Optimization-Based Image Processing for Semiconductor Metrology
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Solution Overview
Problem
Current image processing techniques for measuring fine features in semiconductor wafers are vulnerable to noise and artifacts, limiting the accuracy of metrology data and requiring destructive imaging methods.
Innovation Solution
The use of numerical optimization to define a cost function for geometrical shapes in images, such as ellipses or elliptical rings, to robustly extract metrology data from images acquired using various imaging tools, including electron beam and optical imaging, while incorporating prior knowledge to compensate for obscured features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If direct measurement from raw images is used, then measurement process is simple, but measurement precision deteriorates due to noise and artifacts
Solution Approach 1:
The patent applies preliminary image processing operations (denoising, edge detection, feature enhancement) before the actual measurement step. By preprocessing the raw images to reduce noise and enhance features, the measurement can be performed on cleaned images, thus maintaining measurement simplicity while improving accuracy.
Solution Approach 2:
The patent introduces intermediate processing steps (image filtering, edge detection algorithms, feature extraction) as mediators between the raw image and the final measurement. These intermediary operations transform the noisy raw image into a processed image with enhanced features, enabling accurate measurement without directly measuring the noisy original.
2Measurement precision
If edge detection technique is used, then image processing is applied to improve accuracy, but reliability deteriorates due to vulnerability to noise and artifacts
Solution Approach 1:
The patent segments the image processing into multiple independent stages: denoising, edge detection, feature enhancement, and measurement. By dividing the process into separate modules, each stage can be optimized independently and fail-safes can be implemented, improving overall reliability against noise and artifacts.
Solution Approach 2:
The patent dynamically adjusts processing parameters (filtering strength, edge detection thresholds, enhancement coefficients) based on the characteristics of the input image. By adapting parameters to the specific image conditions, the system maintains high accuracy while being robust to varying noise levels and artifact types.
Data Source
AI summary
One or more images of a device feature are acquired using an imaging tool. A geometrical shape is defined encompassing the relevant pixels of each image, where the geometrical shape is represented in terms of one or more parameters. A cost function is defined whose variables comprise the one or more parameters of the geometrical shape. For each image, numerical optimization is applied to obtain optimal values of the one or more parameters for which the cost function is minimized. The optimal values of the one or more parameters are reported as metrology data pertaining to the device feature.