A mask data generation method including: calculating first evaluation value of projection image based on first mask data in which first value or second value different from first value is set for each of a plurality of unit elements that constitute 2-dimensional grid; generating second mask data by changing value of first unit element to which first value is set to second value and by changing value of second unit element which is disposed close to first unit element on 2-dimensional grid and to which second value is set to first value, among the plurality of unit elements included in the first mask data; calculating second evaluation value of projection image based on the second mask data; and comparing the first evaluation value and the second evaluation value and selecting either the first mask data or the second mask data as output mask data based on the comparison result.

In embodiments of a digital lithography system, physical design data prepared at a data prep server in a hierarchical data structure. A leaf node comprises a repeater nod, comprising a bitmap image and a plurality of locations at which the bitmap appears in a physical design. At an EYE server, a repeater node bitmap is adjusted based upon, for example, spatial light modulator rotational adjustment and substrate distortion. The adjusted repeater node and the plurality of locations in which the adjusted repeater appears is compared to the repeater of the data prep server and its plurality of locations. In further embodiments, a rasterizer generates a checksum of bitmap to be printed to a substrate, from the EYE server bitmap. The checksum is compared to a checksum of the EYE server bitmap.

Embodiments disclosed herein relate to an exposure pattern alteration software application which manipulates exposure polygons having lines with angles substantially close to angles of symmetry of a hex close pack arrangement, which suffer from long jogs. Long jogs present themselves as high edge placement error regions. As such, the exposure pattern alteration software application provides for line wave reduction by serrating polygon edges at affected angles to reduce edge placement errors during maskless lithography patterning in a manufacturing process.

A method to improve a lithographic process of imaging a portion of a design layout onto a substrate using a lithographic projection apparatus, the method including: computing a multi-variable cost function, the multi-variable cost function being a function of a stochastic variation of a characteristic of an aerial image or a resist image, or a function of a variable that is a function of the stochastic variation or that affects the stochastic variation, the stochastic variation being a function of a plurality of design variables that represent characteristics of the lithographic process; and reconfiguring one or more of the characteristics of the lithographic process by adjusting one or more of the design variables until a certain termination condition is satisfied.

A method for characterizing post-processing data in terms of individual contributions from processing stations, the post-processing data relating to a manufacturing process for manufacturing integrated circuits on a plurality of substrates using a corresponding processing apparatus for each of a plurality of process steps, at least some of the processing apparatuses each including a plurality of the processing stations, and wherein the combination of processing stations used to process each substrate defines a process thread for the substrate; the method including: obtaining post-processing data associated with processing of the plurality of substrates in a cyclic sequence of processing threads; and determining an individual contribution of a particular processing station by comparing a subset of the post-processing data corresponding to substrates having shared process sub-threads, wherein a process sub-thread describes the process steps of each process thread other than the process step to which the particular processing station corresponds.

A system and methods for OCD metrology are provided including receiving reference parameters, receiving multiple sets of measured scatterometric data, and receiving an optical model designed to generate one or more sets of model scatterometric data according to a set of pattern parameters, and training a machine learning model by applying, during the training, target features including the reference parameters, and by applying input features including the sets of measured scatterometric data and the sets of model scatterometric data, such that the trained machine learning model estimates new wafer pattern parameters from subsequently sets of measured scatterometric data.

A computer-implemented defect prediction method for a device manufacturing process involving processing a pattern onto a substrate. Non-correctable error is used to help predict locations where defects are likely to be present, allowing improvements in metrology throughput. In an embodiment, non-correctable error information relates to imaging error due to limitations on, for example, the lens hardware, imaging slit size, and/or other physical characteristics of the lithography system. In an embodiment, non-correctable error information relates to imaging error induced by lens heating effects.

A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.

Disclosed is a method for modeling measurement data over a substrate area and associated apparatus. The method comprises obtaining measurement data relating to a first layout; modeling a second model based on said first layout; evaluating the second model on a second layout, the second layout being more dense than said first layout; and fitting a first model to this second model according to the second layout.

The present disclosure relates to apparatus and methods for performing maskless lithography processes. A substrate rocessing apparatus includes a slab with a plurality of guiderails coupled to and extending along the slab. A first shuttle is disposed on the plurality of guiderails, a second shuttle is disposed on the first shuttle, and a metrology bar is coupled to the second shuttle. The etrology bar includes a first plurality of sensors coupled to the metrology bar. A second plurality of sensors coupled to the metrology bar are disposed laterally inward of the first plurality of sensors.