Taiwan semiconductor manufacturing company, ltd. (20240176945). HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION simplified abstract
Contents
- 1 HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.9.1 Unanswered Questions
- 1.9.2 How does this method compare to traditional manual methods of fixing DRC violations in electronic circuit placement layouts?
- 1.9.3 What are the limitations of using a machine learning model for predicting fix rates of DRC violations in electronic circuit placement layouts?
- 1.10 Original Abstract Submitted
HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION
Organization Name
taiwan semiconductor manufacturing company, ltd.
Inventor(s)
Ching Hsu of Hsinchu County (TW)
Shih-Yao Lin of Hsinchu City (TW)
Yi-Lin Chuang of Taipei City (TW)
HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240176945 titled 'HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION
Simplified Explanation
The method described in the abstract involves training a machine learning model with electronic circuit placement layouts, predicting fix rates of design rule check (DRC) violations, identifying hard-to-fix (HTF) DRC violations, and fixing the violations using an engineering change order (ECO) tool.
- Training a machine learning model with electronic circuit placement layouts
- Predicting fix rates of DRC violations for a new electronic circuit placement layout
- Identifying hard-to-fix (HTF) DRC violations based on fix rates
- Fixing DRC violations using an engineering change order (ECO) tool
Potential Applications
This technology could be applied in the semiconductor industry for optimizing electronic circuit placement layouts and improving the efficiency of fixing design rule check violations.
Problems Solved
1. Streamlining the process of identifying and fixing hard-to-fix DRC violations in electronic circuit placement layouts. 2. Enhancing the accuracy of predicting fix rates for design rule check violations.
Benefits
1. Increased efficiency in addressing design rule check violations. 2. Improved overall quality and reliability of electronic circuit designs.
Potential Commercial Applications
Optimizing electronic circuit placement layouts in semiconductor manufacturing processes for increased productivity and reduced design errors.
Possible Prior Art
One possible prior art could be the use of machine learning models in the semiconductor industry for various optimization tasks, such as layout design and error detection.
Unanswered Questions
How does this method compare to traditional manual methods of fixing DRC violations in electronic circuit placement layouts?
This article does not provide a direct comparison between the proposed method and traditional manual methods of fixing DRC violations.
What are the limitations of using a machine learning model for predicting fix rates of DRC violations in electronic circuit placement layouts?
The article does not address any potential limitations or challenges associated with using a machine learning model for predicting fix rates of DRC violations.
Original Abstract Submitted
a method includes: training a machine learning model with a plurality of electronic circuit placement layouts; predicting, by the machine learning model, fix rates of design rule check (drc) violations of a new electronic circuit placement layout; identifying hard-to-fix (htf) drc violations among the drc violations based on the fix rates of the drc violations of the new electronic circuit placement layout; and fixing, by an engineering change order (eco) tool, the drc violations.
