Analog Device Grouping Prediction via Machine Learning
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Solution Overview
Problem
Existing electronic circuit design systems face challenges in automatically placing analog devices to meet snapping and grouping requirements, leading to inefficient optimization processes due to overlapping placements and difficulties in predicting device grouping and isolation, which require extensive manual effort.
Innovation Solution
A computer-implemented method using machine learning to analyze schematic features of devices, such as type, connectivity, size, and orientation, to determine whether devices should be grouped together, isolated, or clustered, applying decision tree-based classifiers to predict optimal placement and reduce manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If optimization variables are formulated for each possible device placement instance, then comprehensive placement options are provided, but the optimization process becomes inefficient due to overlapping placements and large design space
Solution Approach 1:
The patent segments the placement problem by first identifying and grouping devices that should be placed together based on schematic features (device type, connectivity, size, orientation), then optimizing only the positions of these grouped devices rather than all individual devices independently. This reduces the optimization variable space while maintaining placement versatility.
Solution Approach 2:
The patent performs preliminary analysis of schematic features to determine device grouping and isolation requirements before the actual placement optimization. By pre-determining which devices should be grouped together based on their schematic characteristics, the system eliminates unnecessary optimization iterations for overlapping placements and focuses computational resources on valid placement configurations.
2Manufacturing precision
If manual placement methods are used to meet snapping and grouping requirements, then placement precision is achieved, but extensive manual effort is required
Solution Approach 1:
The patent implements self-service automation where the system automatically analyzes schematic features (device type, connectivity, size, orientation) and autonomously determines device grouping and placement requirements. The machine learning model processes schematic data and generates placement recommendations without requiring manual intervention, thereby achieving placement precision while eliminating extensive manual effort.
Solution Approach 2:
The patent replaces manual mechanical placement operations with automated machine learning-based analysis. The system uses computational algorithms to process schematic features and generate placement decisions, substituting the manual designer's expertise and time investment with automated intelligent systems that achieve similar or superior placement precision.
3Measurement precision
If device grouping is predicted manually, then accurate grouping is achieved, but the design process becomes time-consuming
Solution Approach 1:
The patent replaces manual device grouping analysis with automated machine learning models that process schematic features and predict device grouping relationships. The system analyzes device type, connectivity, size, and orientation features to automatically determine which devices should be grouped together, achieving high grouping accuracy while significantly reducing the time required compared to manual analysis.
Solution Approach 2:
The patent implements continuous automated analysis of schematic features to predict device grouping throughout the design process. Rather than requiring discrete manual grouping decisions, the system continuously monitors and analyzes schematic characteristics, automatically updating device groupings and isolation predictions as the design evolves, thereby maintaining high accuracy while eliminating time-consuming manual interventions.
Data Source
AI summary
The present disclosure relates to a computer-implemented method for electronic design is provided. Embodiments may include receiving, using at least one processor, an electronic design schematic and an electronic design layout and analyzing, via machine learning, at least one schematic feature from a pair of devices associated with the electronic design schematic. Embodiments may further include determining, based, at least in part, upon the analyzing, whether the pair of devices should be grouped together.