Samsung electronics co., ltd. (20240193415). METHOD AND APPARATUS WITH SEMICONDUCTOR PATTERN CORRECTION simplified abstract
Contents
METHOD AND APPARATUS WITH SEMICONDUCTOR PATTERN CORRECTION
Organization Name
Inventor(s)
Seong-Jin Park of Suwon-si (KR)
Seon Min Rhee of Suwon-si (KR)
METHOD AND APPARATUS WITH SEMICONDUCTOR PATTERN CORRECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240193415 titled 'METHOD AND APPARATUS WITH SEMICONDUCTOR PATTERN CORRECTION
The abstract describes a processor-implemented method that involves generating a corrected result image of a desired pattern image using neural networks, updating the corrected result image to reduce errors between the desired pattern image and a simulated result image.
- The method utilizes a backward correction neural network for correcting the result image based on the desired pattern image.
- A forward simulation neural network is used to generate a simulated result image based on the corrected result image.
- The corrected result image is continuously updated to minimize errors between the desired pattern image and the simulated result image.
Potential Applications: - Image processing and correction in various industries such as manufacturing, healthcare, and entertainment. - Quality control in production processes where accurate pattern replication is crucial.
Problems Solved: - Addressing errors and discrepancies between desired pattern images and simulated results. - Improving the accuracy and efficiency of image processing and correction tasks.
Benefits: - Enhanced image quality and accuracy in pattern replication. - Streamlined production processes with reduced errors and improved quality control.
Commercial Applications: Title: "Advanced Image Correction Technology for Enhanced Quality Control" This technology can be applied in industries such as manufacturing, healthcare, and entertainment for precise image processing and quality control, leading to improved product quality and efficiency in production processes.
Prior Art: Prior research in the field of neural networks and image processing may provide insights into similar methods and technologies used for image correction and simulation tasks.
Frequently Updated Research: Stay updated on advancements in neural network technology, image processing algorithms, and quality control methods to enhance the performance and efficiency of the described method.
Questions about the technology: 1. How does the backward correction neural network improve the accuracy of the corrected result image? 2. What are the potential challenges in implementing this method in real-world applications?
Original Abstract Submitted
a processor-implemented method including generating a first corrected result image of a first desired pattern image using a backward correction neural network provided an input based on the first desired pattern image, the backward correction neural network performing a backward correction of a first process, generating a first simulated result image using a forward simulation neural network based on the first corrected result image, the forward simulation neural network performing a forward simulation of a performance of the first process, and updating the first corrected result image so that an error between the first desired pattern image and the first simulated result image is reduced.
