Variational Noise Model for IC Coupled Noise Prediction
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Solution Overview
Problem
Integrated circuit design engineers face challenges in predicting coupled noise levels under various process corners and operating conditions, which affects the design's robustness and cost-effectiveness.
Innovation Solution
A computer-implemented method that identifies noise clusters, represents them using a variational model, projects this model onto different corners to yield a projected noise cluster, and determines the computed noise, maximizing terms that increase noise and minimizing those that decrease it, thereby establishing a theoretical upper bound for noise analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise analysis methods are used to predict coupled noise under various process corners, then the prediction accuracy is limited, but the design robustness and cost-effectiveness deteriorate
Solution Approach 1:
The patent applies parameter changes by transforming the noise analysis from traditional fixed-corner methods to a continuous variational model that parameters process corners continuously. The noise cluster is represented as a function of process variables, allowing smooth transitions between corners and enabling more accurate prediction of noise behavior under varying conditions without requiring discrete sampling at each corner point.
Solution Approach 2:
The patent introduces dynamics by projecting the variational noise model onto different process corners dynamically. Instead of static noise values at fixed corners, the model allows noise characteristics to evolve continuously as process parameters change, capturing the dynamic behavior of coupled noise across process variations and improving prediction accuracy for robustness assessment.
2Reliability
If comprehensive noise analysis considering all process corners is performed, then noise prediction robustness improves, but computational complexity and cost increase
Solution Approach 1:
The patent applies segmentation by dividing the complex noise analysis problem into manageable noise clusters, where each cluster represents a specific group of coupled noise sources. This segmentation allows the variational model to be applied to individual clusters independently, reducing the overall computational complexity while maintaining comprehensive coverage of all noise sources across process corners.
Solution Approach 2:
The patent uses partial action by focusing the variational model and corner projection on dominant noise clusters that contribute most to coupled noise, rather than treating all noise sources with equal detail. This approach achieves robust noise prediction by concentrating computational resources on the most significant noise contributors, reducing overall analysis complexity while maintaining accuracy for critical noise paths.
Data Source
AI summary
A computer-implemented method includes identifying a noise cluster, representing the noise cluster according to a variational model, projecting the variational model onto one or more corners to yield a projected noise cluster, and determining a computed noise for the projected noise cluster. Optionally, the noise cluster includes one or more noise cluster elements, and each of the noise cluster elements are expressed as one or more circuit element terms, according to a canonical form. Optionally, at least one of the corners is a bounding corner. For the bounding corner, the projected noise cluster is generated by maximizing the circuit element terms for those noise cluster elements that tend to increase noise, and by minimizing the circuit element terms for those noise cluster elements that tend to decrease noise, whereby noise is maximized for the canonical form. A corresponding computer program product and computer system are also disclosed.