Samsung electronics co., ltd. (20240303824). CONTOUR PROBABILITY PREDICTION METHOD simplified abstract
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CONTOUR PROBABILITY PREDICTION METHOD
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CONTOUR PROBABILITY PREDICTION METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303824 titled 'CONTOUR PROBABILITY PREDICTION METHOD
The abstract describes a method for probabilistically predicting a contour on a wafer image based on a design image, involving acquiring contour images, calculating average and standard deviation, generating a probability distribution image, and training a prediction model.
- Acquiring multiple contour images for a wafer image processed according to a design image
- Calculating the average and standard deviation of the contours from the acquired images
- Generating a probability distribution image based on the calculated average and standard deviation
- Training a prediction model using deep learning by inputting the design image and probability distribution image
Potential Applications: - Quality control in semiconductor manufacturing - Defect detection in wafer processing - Automated inspection in industrial settings
Problems Solved: - Enhances accuracy in predicting contours on wafers - Improves efficiency in quality control processes - Enables automated analysis of wafer images
Benefits: - Increased precision in contour prediction - Time-saving in quality assurance procedures - Reduction in human error in inspection tasks
Commercial Applications: "Contour Probability Prediction Method for Wafer Image Analysis" can be utilized in semiconductor fabrication facilities for optimizing production processes, ensuring product quality, and reducing manufacturing costs.
Questions about Contour Probability Prediction Method for Wafer Image Analysis:
1. How does this method improve upon traditional contour prediction techniques? 2. What are the key advantages of using a probability distribution image in predicting contours on wafers?
Frequently Updated Research: Stay updated on advancements in deep learning algorithms for image analysis in semiconductor manufacturing to enhance the accuracy and efficiency of contour prediction methods.
Original Abstract Submitted
provided is a contour probability prediction method of probabilistically predicting a contour, the contour probability prediction method including acquiring a plurality of contour images for an image of a wafer on which a process has been performed according to a design image, calculating a contour average and a contour standard deviation from the plurality of contour images, generating a probability distribution image calculated with a predetermined probability distribution, on the basis of the contour average and the contour standard deviation, and deep-learning-training a probability prediction model by inputting the design image and the probability distribution image into the probability prediction model.
