Wafer Image Denoising and Contour Extraction for OPC Calibration
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Solution Overview
Problem
The challenge of accurately reproducing small and closely spaced circuit components in integrated circuits due to diffractive effects during photolithographic processes, leading to defects and flaws in manufacturing, is exacerbated by low signal-to-noise ratio SEM images, making optical proximity correction calibration difficult.
Innovation Solution
A computing system employing unsupervised deep learning algorithms for wafer image denoising and contour extraction, using techniques like unsupervised generative adversarial networks and CNN models, to remove noise and accurately extract physical structures without relying on layout or mask designs, thereby calibrating lithographic masks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing is used on SEM images, then the process is simple, but the signal-to-noise ratio is low and contours cannot be accurately extracted
Solution Approach 1:
The patent applies preliminary action by performing denoising and enhancement operations on the SEM images before contour extraction. The system pre-processes the images to improve signal-to-noise ratio and structural visibility, which enables accurate contour extraction without requiring complex post-processing steps. This preliminary preparation resolves the contradiction by simplifying the overall workflow while achieving high measurement precision.
Solution Approach 2:
The patent uses an intermediary approach by introducing a dedicated image processing module that acts as a mediator between the raw SEM images and the contour extraction algorithm. This intermediary layer enhances image quality through denoising and contrast adjustment, bridging the gap between low-quality input images and the requirements for precise contour detection, thereby resolving the accuracy-complexity contradiction.
2Manufacturing precision
If optical proximity correction is applied to improve pattern fidelity, then manufacturing precision improves, but the calibration process becomes more difficult due to noise in SEM images
Solution Approach 1:
The patent applies preliminary action by performing denoising and image enhancement before extracting contours for OPC calibration. This pre-processing step prepares the SEM images to provide clean, accurate contour data that reflects the actual manufactured patterns, thereby enabling reliable calibration of optical proximity correction parameters and resolving the contradiction between improving pattern fidelity and maintaining calibration difficulty.
Solution Approach 2:
The patent implements feedback by using the processed SEM images and extracted contours to calibrate and refine the optical proximity correction model. The system continuously iterates, using the calibration results to improve subsequent pattern predictions, thereby resolving the contradiction by making the calibration process more accurate and less difficult through feedback-driven optimization.
3Productivity
If feature size is reduced to increase circuit density, then more components fit in a given area, but diffractive effects increase and reproduction accuracy decreases
Solution Approach 1:
The patent uses an intermediary image processing system that mediates between the high-density circuit patterns and the measurement process. By introducing advanced denoising and enhancement algorithms as an intermediary layer, the system can accurately measure and extract contours from high-resolution SEM images of densely packed features, thereby enabling continued circuit density increases while maintaining manufacturing precision through improved measurement capabilities.
Data Source
AI summary
This application discloses a computing system to obtain a wafer image of an electronic device having physical structures manufactured using one or more lithographic masks associated with a layout design describing the electronic design. The computing system can implement an unsupervised deep learning algorithm to process the wafer image to remove at least some noise from the wafer image, which generates a denoised wafer image. The computing system can extract contours corresponding to the physical structures of the electronic device from the denoised wafer image of the electronic device without use of the layout design or a mask design. The computing system can calibrate the layout design or the mask design describing the one or more lithographic masks based, at least in part, on the contours extracted from the denoised wafer image.