Attention Network Circuit Routing to Reduce Design Overhead
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Solution Overview
Problem
The process of routing electrical circuits is complex and resource-intensive, requiring careful planning to ensure electrical efficiency, minimize interference, and adhere to design and manufacturing constraints, while efficiently managing heat, signal integrity, and layer management, which existing methods often fail to address efficiently.
Innovation Solution
The use of attention networks with two separate attention mechanisms to process input data representing electrical circuits, accounting for both the topography and layout, and the positions and actions of multiple agents, iteratively determining optimal routing components to connect circuit elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing methods are used to determine connections between circuit elements, then the routing process can be completed, but the design and manufacturing overheads are high and the process is resource-intensive
Solution Approach 1:
The patent replaces traditional manual or conventional automated routing methods with an attention-based neural network system. The neural network processes encodings of circuit topography and agent positions to automatically determine optimal routing components, substituting complex mechanical routing processes with intelligent computational methods that reduce time and resource consumption.
Solution Approach 2:
The patent creates encoded representations (encodings) of the circuit board topography and agent positions that serve as simplified models for the neural network to process. These encodings capture essential routing information in a compressed format, allowing the system to quickly determine routing paths without processing every detail of the physical circuit layout.
2Reliability
If routing paths are optimized to minimize interference between signals, then signal integrity is improved, but the routing process becomes more complex
Solution Approach 1:
The neural network system incorporates feedback mechanisms where the encodings of agent positions and circuit topography are continuously processed to generate routing decisions. The system iteratively refines routing paths by processing updated encodings, allowing it to automatically adjust paths to minimize interference while maintaining signal integrity without requiring complex manual intervention.
Solution Approach 2:
The attention network serves multiple functions simultaneously: it processes topography information, tracks agent positions, determines routing paths, and optimizes for signal integrity. This multi-functional approach consolidates what would traditionally require separate complex processes into a single integrated system that handles routing optimization automatically.
3Measurement precision
If multiple agents are used to determine connections for different nets, then routing accuracy is improved, but the computational load increases
Solution Approach 1:
The patent divides the routing problem into separate agent tasks, where each agent is responsible for determining connections for specific nets. Each agent processes encodings independently to determine routing paths for its assigned nets, allowing parallel computation that improves accuracy through specialized processing while managing computational load through distributed task assignment.
Solution Approach 2:
The patent transforms the routing problem from physical spatial coordinates to encoded representations that capture topological and positional relationships in a transformed dimension. This encoding approach allows the neural network to process routing information more efficiently by working with abstract representations rather than raw spatial data, reducing computational complexity while maintaining routing accuracy.
Data Source
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AI summary
A method of generating output data for manufacturing an electrical circuit is provided. A system and storage medium for implementing the method are also provided. The method includes obtaining input data representing an electrical circuit, and processing the input data to generate a first encoding for circuit elements and a second encoding for agents. An attention network, that implements two attention mechanisms, one for the first encoding and one for the second encoding, is provided. A process of selecting routing component characteristics is performed iteratively, using the attention network to determine routes for connecting circuit elements. Output data for manufacturing the electrical circuit is generated based on the selected routing component characteristics.