Statistical Sensitivity Analyzer for IC Design
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Solution Overview
Problem
Current integrated circuit (IC) design methods often overlook critical parameter combinations, leading to inefficient architectures and potential system flaws, as designers typically focus on extreme parameter corners rather than systematically exploring the entire parameter space, especially when comparing configurations with similar performance.
Innovation Solution
A computer-implemented method and system for performing statistical sensitivity analysis of IC designs, which evaluates and ranks configurations based on selected device parameters, determines optimal values, and simulates performance to differentiate between configurations, using a database of simulation results to refine analysis and identify critical areas of the parameter space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If designers focus on extreme parameter corners, then design exploration is simplified, but critical parameter combinations are overlooked leading to system flaws
Solution Approach 1:
The patent segments the parameter space into discrete parameter sets, each representing a specific configuration corner. By systematically evaluating multiple parameter sets rather than continuous parameters, the method divides the complex exploration task into manageable discrete evaluations, reducing complexity while ensuring comprehensive coverage of critical combinations.
Solution Approach 2:
The patent performs preliminary action by pre-defining multiple parameter sets that represent critical corners of the parameter space before simulation. This preliminary structuring of the exploration space ensures that all critical combinations are considered from the outset, preventing overlooked configurations that would compromise system reliability.
2Reliability
If designers evaluate multiple parameter combinations, then system reliability improves, but design complexity and computational resources increase
Solution Approach 1:
The patent applies partial action by evaluating a selective subset of parameter combinations rather than all possible combinations. By identifying and focusing on critical parameter corners that most impact system reliability, the method achieves sufficient reliability assessment without the computational burden of exhaustive evaluation of every possible parameter combination.
Solution Approach 2:
The patent systematically changes parameters by defining multiple parameter sets with varying values for key parameters. This structured parameter variation allows comprehensive reliability assessment across critical configurations while maintaining manageable complexity through discrete, predefined parameter levels rather than continuous parameter spaces.
3Measurement precision
If exhaustive parameter space exploration is performed, then configuration differentiation is improved, but computational time and resources increase
Solution Approach 1:
The patent segments the parameter space into discrete parameter sets representing critical corners, enabling precise configuration differentiation through targeted evaluation. This segmentation approach achieves high measurement precision in identifying the best configuration by focusing on discrete, predefined parameter combinations rather than continuous exploration.
Solution Approach 2:
The patent performs partial action by evaluating only the critical parameter sets necessary for configuration differentiation. By selecting and evaluating a representative subset of parameter combinations that capture the essential performance variations, the method achieves sufficient precision for configuration comparison without the time cost of exhaustive exploration.
Data Source
AI summary
A method including evaluating a configuration of a device for a selected device parameter and determining a value of the selected device parameter in a first optimal configuration that improves a performance of the device is provided. The method includes determining a sensitivity of the performance of the device relative to the value of the selected device parameter and determining a performance metric that differentiates the first optimal configuration with a second optimal configuration based on the sensitivity of the performance of the device. The method includes ranking the first optimal configuration and the second optimal configuration based on the performance metric and simulating the performance of the device with a second device parameter in one of the first optimal configuration or the second optimal configuration, based on the ranking. A system and a computer readable medium to perform the above method are also provided.