Disclosed are methods of generating a proximity-corrected design layout for photoresist to be used in an etch operation. The methods may include identifying a feature in an initial design layout, and estimating one or more quantities characteristic of an in-feature plasma flux (IFPF) within the feature during the etch operation. The methods may further include estimating a quantity characteristic of an edge placement error (EPE) of the feature by comparing the one or more quantities characteristic of the IFPF to those in a look-up table (LUT, and/or through application of a multivariate model trained on the LUT, e.g., constructed through machine learning methods (MLM)) which associates values of the quantity characteristic of EPE with values of the one or more quantities characteristics of the IFPF. Thereafter, the initial design layout may be modified based on at the determined quantity characteristic of EPE.

Photoresist design layout pattern proximity correction through fast edge placement error prediction via a physics-based etch profile modeling framework

The invention relates to a convolutional neural network prediction method based on a rotation region. The method comprises steps that step 1, a convolutional neural network characteristic graph and an area of interests based on a rotary rectangular frame are generated according to input images; step 2, a rotary area-of-interest pooling layer is utilized to acquire a characteristic mapping graph according to the convolutional neural network characteristic graph and the area of interests; step 3, the characteristic mapping graph is mapped to be a one-dimensional characteristic vector; step 4, the one-dimensional characteristic vector is classified to acquire a classification result; step 5, rotary rectangular frame regression prediction of the one-dimensional characteristic vector is carried out to acquire a regression prediction result; and step 6, a final prediction result is outputted according to the classification result and the regression prediction result. The method is advantaged in that a problem of positioning and identification difficulties caused by target rotation and an aggregation phenomenon of images is solved, and precise positioning is realized.

Convolutional neural network prediction method based on rotation region

PROBLEM TO BE SOLVED: To provide a technique capable of appropriate prediction whether an abnormality occurs or not in a product produced by a production facility.SOLUTION: A prediction model creation device according to one aspect of the present invention comprises, a feature quantity acquisition unit which acquires values of a plurality of types of feature quantity calculated from operation state data indicating an operation state of the production facility, when a production facility which produces a product normally produces the product and when an abnormality occurs in the product to be produced respectively, a feature quantity selection unit which selects the feature quantity effective to predict the abnormality among the acquired plural feature quantity, on the basis of the predetermined algorithm for determining a degree of relevance between an abnormality occurring in the product produced and each kind of feature quantity from the value of each kind of feature quantity acquired at the normal time and at the time of the abnormality, and a prediction model construction unit to construct a prediction model for predicting occurrence of the abnormality by using the selected feature quantity.SELECTED DRAWING: Figure 5

Prediction model creation apparatus, production facility monitoring system, and production facility monitoring method

Computer-implemented methods of optimizing a process simulation model that predicts a result of a semiconductor device fabrication operation to process parameter values characterizing the semiconductor device fabrication operation are disclosed. The methods involve generating cost values using a computationally predicted result of the semiconductor device fabrication operation and a metrology result produced, at least in part, by performing the semiconductor device fabrication operation in a reaction chamber operating under a set of fixed process parameter values. The determination of the parameters of the process simulation model may employ pre-process profiles, via optimization of the resultant post-process profiles of the parameters against profile metrology results. Cost values for, e.g., optical scatterometry, scanning electron microscopy and transmission electron microscopy may be used to guide optimization.

Method of etch model calibration using optical scatterometry

The invention provides a conformal deposition film thickness prediction model establishment and application method. The method comprises steps that 1, the layout information data of three or more existing products is acquired; 2, actual film thickness values of the three or more existing products are acquired; 3, the selected layout information data of the three or more existing products is merged to generate the layout data of a whole photomask; 4, each geometric information influencing film thickness is calculated; 5, a first parameter, a second parameter, a third parameter, a fourth parameter and a fifth parameter of the model are calculated; 6, a prediction model is established; 7, a prediction value of film thickness of a new product is calculated by utilizing the prediction model; 8, a deposition film program type menu is established by utilizing the prediction value; and 9, model optimization is carried out.

Conformal deposition film thickness prediction model establishment and application method

A system and method for predicting a product characteristic are provided. The system includes a data acquisition module configured to acquire raw data associated with to-be predicted prediction information, a data conversion module configured to convert the raw data into computable normalized data, and a result prediction module configured to calculate a prediction result based on the normalized data and compare the prediction result with a predetermined standard value. The result prediction module includes a neural network prediction model configured to calculate the prediction result based on the normalized data. The prediction information may include reliability and/or yield to prevent major reliability or yield problems from occurring during manufacturing of semiconductor devices.

Predicting circuit reliability and yield using neural networks

The present invention provides a semiconductor device manufacture procedure prediction system and method, and relates to the technical field of semiconductors. The system comprises a data acquisition module, a data conversion module and a result prediction module with a neural network model. The method comprises the steps of collecting prediction-related original data of to-be-predicted information, converting the original data into computable standardized data, and calculating a to-be-predicted information prediction result by using a neural network prediction model. With adoption of the system and the method, the to-be-predicted information prediction result can be timely calculated by using the neural network model according to on-line data, thereby preventing major reliability and/or yield problems occurred in a semiconductor device manufacture procedure.

Semiconductor device manufacture procedure prediction system and method

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Predicting circuit reliability and yield using neural networks

Semiconductor device manufacture procedure prediction system and method