AOCV (Advanced On-Chip Variation)

AOCV (Advanced On-Chip Variation)

Definition: Advanced On-Chip Variation (AOCV) is an extension of the traditional On-Chip Variation (OCV) modeling approach used in static timing analysis (STA). AOCV provides a more accurate and comprehensive modeling of local variations within a die by considering the spatial and temporal correlations of process parameters.

Key Points:

• Builds upon the concepts of traditional OCV modeling
• Captures the spatial and temporal correlations of process parameters
• Provides a more accurate representation of local variations within a die
• Enables more precise timing and power analysis
• Particularly important for advanced technology nodes (45nm and below)

Spatial Correlation:

• Captures the relationship between the variations of nearby transistors and interconnects
• Accounts for the fact that nearby devices are more likely to have similar variations
• Modeled using spatial correlation matrices or variograms
• Helps to reduce pessimism in timing analysis by considering the likelihood of correlated variations

Temporal Correlation:

• Captures the relationship between the variations of a single device over time
• Accounts for the fact that a device’s performance may vary over its lifetime due to aging effects
• Modeled using temporal correlation matrices or degradation models
• Helps to ensure the long-term reliability and performance of the circuit

AOCV in Static Timing Analysis (STA):

• Incorporates spatial and temporal correlation information into the STA flow
• Uses advanced statistical techniques, such as Principal Component Analysis (PCA), to model the correlations
• Generates location-dependent and time-dependent timing derates for each timing arc
• Provides more accurate and realistic timing predictions compared to traditional OCV

Benefits of AOCV:

• Reduces pessimism in timing analysis by considering correlated variations
• Enables more aggressive optimization and better design closure
• Improves the yield and reliability of the manufactured chips
• Helps to minimize overdesign and reduce design margins

Challenges:

• Increased complexity and computational overhead compared to traditional OCV
• Requires extensive characterization and modeling of spatial and temporal correlations
• May require changes to the existing STA tools and methodologies
• Balancing the trade-off between accuracy and runtime

AOCV is an important technique for modeling and analyzing local variations in advanced technology nodes, where the impact of variability becomes more significant. By considering the spatial and temporal correlations of process parameters, AOCV provides a more accurate and realistic representation of the circuit’s performance and behavior. This enables designers to make more informed decisions during the design and optimization process, leading to improved yield, reliability, and overall design quality.