EDA Symbol Generation via Image Recognition and Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing electronic design automation (EDA) tools face challenges in interoperability due to different file formats for component libraries and designs, leading to inefficiencies and inaccuracies when converting symbols and designs between tools, particularly in the absence of data read/write APIs or readable data formats.
Innovation Solution
The solution employs image processing, graphical shape and text recognition, and machine learning techniques to generate component symbols and designs in any desired format by extracting feature vectors from images, applying recognition models, and associating data for output in targeted systems, allowing for further refinement of these models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual conversion methods are used to transfer component symbols between EDA tools, then interoperability between different EDA tools can be achieved, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical conversion processes with automated image processing and machine learning systems. The system captures component symbols as images, processes them through neural networks to extract features, and automatically generates target format files, eliminating the need for manual copying and conversion between EDA tool formats.
Solution Approach 2:
The patent creates digital copies of component symbols through image capture and processing. Instead of manually transferring design data, the system captures the visual representation of components, processes the image to extract symbolic information, and generates corresponding data files for target EDA tools, enabling rapid replication of design elements.
2Adaptability or versatility
If manual conversion methods are used to transfer component symbols between EDA tools, then interoperability between different EDA tools can be achieved, but accuracy and precision are reduced due to human errors
Solution Approach 1:
The patent replaces manual conversion processes with automated computer-based image processing and machine learning algorithms. The system objectively analyzes component symbol images, extracts features through programmed algorithms, and generates target format files without human intervention, eliminating transcription errors and improving conversion accuracy.
Solution Approach 2:
The patent implements feedback mechanisms where the system can verify extracted features against expected patterns and refine its processing. The machine learning models learn from training data and can be refined based on performance metrics, continuously improving the accuracy of symbol conversion between different EDA tool formats.
3Productivity
If automated image processing and machine learning techniques are used to generate component symbols, then productivity and conversion accuracy are significantly improved, but the system complexity increases
Solution Approach 1:
The patent divides the complex symbol generation process into distinct modular stages: image capture, preprocessing, feature extraction through neural networks, data association, and output generation. Each module performs a specific function and can be independently optimized or replaced, managing system complexity through functional decomposition.
Solution Approach 2:
The patent creates a universal system that can handle multiple EDA tool formats and component types through a single image processing pipeline. The machine learning models are designed to recognize various symbol types and generate outputs for different target formats, reducing the need for separate specialized tools for each conversion task.
Data Source
AI summary
The present embodiments are generally directed to electronic circuit design and verification and more particularly to techniques for generating electronic design element symbols for electronic circuit design tool libraries and designs in any desired format. In embodiments, such electronic design element symbols can be generated from a datasheet or any other image using image processing, graphical shape and text recognition techniques. Embodiments use step by step processing to extract feature vectors from a symbol/design image, apply text and graphical shapes recognition using models, apply techniques for data association and write the final output for targeted systems. These and other embodiments can feed back the output data for further refinement of the recognition models.