GPU Contour Edge Detection for Faster Optical Lithography
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Solution Overview
Problem
Current contour edge determinations in optical lithography are resource-intensive and slow due to being performed by central processing units (CPUs).
Innovation Solution
Utilizing a graphics processing unit (GPU) to perform contour edge determination and stitching in parallel by creating image cells, comparing pixel values to thresholds, and using a marching squares approach to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If contour edge determination is performed by a central processing unit (CPU), then the method can be implemented with existing hardware, but the process is resource-intensive and slow
Solution Approach 1:
The patent replaces the CPU-based sequential processing mechanism with a GPU-based parallel processing mechanism. The GPU's architecture, designed for parallel computations, is substituted to handle the contour edge determination tasks, thereby increasing productivity while reducing resource consumption per processing unit.
Solution Approach 2:
The patent segments the image into multiple smaller regions and processes each region in parallel using multiple GPU processing units. This segmentation allows the workload to be distributed across many processing cores, significantly improving the overall speed of contour edge determination while reducing the resource burden on any single unit.
2Loss of time
If contour edge determination is performed sequentially by a CPU, then hardware complexity remains low, but processing time increases
Solution Approach 1:
The patent divides the image processing task into segments that can be processed in parallel. Each segment is handled by a separate processing unit, allowing simultaneous computation across multiple regions, thereby reducing total processing time while introducing manageable system complexity through parallel architecture.
Solution Approach 2:
The patent transitions from sequential processing (one-dimensional time progression) to parallel processing (multi-dimensional simultaneous computation). By utilizing the GPU's parallel architecture, the system processes multiple image regions at the same time, fundamentally changing the processing dimension from sequential to parallel, thus reducing time loss.
Data Source
AI summary
In order to determine contour edges within a provided image, a plurality of image cells (e.g., groupings of pixels) are created within the image. For each image cell, a numerical value for each of the pixels is compared to a predetermined threshold value to determine comparison values for each pixel. A total numerical value for each image cell may then be determined utilizing the comparison values and numerical values for each pixel within each image cell. An associated contour cell (indicating present contour edges) is then determined for each image cell by comparing the total numerical value for the image cell to a contour cell index. These operations may be performed in parallel by a graphics processing unit (GPU) for each image cell, which may improve a performance of contour edge determination for the image. The stitching of contour edges may also be performed using the GPU, which may provide additional performance improvements for image contour extraction.