EDA Design Space Tuning via Parameter Sensitivity Analysis
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Solution Overview
Problem
Current electronic design automation (EDA) methods face challenges in determining optimal interconnect wiring rules for high-speed signals, particularly due to the complexity introduced by numerous parameters such as interconnect length, impedance, and equalization capabilities, making exhaustive sweeping impractical due to the large number of variables involved.
Innovation Solution
The design space tuning process involves characterizing the design space using an objective function by randomly sampling parameters and systematically identifying the most sensitive parameters, allowing for reduction of the design space and focusing on key parameters, thereby narrowing down the analysis to essential components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive sweeping of all parameters is performed to determine optimal interconnect wiring rules, then complete analysis coverage is achieved, but computational complexity and time requirements become impractical
Solution Approach 1:
The patent extracts and identifies only the most sensitive parameters from the complete parameter set through systematic sensitivity analysis. By taking out the critical subset of parameters that have the greatest impact on signal integrity, the method achieves comprehensive analysis coverage for the most important factors without requiring exhaustive sweeping of all parameters, thus resolving the contradiction between complete analysis and computational feasibility
Solution Approach 2:
The patent segments the parameter analysis into distinct phases: initial sensitivity analysis to identify key parameters, followed by focused design space tuning on those specific parameters. This segmentation allows the method to achieve thorough analysis of critical factors while avoiding the computational burden of analyzing all parameters simultaneously, effectively balancing analysis completeness with computational practicality
2Adaptability or versatility
If all parameters are varied and analyzed in detail, then complete design space exploration is achieved, but analysis time increases from millions of cases to an impractical extent
Solution Approach 1:
The patent extracts the most sensitive parameters through systematic sensitivity analysis and focuses the design space exploration only on those extracted key parameters. This extraction approach maintains comprehensive design space exploration for the critical parameters while reducing the total number of simulation cases from millions to a manageable number, effectively resolving the time consumption issue
Solution Approach 2:
The patent performs preliminary sensitivity analysis before the main design space exploration. This preliminary action identifies which parameters warrant detailed exploration, allowing the method to achieve thorough design space coverage for important parameters while avoiding wasted computational time on parameters with minimal impact, thus resolving the contradiction between exploration completeness and time efficiency
3Reliability
If numerous parameters such as interconnect length, impedance, and equalization capabilities are all optimized, then comprehensive signal quality improvement is achieved, but the number of variables makes the process impractical
Solution Approach 1:
The patent extracts the most sensitive parameters through systematic sensitivity analysis, identifying which of the numerous parameters (interconnect length, impedance, equalization capabilities, etc.) have the greatest impact on signal quality. By focusing optimization efforts only on these extracted key parameters, the method achieves comprehensive signal quality improvement for the most critical factors while reducing the number of variables from numerous to a manageable subset, effectively resolving the contradiction between optimization completeness and practicality
Solution Approach 2:
The patent applies local quality by treating different parameters differently based on their sensitivity. Rather than uniformly optimizing all parameters, the method identifies and applies detailed optimization only to the most sensitive parameters, while other parameters receive less attention or are held constant. This localized approach achieves high signal quality where it matters most while avoiding the impracticality of optimizing every parameter equally
Data Source
AI summary
The present disclosure relates to a system and method for use with an electronic circuit design. The method may include providing, using at least one processor, an electronic design and modeling the electronic design to obtain a characteristic distribution associated with the electronic design, wherein modeling includes randomly varying one or more parameters associated with the electronic design. The method may further include identifying at least one key parameter from the modeled electronic design and reducing the electronic design only to the at least one key parameter. The method may also include in response to reducing, randomly varying the one or more parameters and re-modeling the reduced electronic design with the randomly varied one or more parameters.