AI Circuit Design Automation for Component Selection Optimization
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Solution Overview
Problem
The design of electronic circuits and circuit boards is a complex, time-consuming process that often relies on manual engineering efforts, making it difficult to optimize component selection and configuration within reasonable timeframes, especially when dealing with large numbers of components and multiple design variants.
Innovation Solution
A computer-implemented method and system that uses artificial intelligence and machine learning algorithms to automate the design process by receiving design specifications, determining key performance indicators, generating solution variants, and presenting them in a graphical user interface, thereby reducing the search space and computational resources required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual engineering efforts are used for circuit board design, then design flexibility and expertise utilization are improved, but design time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-defining blocks with their properties, connections, and associated hardware components before the actual design optimization process. This allows the AI algorithm to work with pre-structured data, reducing the time needed for component selection and configuration while maintaining design flexibility through the block-based approach.
Solution Approach 2:
The patent introduces an intermediary AI-based resolution service that acts as a mediator between the manual design process and automated optimization. This service receives design specifications, applies machine learning algorithms to optimize component selection, and returns optimized solutions, thereby reducing design time without completely eliminating human expertise involvement.
2Reliability
If comprehensive component selection is performed manually, then component optimization is improved, but computational resources and time required increase
Solution Approach 1:
The patent segments the component selection process into discrete blocks, where each block represents a functional unit with specific properties and connections. This segmentation allows the AI algorithm to process and optimize each block independently, reducing the overall computational complexity while maintaining comprehensive component optimization through systematic evaluation of all blocks.
Solution Approach 2:
The system changes parameters by representing hardware components and their properties in a standardized data structure format. This parameter transformation enables efficient AI-based optimization by converting complex component specifications into processable data elements that can be systematically evaluated and optimized without overwhelming computational resources.
3Measurement precision
If multiple design variants are evaluated manually, then solution quality is improved, but time consumption and resource usage increase
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an AI-based automated system. The machine learning algorithms automatically evaluate multiple design variants by processing block definitions, connections, and hardware component data, thereby maintaining high solution quality through systematic analysis while dramatically increasing design throughput by eliminating manual evaluation bottlenecks.
Data Source
AI summary
The system receives user-provided input requirements. The system automatically generates solution variants for an electronics hardware system using the user-provided input requirements and artificial intelligence. The system automatically evaluates the solution variants using additional artificial intelligence. The system presents the solution variants and provides visual feedback to engineers to evaluate the solutions based on metrics.