Data Mining for Analog Circuit Design Visualization
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Solution Overview
Problem
Current design tools for semiconductor circuits lack the ability to effectively extract and visualize the complex nonlinear relationships between design variables, random variables, and environmental variables, leading to difficulties in optimizing performance and yield, especially when dealing with large datasets and multiple variables.
Innovation Solution
A method and system that utilizes data mining techniques, including classification and regression trees, Bayes networks, and regression models, to process and visualize circuit simulation data, identifying causal dependencies, clustering, and reducing dimensionality to provide insights into the impact of individual elements and interactions on performance and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional circuit simulation tools are used to generate datasets, then circuit performance can be evaluated, but the complex nonlinear relationships between multiple variables cannot be effectively extracted and visualized
Solution Approach 1:
The patent introduces data mining algorithms (classification and regression trees, Bayes networks, regression models) as intermediary tools between the circuit simulation data and the designer's understanding. These algorithms process the complex nonlinear relationships between design variables, random variables, and environmental variables, extracting meaningful patterns and visualizing them in a manageable format that reveals causal dependencies without requiring direct manual analysis of the complex data.
Solution Approach 2:
The patent replaces manual mechanical analysis of circuit data with automated data mining techniques. Instead of manually examining schematics and deriving modeling equations, the system automatically processes simulation datasets through machine learning algorithms that can handle complex nonlinear relationships, substituting human analytical effort with computational intelligence that scales effectively with data complexity.
2Loss of information
If comprehensive simulation data is collected for all design variables, then complete performance insight is achieved, but data management and analysis becomes overwhelming
Solution Approach 1:
The patent extracts meaningful patterns, causal relationships, and key insights from the comprehensive simulation data through data mining algorithms. Rather than presenting the full complex dataset, the system extracts and visualizes only the essential relationships between variables, separating the signal from the noise and presenting distilled information that maintains completeness of insight while dramatically improving ease of analysis and interpretation.
Solution Approach 2:
The patent segments the complex data analysis task into manageable components through hierarchical data mining approaches. The system divides the data into structured representations (such as decision trees and network models) that organize relationships between variables in a hierarchical manner, making the analysis process more manageable and the insights more accessible while preserving comprehensive coverage of all design variables.
3Loss of information
If manual derivation of modeling equations is performed, then design insight is obtained, but time consumption increases
Solution Approach 1:
The patent replaces the manual mechanical process of deriving modeling equations with automated data mining algorithms that directly extract relationships from simulation data. The machine learning models automatically learn the functional relationships between variables without requiring manual mathematical derivation, significantly reducing the time required to obtain design insights while maintaining or improving the quality of the extracted relationships through computational optimization.
Solution Approach 2:
The patent performs preliminary data processing and feature engineering automatically as part of the data mining process. The system pre-processes the simulation data, identifies relevant features, and prepares the data structure needed for analysis before the actual insight extraction begins, eliminating the need for manual preliminary derivation work and reducing overall processing time while ensuring comprehensive data preparation.
Data Source
AI summary
A system and method of generating a set of circuit simulation data, applying data mining to for knowledge extraction from the data, and graphically presenting the extracted knowledge in a format that is easy to digest to a designer.