CCSN Model Calibration for Waveform Propagation Accuracy
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Solution Overview
Problem
Existing models, such as Nonlinear Delay Model (NLDM), Composite Current Source for Timing (CCST), and Composite Current Source for Noise (CCSN), are inadequate for accurately modeling waveform propagation and timing analysis in integrated circuit designs, especially at lower geometries, leading to significant errors and performance impacts due to increased analog effects like the Miller effect and crosstalk.
Innovation Solution
The method involves calibrating the CCSN model using multi-segment receiver capacitance and NLDM models to determine calibration factors that minimize differences in capacitance and delay values, thereby improving the accuracy of waveform propagation modeling without requiring new characterization constructs or modifying existing library data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing models (NLDM, CCST, CCSN) are used for timing analysis, then the design process is simple and fast, but the accuracy of waveform propagation modeling deteriorates significantly at lower geometries
Solution Approach 1:
The patent applies preliminary action by pre-calibrating the CCSN model parameters using measured capacitance values from receiver cells before actual timing analysis. Calibration factors are determined in advance through comparison between CCSN model predictions and multi-segment receiver capacitance model measurements, storing these factors for subsequent use without requiring recalibration during design iterations.
Solution Approach 2:
The patent implements parameter changes by adjusting the CCSN model parameters (capacitance values, current source characteristics) based on measured data from receiver cells. The calibration process modifies these parameters to minimize differences between model predictions and actual measurements, thereby improving accuracy without changing the fundamental model structure.
2Reliability
If high margins are applied to compensate for model inaccuracies, then reliability improves, but performance, power consumption, and die area deteriorate
Solution Approach 1:
The patent replaces the mechanical approach of adding timing margins with a refined modeling approach. Instead of increasing margins to compensate for model inaccuracies, the solution substitutes the existing CCSN model with a calibrated version that accurately predicts waveform propagation, eliminating the need for conservative margin additions and thereby improving circuit performance.
3Measurement precision
If existing CCSN model parameters are used without calibration, then the model is easy to implement, but the accuracy of delay and slew computation deteriorates
Solution Approach 1:
The patent applies self-service by enabling the CCSN model to calibrate itself using measured data from receiver cells. The calibration process automatically adjusts model parameters based on comparisons between model predictions and actual measurements, allowing the model to improve its own accuracy without requiring manual intervention or complex external calibration procedures.
Data Source
AI summary
Disclosed is a method and apparatus that determines receiver capacitance values for a receiver cell from a multi-segment receiver capacitance model (C1Cn) model. Values for receiver capacitance are determined from a Composite Current Source for Noise (CCSN) model under conditions used to attain receiver capacitance values for the C1Cn model Difference values for the difference between the values from the CCSN model and from the C1Cn model are determined. Calibration factors are iteratively applied to parameters of the CCSN model to obtain a minimum difference value for difference between receiver capacitance values from the CCSN model and receiver capacitance values from the C1Cn model. Calibration factor values that result in the difference value being within an acceptable range are stored.