Aging Simulation via Digital Waveform Segmentation
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Solution Overview
Problem
Existing technologies face challenges in accurately and efficiently simulating the aging of electronic circuits under time-dependent variability, particularly due to workload signals like voltage and temperature sequences, which impact reliability and performance over extended lifetimes.
Innovation Solution
The method involves grouping contiguous sets of cycles into segments based on unique segment parameters such as duty factor, frequency, and time duration, and applying an aging model to these segments to simulate device aging. This approach allows for the representation of digital waveforms and the calculation of degradation based on workload patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation methods are used to model aging under time-dependent variability, then measurement precision and reliability can be improved, but computational time and device complexity increase significantly
Solution Approach 1:
The workload signal is segmented into distinct segments based on changing characteristics (voltage, temperature, frequency). Each segment is processed independently through simplified aging models, reducing overall computational complexity while maintaining accuracy. The segmentation approach allows the system to handle complex time-dependent variability without requiring exhaustive simulation of every possible condition.
Solution Approach 2:
The method changes the parameters of the aging model based on the characteristics of each workload segment. Different aging models or model parameters are applied depending on the voltage, temperature, and frequency conditions of each segment. This parameter adaptation enables accurate aging prediction across varying conditions without requiring a single complex universal model.
2Reliability
If detailed workload signal analysis is performed to capture all variability, then reliability assessment improves, but device complexity and computational overhead increase
Solution Approach 1:
The workload signal is divided into manageable segments with distinct characteristics. This segmentation reduces the complexity of analyzing the entire signal by breaking it down into smaller, more manageable portions that can be processed through simplified models. Each segment maintains enough detail to capture relevant aging effects while reducing overall computational burden.
Solution Approach 2:
The method extracts key characteristics from the workload signal (such as voltage levels, temperature conditions, frequency ranges) and uses these extracted parameters to drive the aging simulation. By taking out only the essential features rather than processing the complete complex signal, the system achieves reliable aging assessment with reduced computational overhead.
3Measurement precision
If extended lifetime simulation is performed to capture cumulative degradation, then measurement precision improves, but computational time increases exponentially
Solution Approach 1:
The extended lifetime simulation is divided into multiple segments corresponding to different operational phases or conditions. Each segment contributes to the cumulative degradation in a manageable way, allowing the system to predict long-term aging without requiring a single continuous simulation of the entire lifetime. The segmentation enables modular computation that scales more efficiently with time.
Solution Approach 2:
The method handles periodic or repeating workload patterns by processing each period as a segment and applying appropriate aging models. For repetitive patterns, the system can use the results from one or a few periods to extrapolate long-term degradation, reducing the need to simulate every single period in detail and thereby decreasing computational time while maintaining prediction accuracy.
Data Source
AI summary
A system and method of simulating device aging based on a digital waveform representative of a workload of an electronic device are disclosed. In one aspect, the method comprises grouping contiguous sets of cycles into segments, each set corresponding to a segment. Each segment has values for a combination of segment parameters that are unique from each of the other segments and a start point that is separated from a start point of an adjacent segment by a pre-defined distance criterion. Grouping the sets into the segments comprises, for each segment: sampling one or more sequential cycles of the workload, generating the segment based on the sampled contiguous cycles having a period exceeding a threshold period, and determining the values for the combination of segment parameters. The method further comprises applying an aging model to the segments to simulate the aging. The segments are a representation of the digital waveform.