Described herein is a method for training a machine learning model to determine a source of error contribution to multiple features of a pattern printed on a substrate. The method includes obtaining training data having multiple datasets, wherein each dataset has error contribution values representative of an error contribution from one of multiple sources to the features, and wherein each dataset is associated with an actual classification that identifies a source of the error contribution of the corresponding dataset; and training, based on the training data, a machine learning model to predict a classification of a reference dataset of the datasets such that a cost function that determines a difference between the predicted classification and the actual classification of the reference dataset is reduced.

An EUV stocker and an EUV pod device is disclosed. The EUV stocker includes an AI driven dynamic control circuitry, an AI controlled safety interlock, and an independent air return control device. The EUV stocker includes a Mass Flow Control (MFC) that operates in conjunction with one or more valves. The EUV stocker further includes a hydrocarbon detecting assembly, oxygen detecting assembly, pressure detecting assembly, and temperature detecting assembly and more to maintain the required condition within the EUV stocker. The EUV stocker also includes automated transportation devices such as AMHS, OHT, MR, AGV, RGV, or the like to provide a safe EUV mask storage environment for operators.

A lithography system is provided capable of deterring contaminants, such as tin debris from entering into the scanner. The lithography system in accordance with various embodiments of the present disclosure includes a processor, an extreme ultraviolet light source, a scanner, and a hollow connection member. The light source includes a droplet generator for generating a droplet, a collector for reflecting extreme ultraviolet light into an intermediate focus point, and a light generator for generating pre-pulse light and main pulse light. The droplet generates the extreme ultraviolet light in response to the droplet being illuminated with the pre-pulse light and the main pulse light. The scanner includes a wafer stage. The hollow connection member includes an inlet that is in fluid communication with an exhaust pump. The hollow connection member provides a hollow space in which the intermediate focus point is disposed. The hollow connection member is disposed between the extreme ultraviolet light source and the scanner.

A method for calibrating a process model of a patterning process. The method includes identifying a portion of the substrate that has values within a tolerance band of one or more parameters (e.g., CD, EPE, etc.) of the patterning process, obtaining, via a metrology tool, metrology data corresponding to the portion of the substrate, processing the metrology data, and calibrating a process model based on the processed metrology data.

A lithography system is provided capable of deterring contaminants, such as tin debris from entering into the scanner. The lithography system in accordance with various embodiments of the present disclosure includes a processor, an extreme ultraviolet light source, a scanner, and a hollow connection member. The light source includes a droplet generator for generating a droplet, a collector for reflecting extreme ultraviolet light into an intermediate focus point, and a light generator for generating pre-pulse light and main pulse light. The droplet generates the extreme ultraviolet light in response to the droplet being illuminated with the pre-pulse light and the main pulse light. The scanner includes a wafer stage. The hollow connection member includes an inlet that is in fluid communication with an exhaust pump. The hollow connection member provides a hollow space in which the intermediate focus point is disposed. The hollow connection member is disposed between the extreme ultraviolet light source and the scanner.

Disclosed herein are systems, methods, and software managing the deployment of development environments for an organization. In one example, a computing system may identify a request for a development environment. In response to the request, the computing system may select one or more images for the development environment from a plurality of images based on an identifier associated with the request and initiate one or more virtual nodes from the one or more images based on a configuration associated with the identifier.

A method including: determining a sequence of states of an object, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object; determining, based on at least one of the states within the sequence of states and the one or more future states, a process metric associated with the object, the process metric including an indication of whether processing requirements for the object are satisfied for individual states in the sequence of states; and initiating an adjustment to processing based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied.

A method for categorizing a substrate subject to a semiconductor manufacturing process including multiple operations, the method including: obtaining values of functional indicators derived from data generated during one or more of the multiple operations on the substrate, the functional indicators characterizing at least one operation; applying a decision model including one or more threshold values to the values of the functional indicators to obtain one or more categorical indicators; and assigning a category to the substrate based on the one or more categorical indicators.

A simulation apparatus has: a first processing part configured to obtain a value of a parameter in a first set relating to the forming of the pattern; a second processing part configured to obtain a value of a parameter in a second set that is at least partially same as the parameter in the first set and relating to the forming of the pattern; and an integration processing part configured to evaluate, based on the value of the parameter in the first set and the value of the parameter in the second set, a state of the pattern formed on the substrate and a forming condition when the pattern is formed, and to determine based on the result of the evaluation whether or not to make at least one of the first processing part and the second processing part recalculate the value of the parameter in the corresponding set.

A positioning method for particles on a reticle includes: data of positions passed by a target reticle within a preset period of time is determined according to path data of the target reticle that includes particle information of the target reticle at each scan moment; position information of the target reticle when particles are present on a surface of the target reticle is determined according to the data of positions, to obtain target position data of the target reticle; reticle position data of the target reticle within adjacent scan moments is determined according to the target position data, and a particle source position of the particles on the surface of the target reticle is determined from the reticle position data according to position priorities; and a particle position analysis report of the target reticle within the preset period of time is generated according to the particle source position.