18437740. HARD-TO-FIX (HTF) DESIGN RULE CHECK (DRC) VIOLATIONS PREDICTION simplified abstract (Taiwan Semiconductor Manufacturing Company, Ltd.)
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
- 1.10 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 18437740 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 various electronic circuit placement layouts to predict fix rates of design rule check (DRC) violations in new layouts. The model then identifies hard-to-fix (HTF) DRC violations based on these fix rates and suggests fixes using an engineering change order (ECO) tool.
- Training a machine learning model with electronic circuit placement layouts
- Predicting fix rates of DRC violations in new layouts
- 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 design processes and improving overall efficiency in circuit placement.
Problems Solved
This technology helps in identifying and addressing hard-to-fix DRC violations in electronic circuit layouts, leading to improved design quality and reduced time spent on manual fixes.
Benefits
- Increased efficiency in electronic circuit design processes - Improved accuracy in identifying and fixing DRC violations - Reduction in manual effort required for fixing design rule check issues
Potential Commercial Applications
Optimizing electronic circuit design processes in semiconductor companies Improving design quality and efficiency in electronic circuit placement
Possible Prior Art
One possible prior art could be the use of machine learning models in electronic design automation tools to optimize circuit layouts and identify design rule violations.
Unanswered Questions
How does this technology compare to traditional methods of identifying and fixing DRC violations in electronic circuit layouts?
This article does not provide a direct comparison between this technology and traditional methods of identifying and fixing DRC violations.
What are the potential limitations or challenges of implementing this technology in real-world electronic circuit design processes?
This article does not address the potential limitations or challenges of implementing this technology in real-world electronic circuit design processes.
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.
