IC Timing Degradation Prediction via Resource Scaling
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Solution Overview
Problem
Integrated circuit devices (ICs) face timing degradations such as jitter, push-out, pull-in, change in rise time, and change in fall time, which affect data signal timing and are not accurately predicted by existing technologies, leading to inefficiencies in circuit design and performance.
Innovation Solution
A computer-implemented method to predict timing characteristics in ICs by determining configuration information and measuring timing degradation, including scaling push-out, pull-in, rise time, and fall time based on resource utilization of configuration logic blocks and input/output banks, allowing for threshold setting and status indication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing timing prediction technologies are used, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent segments timing degradation into distinct components (push-out, pull-in, rise time, fall time) and calculates each separately based on different resource utilization factors. This segmentation allows for more accurate predictions by addressing each timing component independently with appropriate scaling factors, rather than using a single粗放 prediction method.
Solution Approach 2:
The patent changes the parameters used for timing prediction by introducing resource utilization metrics (CLB usage, I/O bank usage) as scaling factors. Instead of using fixed timing values, the method dynamically adjusts timing degradation parameters based on actual resource usage, improving prediction accuracy while maintaining a systematic approach.
2Measurement precision
If resource utilization scaling is applied to timing degradation measures, then prediction accuracy improves, but calculation complexity increases
Solution Approach 1:
The patent implements feedback by using resource utilization measurements (CLB usage, I/O bank usage) to scale timing degradation measures. The utilization data feeds back into the calculation process, allowing the prediction to adapt to actual resource usage patterns. This feedback mechanism improves accuracy by accounting for the impact of resource contention on timing performance.
Solution Approach 2:
The patent transforms static timing degradation values into dynamic parameters by applying resource utilization scaling factors. The timing measures change based on utilization levels, allowing the system to predict timing behavior under different loading conditions without requiring separate measurements for each scenario.
3Reliability
If separate scaling for CLB usage and I/O bank usage is performed, then timing prediction comprehensiveness improves, but processing time increases
Solution Approach 1:
The patent segments the timing degradation calculation into separate components for CLB usage and I/O bank usage. Each component is scaled independently based on its respective utilization metric, allowing for comprehensive coverage of different resource types. This segmentation improves reliability by ensuring that timing predictions account for bottlenecks in specific resource areas.
Solution Approach 2:
The patent applies partial scaling actions by separately adjusting timing measures for CLB usage and I/O bank usage. Rather than attempting to calculate all factors simultaneously, the method applies incremental scaling adjustments for each resource type, which improves comprehensiveness while managing processing complexity through staged calculations.
Data Source
AI summary
A computer-implemented method of predicting timing characteristics within a semiconductor device can include determining configuration information for the semiconductor device and determining a measure of timing degradation for data signals of the semiconductor device according to the configuration information. The measure of timing degradation for the data signals can be output.