Reticle Layout Data Inspection with Variable Sensitivity
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Solution Overview
Problem
Conventional reticle inspection systems fail to distinguish between electrically critical and non-critical areas, leading to inefficient inspection and potential defects in semiconductor fabrication, as they do not account for the printability sensitivity of different regions in reticle layout data.
Innovation Solution
A computer-implemented method that identifies regions in reticle layout data with varying printability sensitivity, assigning higher inspection, simulation, and review parameters to regions more sensitive to process parameter changes, allowing for differentiated inspection and simulation fidelity based on printability characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reticle inspection systems use uniform inspection parameters for all regions, then the inspection process is simple and fast, but the accuracy of detecting printability-sensitive defects is insufficient
Solution Approach 1:
The patent applies local quality by differentiating inspection parameters based on the printability sensitivity of specific reticle regions. Regions with high printability sensitivity (such as critical dimensions and spacing features) are inspected with higher precision and more stringent parameters, while regions with lower sensitivity use standard parameters. This localized approach improves detection accuracy for critical defects without unnecessarily complicating the entire inspection process.
Solution Approach 2:
The patent segments the reticle layout data into multiple regions based on printability sensitivity characteristics. By dividing the reticle into regions with different sensitivity profiles (e.g., critical features vs. non-critical features), the inspection system can apply appropriate parameters to each segment, balancing accuracy and complexity effectively.
2Reliability
If reticle inspection systems inspect all regions with high sensitivity, then defect detection is thorough, but inspection time and computational resources increase
Solution Approach 1:
The patent applies local quality by differentiating inspection parameters based on the printability sensitivity of specific reticle regions. Regions with high printability sensitivity (such as critical dimensions and spacing features) are inspected with higher precision and more stringent parameters, while regions with lower sensitivity use standard parameters. This localized approach improves detection accuracy for critical defects without unnecessarily complicating the entire inspection process.
Solution Approach 2:
The patent applies partial action by focusing high-sensitivity inspection only on regions where it is most needed. Instead of uniformly applying maximum inspection stringency across the entire reticle, the system applies enhanced inspection to critical regions with high printability sensitivity while using standard inspection for less critical regions, thereby optimizing the balance between reliability and productivity.
3Measurement precision
If the inspection threshold is set low to detect all potential defects, then detection sensitivity is high, but false positives increase and inspection efficiency decreases
Solution Approach 1:
The patent applies local quality by differentiating inspection parameters based on the printability sensitivity of specific reticle regions. Regions with high printability sensitivity (such as critical dimensions and spacing features) are inspected with higher precision and more stringent parameters, while regions with lower sensitivity use standard parameters. This localized approach improves detection accuracy for critical defects without unnecessarily complicating the entire inspection process.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting inspection thresholds and parameters based on the printability sensitivity characteristics of different reticle regions. The system modifies detection parameters (such as threshold values and inspection stringency) according to the specific requirements of each region, optimizing both sensitivity and efficiency by avoiding false positives in low-sensitivity regions while maintaining high detection capability in critical regions.
Data Source
AI summary
Various computer-implemented methods are provided. One method for generating a process for inspecting reticle layout data includes identifying a first region in the reticle layout data. A printability of the first region is more sensitive to changes in process parameters than a printability of a second region in the reticle layout data. The method also includes assigning one or more inspection parameters to the first region and the second region such that the first region will be inspected during the process with a higher sensitivity than the second region. Another method includes inspecting the first region with a higher sensitivity than the second region. An additional method includes simulating how the reticle layout data will print. Simulation of the first and second regions is performed with one or more different simulation parameters such that the first region is simulated with a higher fidelity than the second region.