Abstract: A lithographic process is performed on a set of semiconductor substrates consisting of a plurality of substrates, As part of the process, the set of substrates is partitioned into a number of subsets. The partitioning may be based on a set of characteristics associated with a first layer on the substrates. A fingerprint of a performance parameter is then determined for at least one substrate of the set of substrates. Under some circumstances, the fingerprint is determined for one substrate of each subset of substrates. The fingerprint is associated with at least the first layer. A correction for the performance parameter associated with an application of a subsequent layer is then derived, the derivation being based on the determined fingerprint and the partitioning of the set of substrates.

Abstract: A diagnostic system implements a network including two or more sub-domains. Each sub-domain has diagnostic information extracted by analysis of object data, the object data representing one or more parameters measured in relation to a set of product units that have been subjected nominally to the same industrial process as one another. The network further has at least one probabilistic connection from a first variable in a first diagnostic sub-domain to a second variable in a second diagnostic sub-domain. Part of the second diagnostic information is thereby influenced probabilistically by knowledge within the first diagnostic information. Diagnostic information may include, for example, a spatial fingerprint observed in the object data, or inferred. The network may include connections within sub-domains. The network may form a directed acyclic graph, and used for Bayesian inference operations.

Abstract: A method of determining a correction for a process parameter related to a lithographic process, wherein the lithographic process includes a plurality of runs during each one of which a pattern is applied to one or more substrates. The method of determining includes obtaining pre-exposure metrology data describing a property of a substrate; obtaining post-exposure metrology data comprising one or more measurements of the process parameter having been performed on one or more previously exposed substrates; assigning, based on the pre-exposure metrology data, a group membership status from one or more groups to the substrate; and determining the correction for the process parameter based on the group membership status and the post-exposure metrology data.

Abstract: A technique to generate predicted data for control or monitoring of a production process to improve a parameter of interest. Context data associated with operation of the production process is obtained. Metrology/testing is performed on the product of the production process, thereby obtaining performance data. A context-to-performance model is provided to generate predicted performance data based on labeling of the context data with performance data. This is an instance of semi-supervised learning. The context-to-performance model may include the learner that performs semi-supervised labeling. The context-to-performance model is modified using prediction information related to quality of the context data and/or performance data. Prediction information may include relevance information relating to relevance of the obtained context data and/or obtained performance data to the parameter of interest.

Abstract: A method of characterizing a deformation of a plurality of substrates is described. The method includes: measuring, for a plurality of n different alignment measurement parameters ? and for a plurality of substrates, a position of the alignment marks; determining a positional deviation as the difference between the n alignment mark position measurements and a nominal alignment mark position; grouping the positional deviations into data sets; determining an average data set; subtracting the average data set from the data sets to obtain a plurality of variable data sets; performing a blind source separation method on the variable data sets, thereby decomposing the variable data sets into a set of eigenwafers representing principal components of the variable data sets; and subdividing the set of eigenwafers into a set of mark deformation eigenwafers and a set of substrate deformation eigenwafers.

Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in the multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Abstract: A diagnostic system implements a network including two or more sub-domains. Each sub-domain has diagnostic information extracted by analysis of object data, the object data representing one or more parameters measured in relation to a set of product units that have been subjected nominally to the same industrial process as one another. The network further has at least one probabilistic connection from a first variable in a first diagnostic sub-domain to a second variable in a second diagnostic sub-domain. Part of the second diagnostic information is thereby influenced probabilistically by knowledge within the first diagnostic information. Diagnostic information may include, for example, a spatial fingerprint observed in the object data, or inferred. The network may include connections within sub-domains. The network may form a directed acyclic graph, and used for Bayesian inference operations.

Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Abstract: In a lithographic process in which a series of substrates are processed in different contexts, object data (such as performance data representing overlay measured on a set of substrates that have been processed previously) is received. Context data represents one or more parameters of the lithographic process that vary between substrates within the set. By principal component analysis or other statistical analysis of the performance data, the set of substrates are partitioned into two or more subsets. The first partitioning of the substrates and the context data are used to identify one or more relevant context parameters, being parameters of the lithographic process that are observed to correlate most strongly with the first partitioning. The lithographic apparatus is controlled for new substrates by reference to the identified relevant context parameters. Embodiments with feedback control and feedforward control are described.

Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Abstract: A process of calibrating parameters of a stack model used to simulate the performance of measurement structures in a patterning process, the process including: obtaining a stack model used in a simulation of performance of measurement structures; obtaining calibration data indicative of performance of the measurement structures; calibrating parameters of the model by, until a termination condition occurs, repeatedly: simulating performance of the measurement structures with the simulation using a candidate model; approximating the simulation, based on a result of the simulation, with a surrogate function; and selecting a new candidate model based on the approximation.

