Circuit Cell Variability Characterization via Process Perturbation Correlation
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Solution Overview
Problem
Current methods for characterizing performance parameter variability in circuit cell libraries due to manufacturing process perturbations are inefficient, requiring extensive SPICE modeling for each circuit cell, which is burdensome and assumes independent perturbations, leading to errors as process size diminishes and performance increases.
Innovation Solution
Identifying families of circuit cells with common perturbation sets that produce similar performance changes, determining correlation values between these families, and creating a representation of variability to estimate likely statistical distributions of performance parameters, reducing the need for extensive modeling and accounting for correlated variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive SPICE modeling is performed for each circuit cell to characterize performance parameter variability, then measurement precision and reliability of performance characterization is improved, but device complexity and loss of time increase significantly
Solution Approach 1:
The patent segments the circuit cell library into families based on shared characteristics (circuit topology, transistor types, sizing relationships). Instead of modeling every individual circuit cell, only representative cells from each family are modeled. This segmentation reduces the number of SPICE simulations required while maintaining comprehensive coverage of performance variability across the entire library.
Solution Approach 2:
The patent creates a compact representation model that captures the essential variability characteristics of entire families of circuit cells. This compact model serves as a copy that represents multiple individual cells, allowing performance variability to be characterized through fewer simulations. The compact representation includes family-level statistics and correlation structures that can be applied to all members of the family.
2Measurement precision
If extensive SPICE modeling is performed for each circuit cell, then measurement precision of performance parameter variability is improved, but device complexity increases
Solution Approach 1:
The modeling process is segmented into distinct phases: family identification, representative cell selection, SPICE modeling of representatives, and compact representation generation. This segmentation organizes the complex task into manageable steps, reducing the overall complexity of the modeling process while maintaining precision.
Solution Approach 2:
The compact representation model serves multiple functions simultaneously: it characterizes performance variability, captures correlation structures between parameters, and provides a basis for statistical timing analysis. This multi-functionality reduces the need for separate modeling efforts for different purposes.
3Ease of operation
If independent perturbation analysis is used for manufacturing process parameters, then ease of operation and simplicity of analysis is improved, but measurement precision deteriorates due to errors from assuming independence
Solution Approach 1:
The patent incorporates correlation analysis that provides feedback about the relationships between different manufacturing process parameter perturbations. By analyzing how perturbations in one parameter correlate with perturbations in other parameters, the method adjusts the statistical model to account for these dependencies, improving precision while maintaining analytical tractability.
Solution Approach 2:
The patent transforms the analysis from individual parameter perturbation to joint perturbation analysis. Instead of analyzing each manufacturing parameter independently, the method considers correlated changes in multiple parameters simultaneously, capturing the true statistical behavior of process variations while maintaining computational efficiency through the compact representation.
4Productivity
If family-based grouping with correlation analysis is implemented, then productivity and reduction of modeling effort is improved, but device complexity increases due to family identification and correlation calculation
Solution Approach 1:
The patent performs family identification and correlation analysis as preliminary steps before detailed performance characterization. By pre-grouping circuit cells into families and pre-calculating correlation structures, the method reduces the computational burden of subsequent analysis. This preliminary action organizes the data in a way that accelerates the main characterization task.
Solution Approach 2:
The correlation structures and family characteristics are captured in compact representation models that can be reused across multiple analysis scenarios. Once the families are identified and correlations are calculated, these representations serve as reusable artifacts that eliminate the need to repeat the complex identification and calculation processes for each new analysis.
Data Source
AI summary
A technique for characterising variation in a performance parameter(s) of circuit cells within a circuit cell library with perturbations in manufacturing process parameters uses a statistical approach whereby the statistical distribution of performance parameter(s) resulting from a joint distribution across manufacturing process parameter space is determined. The perturbation in manufacturing process parameter which results in a characteristic amount of variation is then identified and common sets of such perturbations used to group families of circuit cells together. Families of circuit cells have a correlation in their response to manufacturing process parameter perturbation and this is represented by a correlation matrix. Variation characterising data generated in accordance with the above technique is used to drive electronic design automation tools in integrated circuit design and manufacture.