such as tilt, offset, etc.) based on the lens fingerprint of the tuning apparatus, the lens model, and a cost function, wherein the cost function is a difference between the reference performance and a tuning apparatus performance.

Abstract: A method for determining a patterning device pattern. The method includes obtaining (i) an initial patterning device pattern having at least one feature, and (ii) a desired feature size of the at least one feature, obtaining, based on a patterning process model, the initial patterning device pattern and a target pattern for a substrate, a difference value between a predicted pattern of the substrate image by the initial patterning device and the target pattern for the substrate, determining a penalty value related the manufacturability of the at least one feature, wherein the penalty value varies as a function of the size of the at least one feature, and determining the patterning device pattern based on the initial patterning device pattern and the desired feature size such that a sum of the difference value and the penalty value is reduced.

Abstract: Methods of constructing a process model for simulating a characteristic of a product of lithography from patterns produced under different processing conditions. The methods use a deviation between the variation of the simulated characteristic and the variation of the measured characteristic to adjust a parameter of the process model.

Abstract: A method for determining a wavefront parameter of a patterning process. The method includes obtaining a reference performance (e.g., a contour, EPE, CD) of a reference apparatus (e.g., a scanner), a lens model for a patterning apparatus configured to convert a wavefront parameter of a wavefront to actuator movement, and a lens fingerprint of a tuning apparatus (e.g., a to-be-matched scanner). Further, the method involves determining the wavefront parameter (e.g., a wavefront parameter such as tilt, offset, etc.) based on the lens fingerprint of the tuning apparatus, the lens model, and a cost function, wherein the cost function is a difference between the reference performance and a tuning apparatus performance.

Abstract: A method for determining a patterning device pattern. The method includes obtaining (i) an initial patterning device pattern having at least one feature, and (ii) a desired feature size of the at least one feature, obtaining, based on a patterning process model, the initial patterning device pattern and a target pattern for a substrate, a difference value between a predicted pattern of the substrate image by the initial patterning device and the target pattern for the substrate, determining a penalty value related the manufacturability of the at least one feature, wherein the penalty value varies as a function of the size of the at least one feature, and determining the patterning device pattern based on the initial patterning device pattern and the desired feature size such that a sum of the difference value and the penalty value is reduced.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: A method including: determining a first simulated partial image formed, by a lithographic projection apparatus, from a first radiation portion propagating along a first group of one or more directions; determining a second simulated partial image formed, by the lithographic projection apparatus, from a second radiation portion propagating along a second group of one or more directions; and determining an image by incoherently adding the first partial image and the second partial image, wherein the first group of one or more directions and the second group of one or more directions are different.

Abstract: A method of simulating a pattern to be imaged onto a substrate using a photolithography system, the method includes obtaining a pattern to be imaged onto the substrate, smoothing the pattern, and simulating an image of the smoothed pattern. The smoothing may include application of a graphical low pass filter and the simulating may include application of edge filters from an edge filter library.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.