Abstract: A lithography system configured to apply a pattern to a substrate, the system including a lithography apparatus configured to expose a layer of the substrate according to the pattern, and a machine learning controller configured to control the lithography system to optimize a property of the pattern, the machine learning controller configured to be trained on the basis of a property measured by a metrology unit configured to measure the property of the exposed pattern in the layer and/or a property associated with exposing the pattern onto the substrate, and to correct lithography system drift by adjusting one or more selected from: the lithography apparatus, a track unit configured to apply the layer on the substrate for lithographic exposure, and/or a control unit configured to control an automatic substrate flow among the track unit, the lithography apparatus, and the metrology unit.

Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Abstract: A system for establishing a hearing ability model of a hearing ability of a person, includes a data storage configured to store a representation of a distribution of a hearing ability of a population of individuals, and a processor configured to establish a hearing ability model representing a hearing ability of the person based at least in part on (i) information regarding a person's response to a stimulus of a hearing evaluation event, and (ii) the representation of the distribution of the hearing ability of the population.

Abstract: A reticle is loaded into a lithographic apparatus. The apparatus performs measurements on the reticle, so as to calculate alignment parameters for transferring the pattern accurately to substrates. Tests are performed to detect possible contamination of the reticle or its support. Either operation proceeds with a warning, or the patterning of substrates is stopped. The test uses may use parameters of the alignment model itself, or different parameters. The integrity parameters may be compared against reference values reflecting historic measurements, so that sudden changes in a parameter are indicative of contamination. Integrity parameters may be calculated from residuals of the alignment model. In an example, height residuals are used to calculate parameters of residual wedge (Rx?) and residual roll (Ryy?). From these, integrity parameters expressed as height deviations are calculated and compared against thresholds.

Abstract: In a lithographic process, product units such as semiconductor wafers are subjected to lithographic patterning operations and chemical and physical processing operations. Alignment data or other measurements are made at stages during the performance of the process to obtain object data representing positional deviation or other parameters measured at points spatially distributed across each unit. This object data is used to obtain diagnostic information by performing a multivariate analysis to decompose a set of vectors representing the units in said multidimensional space into one or more component vectors. Diagnostic information about the industrial process is extracted using the component vectors. The performance of the industrial process for subsequent product units can be controlled based on the extracted diagnostic information.

Abstract: In a hearing aid with a signal processor for signal processing in accordance with selected values of a set of parameters ?, a method of automatic adjustment of a set z of the signal processing parameters ?, using a set of learning parameters ? of the signal processing parameters ? is provided, wherein the method includes extracting signal features u of a signal in the hearing aid, recording a measure r of an adjustment e made by the user of the hearing aid, modifying z by the equation z=u?+r, and absorbing the user adjustment e in ? by the equation ?N=?(u,r)+?P, wherein ?N is the new values of the learning parameter set ?, ?P is the previous values of the learning parameter set ?, and ? is a function of the signal features u and the recorded adjustment measure r.

Abstract: A lithography system configured to apply a pattern to a substrate, the system including a lithography apparatus configured to expose a layer of the substrate according to the pattern, and a machine learning controller configured to control the lithography system to optimize a property of the pattern, the machine learning controller configured to be trained on the basis of a property measured by a metrology unit configured to measure the property of the exposed pattern in the layer and/or a property associated with exposing the pattern onto the substrate, and to correct lithography system drift by adjusting one or more selected from: the lithography apparatus, a track unit configured to apply the layer on the substrate for lithographic exposure, and/or a control unit configured to control an automatic substrate flow among the track unit, the lithography apparatus, and the metrology unit.

Abstract: The present invention relates to a method for automatic adjustment of signal processing parameters in a hearing aid. It is based on an interactive estimation process that incorporates user feedback. The method is capable of incorporating user perception of sound reproduction, such as sound quality over time. The user may fine-tune the hearing aid using a volume-control wheel or a push-button on the hearing aid housing, which is linked to an adaptive parameter that is a projection of a relevant parameter space. For example, this new parameter could control simple volume, the number of active microphones, or a complex trade-off between noise reduction and signal distortion. By turning the “personalization wheel” in accordance with user preferences and absorbing these preferences in the model resident in the hearing aid, it is possible to absorb user preferences while the user wears the hearing aid device in the field.

Abstract: The present invention relates to a new method for effective estimation of signal processing parameters in a hearing aid. It is based on an interactive estimation process that incorporates—possibly inconsistent—user feedback. In particular, the present invention relates to optimization of hearing aid signal processing parameters based on Bayesian incremental preference elicitation.