Semiconductor Layout Iteration for Low-Resistance Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing manual procedures for designing semiconductor device layouts often result in sub-optimal layouts with excessive resistance, leading to increased chip area, cost, and power dissipation, due to factors like parasitic metal routing resistance.
Innovation Solution
Utilizing electronic design automation (EDA) tools with artificial intelligence/machine learning (AI/ML) techniques to generate optimal semiconductor device layouts, which are then used to guide design teams and directly manufacture semiconductor devices, minimizing resistance through iterative simulations and modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual procedures are used to design semiconductor device layouts, then design simplicity is maintained, but the layout results in excessive resistance and sub-optimal performance
Solution Approach 1:
The patent replaces manual mechanical design procedures with an automated EDA tool system that uses AI/ML algorithms to generate and optimize layout configurations. The system automatically performs iterations of generating layouts with different parameters and analyzing their characteristics, substituting human manual work with computational automation to achieve optimal resistance values while maintaining design simplicity for the user.
Solution Approach 2:
The EDA tool system performs self-optimization by automatically iterating through different layout parameters, analyzing resistance characteristics, and adjusting configurations without external intervention. The system autonomously generates multiple layout iterations, evaluates their performance, and selects optimal configurations, enabling the design process to serve itself rather than requiring continuous manual adjustment.
2Reliability
If iterative generating and analyzing of layouts is performed, then resistance is minimized and performance is optimized, but computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining parameter ranges and constraints before the iterative optimization process begins. The EDA tool pre-configures the search space for layout parameters, establishing boundaries and initial conditions that guide the subsequent iterations, thereby reducing the overall computational time required to achieve optimal results.
Solution Approach 2:
The iterative process incorporates feedback mechanisms where each layout analysis result informs subsequent parameter adjustments. The system analyzes resistance characteristics from each iteration and uses this feedback to refine parameter values for the next iteration, creating a closed-loop optimization process that converges efficiently toward optimal solutions without requiring excessive computational resources.
Data Source
AI summary
In some implementations, a computing device may generate a layout of a semiconductor device based on one or more parameters regarding the layout of the semiconductor device. The computing device may analyze the layout of the semiconductor device. The computing device may perform iterations of generating the layout and analyzing the layout, wherein the one or more parameters are a first value during a first iteration of the iterations, and wherein the one or more parameters are a second value during a second iteration of the iterations. The computing device may generate a model of the semiconductor device based on performing the iterations. The computing device may provide the model to cause the semiconductor device to be manufactured based on the model.