Abstract:
A method to improve a lithographic process for imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method including: computing a multi-variable cost function of a plurality of design variables that are characteristics of the lithographic process, and reconfiguring the characteristics of the lithographic process by adjusting the design variables until a predefined termination condition is satisfied. The multi-variable cost function may be a function of one or more pattern shift errors. Reconfiguration of the characteristics may be under one or more constraints on the one or more pattern shift errors.
Abstract:
A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.
Abstract:
A method of measuring a parameter of a patterning process, the method including obtaining a measurement of a substrate processed by a patterning process, with a first metrology target measurement recipe; obtaining a measurement of the substrate with a second, different metrology target measurement recipe, wherein measurements using the first and second metrology target measurement recipes have their own distinct sensitivity to a metrology target structural asymmetry of the patterning process; and determining a value of the parameter by a weighted combination of the measurements of the substrate using the first and second metrology target measurement recipes, wherein the weighting reduces or eliminates the effect of the metrology target structural geometric asymmetry on the parameter of the patterning process determined from the measurements using the first and second metrology target measurement recipes.
Abstract:
A method for analyzing a process, the method including obtaining a multi-dimensional probability density function representing an expected distribution of values for a plurality of process parameters; obtaining a performance function relating values of the process parameters to a performance metric of the process; and using the performance function to map the probability density function to a performance probability function having the process parameters as arguments.
Abstract:
A measurement apparatus and method for determining a substrate grid describing a deformation of a substrate prior to exposure of the substrate in a lithographic apparatus configured to fabricate one or more features on the substrate. Position data for a plurality of first features and/or a plurality of second features on the substrate is obtained. Asymmetry data for at least a feature of the plurality of first features and/or the plurality of second features is obtained. The substrate grid based on the position data and the asymmetry data is determined. The substrate grid and asymmetry data are passed to the lithographic apparatus for controlling at least part of an exposure process to fabricate one or more features on the substrate.
Abstract:
The present invention relates to methods and systems for designing gauge patterns that are extremely sensitive to parameter variation, and thus robust against random and repetitive measurement errors in calibration of a lithographic process utilized to image a target design having a plurality of features. The method may include identifying most sensitive line width/pitch combination with optimal assist feature placement which leads to most sensitive CD (or other lithography response parameter) changes against lithography process parameter variations, such as wavefront aberration parameter variation. The method may also include designing gauges which have more than one test patterns, such that a combined response of the gauge can be tailored to generate a certain response to wavefront-related or other lithographic process parameters. The sensitivity against parameter variation leads to robust performance against random measurement error and/or any other measurement error.
Abstract:
Grouping image patterns to determine wafer behavior in a patterning process with a trained machine learning model is described. The described operations include converting, based on the trained machine learning model, one or more patterning process images including the image patterns into feature vectors. The feature vectors correspond to the image patterns. The described operations include grouping, based on the trained machine learning model, feature vectors with features indicative of image patterns that cause matching wafer and/or wafer defect behavior in the patterning process. The one or more patterning process images include aerial images, resist images, and/or other images. The grouped feature vectors may be used to: detect potential patterning defects on a wafer during a lithography manufacturability check as part of optical proximity correction, adjust a mask layout design, and/or generate a gauge line/defect candidate list, among other uses.
Abstract:
A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.
Abstract:
A method for determining a root cause affecting yield in a process for manufacturing devices on a substrate, the method including: obtaining yield distribution data including a distribution of a yield parameter across the substrate or part thereof; obtaining sets of metrology data, each set including a spatial variation of a process parameter over the substrate or part thereof corresponding to a different layer of the substrate; comparing the yield distribution data and metrology data based on a similarity metric describing a spatial similarity between the yield distribution data and an individual set out of the sets of the metrology data; and determining a first similar set of metrology data out of the sets of metrology data, being the first set of metrology data in terms of processing order for the corresponding layers, which is determined to be similar to the yield distribution data.
Abstract:
A substrate has first and second target structures formed thereon by a lithographic process. Each target structure has two-dimensional periodic structure formed in a single material layer on a substrate using first and second lithographic steps, wherein, in the first target structure, features defined in the second lithographic step are displaced relative to features defined in the first lithographic step by a first bias amount that is close to one half of a spatial period of the features formed in the first lithographic step, and, in the second target structure, features defined in the second lithographic step are displaced relative to features defined in the first lithographic step by a second bias amount close to one half of said spatial period and different to the first bias amount. An angle-resolved scatter spectrum of the first target structure and an angle-resolved scatter spectrum of the second target structure is obtained, and a measurement of a parameter of a lithographic process is derived from the measurements using asymmetry found in the scatter spectra of the first and second target structures.