AI-Driven Wiring Layout Optimization for Parasitic Reduction
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Solution Overview
Problem
Existing wiring layout design methods terminate when parasitic capacitance and wiring resistance reach allowable values, leading to suboptimal layouts and higher allowable values to satisfy design rules.
Innovation Solution
A wiring layout design method that involves obtaining initial layout information, generating initial and subsequent wiring layouts, performing Design Rule Check (DRC), and using learning techniques, such as Q-learning or neural networks, to optimize the layout information based on parasitic capacitance and wiring resistance values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the wiring layout design terminates when parasitic capacitance and wiring resistance reach allowable values, then the design process is simple and fast, but the wiring layout cannot be fully optimized and may not achieve the minimum possible parasitic capacitance and wiring resistance
Solution Approach 1:
The patent implements a feedback mechanism where the learning model continuously receives parasitic capacitance and wiring resistance values as feedback, compares them against target values, and adjusts the wiring layout design accordingly. This allows the design process to iterate and optimize beyond simple termination criteria, achieving minimal parasitic capacitance and wiring resistance while maintaining design rule compliance.
Solution Approach 2:
The patent changes the approach from fixed termination criteria to dynamic parameter optimization. The learning model adjusts design parameters (wiring width, spacing, layer positioning) based on real-time parasitic capacitance and wiring resistance calculations, enabling continuous optimization until target values are achieved rather than stopping at predetermined thresholds.
2Ease of manufacture
If higher allowable values are set for parasitic capacitance and wiring resistance to satisfy design rules, then the design process is easier and faster, but the signal integrity deteriorates and power consumption increases
Solution Approach 1:
The learning model uses feedback from parasitic capacitance and wiring resistance measurements to iteratively optimize the wiring layout. By continuously comparing actual values against target values and adjusting the design accordingly, the system achieves both design rule compliance and optimal signal integrity, avoiding the need to settle for higher allowable values.
Solution Approach 2:
The patent introduces dynamic optimization where the allowable values are not fixed but are determined through iterative learning. The system adapts the design parameters dynamically based on real-time calculations of parasitic capacitance and wiring resistance, achieving the lowest possible values that satisfy both design rules and signal integrity requirements.
3Reliability
If the wiring layout is optimized to minimize parasitic capacitance and wiring resistance, then signal integrity improves and power consumption reduces, but the design process becomes more complex and time-consuming
Solution Approach 1:
The patent replaces traditional manual or rule-based wiring layout optimization with an AI learning model. The learning model automatically performs iterative optimization by processing parasitic capacitance and wiring resistance data, eliminating the need for complex manual design processes while achieving optimal signal integrity and minimal power consumption.
Solution Approach 2:
The learning model performs self-service optimization by automatically adjusting wiring layout parameters based on parasitic capacitance and wiring resistance feedback. The system self-corrects and iterates without requiring external intervention, simplifying the overall design process while achieving optimal performance.
4Loss of time
If traditional wiring layout design methods are used, then the design process is simple and quick, but the parasitic capacitance and wiring resistance cannot be reduced to the minimum possible values
Solution Approach 1:
The learning model implements feedback-driven optimization where parasitic capacitance and wiring resistance values are continuously monitored and used to adjust the wiring layout. This feedback mechanism enables the system to reduce parasitic capacitance and wiring resistance to minimum possible values while managing design time through automated iterative processes.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining an iterative optimization process that continues until target parasitic capacitance and wiring resistance values are achieved. The learning model continuously refines the wiring layout, ensuring that every design iteration contributes to reducing energy loss while managing total design time through efficient automated processing.
Data Source
AI summary
A novel wiring layout design method is provided. A wiring layout in which a starting terminal group and an end terminal group are electrically connected to each other is generated using layout information and a netlist. In the case where the wiring layout satisfies a design rule, a wiring resistance and a parasitic capacitance of the wiring layout are extracted. The layout information is updated using Q learning and a new wiring layout is generated. In the Q learning, a positive reward is given when the values of the wiring resistance and the parasitic capacitance decrease, and a weight of the neural network is updated in accordance with the reward. In the case where the new wiring layout satisfies the design rule, a wiring resistance and a parasitic capacitance of the new wiring layout are extracted. In the case where the change rate of the wiring resistance and the parasitic capacitance is high, the layout information is updated using the Q learning.