Weak Pattern Severity Model for Semiconductor Layouts
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Solution Overview
Problem
Current methods for predicting weak patterns in semiconductor layouts are inefficient, often failing to detect all weak patterns due to strict design rules, requiring excessive manpower and time for screening, and have low prediction accuracy, leading to potential disconnections and short circuits in semiconductor devices.
Innovation Solution
A method and device using Design Of Experiment (DOE) technology to analyze severity levels of weak patterns by performing experiments with varying process parameter settings, followed by machine learning to train a weak pattern severity model, which predicts the severity of patterns with high accuracy, reducing the need for manual screening and improving product yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If strict design rules are used to detect weak patterns, then manufacturing precision is improved, but the number of detected weak patterns increases excessively requiring more screening resources
Solution Approach 1:
The patent changes the parameter of design rule strictness by introducing multiple severity levels (first, second, third levels) instead of using a single strict threshold. This allows the system to maintain high detection accuracy for critical weak patterns while reducing the volume of patterns requiring extensive screening, thus resolving the contradiction between manufacturing precision and screening efficiency.
2Measurement precision
If manual screening is performed on all detected weak patterns, then prediction accuracy is improved, but time consumption and labor resources increase significantly
Solution Approach 1:
The patent segments the weak pattern screening process into multiple severity levels (first level for critical patterns requiring manual review, second and third levels for less critical patterns that can be handled automatically). This segmentation allows the system to apply manual screening only where necessary, maintaining high prediction accuracy for critical cases while reducing overall time consumption.
Solution Approach 2:
The patent applies different quality standards and screening approaches to different severity levels of weak patterns. Critical patterns (first level) receive thorough manual screening, while less critical patterns (second and third levels) receive automated or reduced screening, optimizing the balance between accuracy and time efficiency for each category.
3Productivity
If comprehensive weak pattern screening is performed, then product yield is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent introduces a hierarchical severity classification system with multiple levels, changing the parameter of screening comprehensiveness from a single uniform approach to a differentiated multi-level approach. This maintains high product yield by thoroughly screening critical patterns while reducing system complexity by applying simpler screening methods to less critical patterns.
Data Source
AI summary
A method and a device for establishing a weak pattern severity model are provided. The method for establishing the weak pattern severity model includes the following steps. A plurality of weak patterns are obtained. A plurality of experiments are performed on each of the weak patterns with a plurality of parameter setting values of at least one process parameter to obtain a plurality of experimental results. According to the experimental results, a plurality of defects are obtained. According to the defects and the corresponding parameter setting values, a severity level of each of the weak patterns is analyzed. The weak patterns are labeled the severity levels. Machine learning is performed to train a weak pattern severity model.