OPC Model Calibration Using SEM-GDS Alignment and Contour Verification
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Solution Overview
Problem
Existing semiconductor fabrication methods face challenges in achieving high accuracy and reliability due to optical proximity effects during photolithography, leading to distorted patterns and potential device malfunction.
Innovation Solution
A method involving the formation of an optical proximity correction (OPC) model using a scanning electron microscope (SEM) image and graphic data system (GDS) image alignment, followed by image filtering and contour verification with a genetic algorithm, to correct and verify the OPC model, ensuring precise pattern formation on the substrate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If optical proximity correction (OPC) is performed using conventional methods, then pattern formation is achieved, but pattern distortion occurs due to optical proximity effects
Solution Approach 1:
The patent applies preliminary action by performing optical proximity correction (OPC) on the layout design before photolithography fabrication. The OPC model predicts and compensates for optical proximity effects in advance, modifying the layout to pre-counteract expected distortions. This preliminary correction ensures that the final fabricated pattern matches the intended design, resolving the contradiction between achieving pattern formation and preventing pattern distortion.
2Measurement precision
If image filtering process is applied to SEM image, then noise is reduced, but processing time increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting image filtering parameters based on the specific characteristics of each SEM image and processing requirements. Rather than applying fixed or excessive filtering, the system optimizes filtering strength and type to achieve adequate noise reduction while minimizing processing time. This balanced approach maintains contour extraction accuracy without incurring excessive time losses.
3Measurement precision
If genetic algorithm is used for contour verification, then verification accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing the genetic algorithm's verification efforts on critical regions of the contour rather than uniformly processing the entire contour. The system identifies key areas where verification is most important and applies the computationally intensive genetic algorithm primarily to these regions. This localized approach maintains high verification accuracy while significantly reducing overall computational complexity.
4Measurement precision
If multiple images are averaged to form SEM image, then image quality is improved, but acquisition time increases
Solution Approach 1:
The patent applies partial action by acquiring and averaging only a sufficient number of SEM images needed to achieve the required image quality threshold, rather than indiscriminately averaging all possible images. The system monitors image quality during acquisition and stops once the desired quality level is reached, balancing image quality improvement with reasonable acquisition time.
Data Source
AI summary
Disclosed are a method of forming an optical proximity correction (OPC) model and/or a method of fabricating a semiconductor device using the same. The method of forming the OPC model may include obtaining a scanning electron microscope (SEM) image, which is an average image of a plurality of images taken using one or more scanning electron microscopes, and a graphic data system (GDS) image, which is obtained by imaging a designed layout, aligning the SEM image and the GDS image, performing an image filtering process on the SEM image, extracting a contour from the SEM image, and verifying the contour. The verifying of the contour may be performed using a genetic algorithm. Variables in the genetic algorithm may include first parameters related to the image alignment process, second parameters related to the image filtering process, and third parameters related to a critical dimension (CD) measurement process.


