18594453. CONTOUR PROBABILITY PREDICTION METHOD simplified abstract (Samsung Electronics Co., Ltd.)
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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 18594453 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 contour statistics, generating a probability distribution image, and training a prediction model.
- Acquiring multiple contour images for a wafer image post-process.
- Calculating contour average and standard deviation from the acquired images.
- Generating a probability distribution image based on the contour statistics.
- Training a prediction model using deep learning with the design image and probability distribution image inputs.
Potential Applications: This technology can be applied in semiconductor manufacturing for defect detection and quality control processes.
Problems Solved: This method addresses the challenge of accurately predicting contours on wafer images post-process.
Benefits: Enhances defect detection accuracy and improves quality control in semiconductor manufacturing processes.
Commercial Applications: "Contour Probability Prediction Method for Semiconductor Manufacturing Quality Control"
Frequently Updated Research: Researchers are continuously exploring new deep learning techniques to enhance the accuracy and efficiency of contour prediction methods in semiconductor manufacturing.
Questions about Contour Probability Prediction Method: 1. How does this method improve defect detection accuracy in semiconductor manufacturing? 2. What are the key advantages of using deep learning for contour prediction in this context?
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.
