Abstract: The present disclosure provides a packing method including following steps. Genetic algorithm is utilized to calculate multiple packing programs. Multiple candidate packing programs including all items are selected from the packing programs. Among each of the candidate packing programs, at least one of the items to be placed earlier is classified into a first subset, and at least another one of the items to be placed later is classified into a second subset. Among each of the candidate packing programs, a first packing for the first subset is maintained, and a second packing for the second subset is recalculated by using a greedy algorithm to generate an updated second packing.

Abstract: The present disclosure belongs to the technical field of biomedicine, and specifically relates to use of a malic enzyme 1 (ME1) inhibitor in preparation of a drug for preventing and treating pulmonary hypertension (PH) and as a marker for detecting and/or treating the PH. Since a protein level and/or an enzymatic activity are significantly increased in a lung tissue, the ME1 can be used as a marker for detecting or treating the PH. Meanwhile, the ME1 inhibitor can ameliorate pulmonary vascular resistance (PVR) and right ventricular involvement/right heart failure in PH patients by reducing a right ventricular systolic pressure (RVSP) and a right ventricular hypertrophy index (RVHI). Therefore, the ME1 inhibitor can be used as a therapeutic target for treating the PH, thereby increasing a medical utility of the ME1 and the ME1 inhibitor.

Abstract: This disclosure relates to systems and methods for the observation and/or measurement of clot formation and/or contraction. The system includes reaction chambers arranged with an image capturing device to allow for quantification of clot side-view cross-sectional area over time to determine the kinetics of clot formation and/or contraction. By varying the cellular materials and test agents included in the reaction chambers, it is possible to determine the effect of any particular component may yield on clot contraction. The system provides low-cost set-up, robust software, and multi-sample capacity, thus is a sensitive and flexible system to be used as a “workhorse” tool in a regular laboratory setting for better understanding the molecular features of how platelets mediate clot contraction, with the potential for clinical usages and/or medium throughput laboratory testing and drug screens.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: Systems and methods for predicting substrate geometry associated with a patterning process are described. Input information including geometry information and/or process information for a pattern is received and, using a machine learning prediction model, multi-dimensional output substrate geometry is predicted. The multi-dimensional output information may include pattern probability images. A stochastic edge placement error band and/or a stochastic failure rate may be predicted. The input information can include simulated aerial images, simulated resist images, target substrate dimensions, and/or data from a lithography apparatus associated with device manufacturing. Different aerial images may correspond to different heights in resist layers associated with the patterning process, for example.

Abstract: A method for generating modified contours and/or generating metrology gauges based on the modified contours. A method of generating metrology gauges for measuring a physical characteristic of a structure on a substrate includes obtaining (i) measured data associated with the physical characteristic of the structure printed on the substrate, and (ii) at least portion of a simulated contour of the structure, the at least a portion of the simulated contour being associated with the measured data; modifying, based on the measured data, the at least a portion of the simulated contour of the structure; and generating the metrology gauges on or adjacent to the modified at least a portion of the simulated contour, the metrology gauges being placed to measure the physical characteristic of the simulated contour of the structure.

Abstract: A method for training a machine learning model configured to predict values of a physical characteristic associated with a substrate and for use in adjusting a patterning process. The method involves obtaining a reference image; determining a first set of model parameter values of the machine learning model such that a first cost function is reduced from an initial value of the cost function obtained using an initial set of model parameter values, where the first cost function is a difference between the reference image and an image generated via the machine learning model; and training, using the first set of model parameter values, the machine learning model such that a combination of the first cost function and a second cost function is iteratively reduced, the second cost function representing a difference between measured values and predicted values.

Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

Abstract: Training methods and a mask correction method. One of the methods is for training a machine learning model configured to predict a post optical proximity correction (OPC) image for a mask. The method involves obtaining (i) a pre-OPC image associated with a design layout to be printed on a substrate, (ii) an image of one or more assist features for the mask associated with the design layout, and (iii) a reference post-OPC image of the design layout; and training the machine learning model using the pre-OPC image and the image of the one or more assist features as input such that a difference between the reference image and a predicted post-OPC image of the machine learning model is reduced.

Abstract: Methods of determining, and using, a process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: A method including: obtaining a characteristic of a portion of a design layout; determining a characteristic of M3D of a patterning device including or forming the portion; and training, by a computer, a neural network using training data including a sample whose feature vector includes the characteristic of the portion and whose supervisory signal includes the characteristic of the M3D. Also disclosed is a method including: obtaining a characteristic of a portion of a design layout; obtaining a characteristic of a lithographic process that uses a patterning device including or forming the portion; determining a characteristic of a result of the lithographic process; training, by a computer, a neural network using training data including a sample whose feature vector includes the characteristic of the portion and the characteristic of the lithographic process, and whose supervisory signal includes the characteristic of the result.

Abstract: Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: Methods of training machine learning models related to a patterning process, including a method for training a machine learning model configured to predict a mask pattern. The method including obtaining (i) a process model of a patterning process configured to predict a pattern on a substrate, wherein the process model comprises one or more trained machine learning models, and (ii) a target pattern, and training the machine learning model configured to predict a mask pattern based on the process model and a cost function that determines a difference between the predicted pattern and the target pattern.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining a portion of a design layout; determining characteristics of assist features based on the portion or characteristics of the portion; and training a machine learning model using training data including a sample whose feature vector includes the characteristics of the portion and whose label includes the characteristics of the assist features. The machine learning model may be used to determine characteristics of assist features of any portion of a design layout, even if that portion is not part of the training data.

Abstract: Methods of determining, and using, a patterning process model that is a machine learning model. The process model is trained partially based on simulation or based on a non-machine learning model. The training data may include inputs obtained from a design layout, patterning process measurements, and image measurements.

Abstract: A method including: obtaining a characteristic of a portion of a design layout; determining a characteristic of M3D of a patterning device including or forming the portion; and training, by a computer, a neural network using training data including a sample whose feature vector includes the characteristic of the portion and whose supervisory signal includes the characteristic of the M3D. Also disclosed is a method including: obtaining a characteristic of a portion of a design layout; obtaining a characteristic of a lithographic process that uses a patterning device including or forming the portion; determining a characteristic of a result of the lithographic process; training, by a computer, a neural network using training data including a sample whose feature vector includes the characteristic of the portion and the characteristic of the lithographic process, and whose supervisory signal includes the characteristic of the result.