In a method of manufacturing a photo mask for lithography, circuit pattern data are acquired. A pattern density, which is a total pattern area per predetermined area, is calculated from the circuit pattern data. Dummy pattern data for areas having pattern density less than a threshold density are generated. Mask drawing data is generated from the circuit pattern data and the dummy pattern data. By using an electron beam from an electron beam lithography apparatus, patterns are drawn according to the mask drawing data on a resist layer formed on a mask blank substrate. The drawn resist layer is developed using a developing solution. Dummy patterns included in the dummy pattern data are not printed as a photo mask pattern when the resist layer is exposed with the electron beam and is developed.

A diagnostic apparatus monitors a lithographic manufacturing system. First measurement data representing local deviations of some characteristic across a substrate is obtained using sensors within a lithographic apparatus, and/or a separate metrology tool. Other inspection tools perform substrate backside inspection to produce second measurement data. A high-resolution backside defect image is processed into a form in which it can be compared with lower resolution information from the first measurement data. Cross-correlation is performed to identify which of the observed defects are correlated spatially with the deviations represented in the first measurement data. A correlation map is used to identify potentially relevant clusters of defects in the more detailed original defect map. The responsible apparatus can be identified by pattern recognition as part of an automated root cause analysis. Alternatively, reticle inspection data may be used as second measurement data.

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 mode control system and method for controlling an output mode of a broadband radiation source including a photonic crystal fiber (PCF). The mode control system includes at least one detection unit configured to measure one or more parameters of radiation emitted from the broadband radiation source to generate measurement data, and a processing unit configured to evaluate mode purity of the radiation emitted from the broadband radiation source, from the measurement data. Based on the evaluation, the mode control system is configured to generate a control signal for optimization of one or more pump coupling conditions of the broadband radiation source. The one or more pump coupling conditions relate to the coupling of a pump laser beam with respect to a fiber core of the photonic crystal fiber.

A computing system may include a virtual cross metrology engine configured to construct a given virtual metrology model. The given virtual metrology model may take, as inputs, process parameters applied for the given step of a semiconductor fabrication process. The virtual cross metrology engine may also be configured to construct a subsequent virtual metrology model, and the subsequent step is performed after the given step in the semiconductor fabrication process. Doing so may include determining inputs for the subsequent virtual metrology model from a combination of the process parameters applied for the given step of the semiconductor fabrication process, process parameters applied for the subsequent step of the semiconductor fabrication process, and a wafer value for the given step of the semiconductor fabrication process that the given virtual metrology model is configured to predict.

A method and a system for preparing a substrate with three dimensional features for immersion based inspection. The method may include (a) receiving, by a secondary chamber, an article that includes the substrate, a housing, and a transparent element; wherein the transparent element is sealingly coupled to the housing to provide a sealed inner space; wherein the sealed inner space may include a gap between a first surface of the substrate to a second surface of the transparent element; wherein the gap is filled with gas during the receiving of the article; (b) evacuating the gas from the gap while reducing a pressure within the secondary chamber and maintaining an integrity of the transparent element; (c) filling the gap with fluid while increasing the pressure within a secondary chamber inner space and maintaining an integrity of the transparent element; and (d) outputting the article from the secondary chamber.

Apparatus and methods are described for the automated transfer and storage of transmission electron microscope (TEM) and scanning/transmission electron microscope (STEM) lamella samples throughout a semiconductor manufacturing facility using existing automation infrastructure such as a Front Opening Unified Pod (FOUP). Also provided are wafer facsimiles corresponding to outer dimensions of semiconductor, data storage or solar cell wafers, wherein the facsimiles adapted to store, carry and/or provide a testing platform for testing of samples taken from semiconductor, data storage or solar cell wafers.

A system and a method for measuring of parameter values of semiconductor objects within wafers with increased throughput include using a modified machine learning algorithm to extract measurement results from instances of semiconductor objects. A training method for training the modified machine learning algorithm includes reducing a user interaction. The method can be more flexible and robust and can involve less user interaction than conventional methods. The system and method can be used for quantitative metrology of integrated circuits within semiconductor wafers.

A semiconductor device patterning method includes generating an input image by imaging information about a pattern of a sample, acquiring an output image of the pattern of the sample after a preset semiconductor process with respect to the sample, generating a predictive model through learning using a Deep Neural Network (DNN) with the input image and the output image, and predicting a pattern image after the semiconductor process for a pattern of a semiconductor device by using the predictive model.