Machine Learning Design Space Reduction for Electrical Circuits
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Solution Overview
Problem
Electrical circuit designers face long design cycles and sub-optimal performance due to the exponential growth of possible designs when using manual trial-and-error methods, making it difficult to efficiently generate and test feasible electrical devices.
Innovation Solution
A system and method utilizing machine learning models to predict a reduced set of feasible designs from a component library, which are then optimized based on design criteria, reducing the design space and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual trial-and-error methods are used to design electrical circuits, then designers can explore all possible designs, but the design cycle becomes extremely long and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical trial-and-error design processes with an automated computer-based system that uses machine learning models and algorithms to generate, simulate, and evaluate circuit designs automatically, eliminating the need for manual iteration while maintaining comprehensive design exploration
Solution Approach 2:
The patent introduces computer-based simulation software and machine learning models as intermediaries between the designer's requirements and the final circuit design, enabling automated evaluation of design feasibility and performance before physical implementation
2Reliability
If all possible designs are generated from a component library, then design completeness is achieved, but the numerical complexity and computational burden become unmanageable
Solution Approach 1:
The patent applies preliminary filtering using machine learning models to predict and eliminate infeasible or suboptimal designs before full simulation, performing preliminary assessments of design viability based on component characteristics and design requirements to reduce the number of designs requiring detailed analysis
Solution Approach 2:
The patent segments the design evaluation process into multiple stages: initial generation of possible designs, machine learning-based filtering of infeasible options, detailed simulation of promising candidates, and final optimization, dividing the complex task into manageable segments that can be processed efficiently
3Manufacturing precision
If comprehensive design testing and simulation are performed, then design accuracy and reliability improve, but the time and computational resources required increase significantly
Solution Approach 1:
The patent performs preliminary filtering using machine learning models to identify and eliminate designs that are likely to fail simulation or meet requirements, conducting preliminary assessments of design viability before committing resources to full simulation and testing of all possible designs
Solution Approach 2:
The patent applies partial testing strategies where not all possible designs undergo complete simulation and testing - instead, machine learning models identify the most promising subset of designs for detailed analysis, performing sufficient testing on likely candidates while avoiding wasteful testing of obviously inferior options
Data Source
AI summary
A component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received. At least one design solution is generated based on the set of design criteria and the predicted designs.