Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process
Abstract
:1. Introduction
- We performed an on-chip, accelerated aging test to observe the effects of different stress signals and LUT configurations on FPGA aging, which showed how the frequencies of ROs change with aging and which aging mechanisms mainly affect 28-nm FPGAs;
- A measurement method was improved to correct measurement errors that were caused by the accelerated experiment and the corrected data were used for the analysis of the aging effects and the training of the aging prediction model;
- A variety of machine learning technologies were employed to predict the aging trends of FPGAs to evaluate the effectiveness of the ML models for the prediction of FPGA aging trends.
- The experimental results, based on a group of 28-nm XILINX 7-series FPGAs, showed that negative BTI (NBTI) was the main aging mechanism; moreover, the correction method proposed in this paper could effectively rectify measurement errors and in terms of aging prediction, the XGBoost-based ML model was competent for fitting the actual aging trends of FPGAs.
2. Background and Related Work
2.1. Aging Mechanisms
2.2. Aging Tests on FPGAs
2.3. Aging Prediction of FPGAs
3. Aging Test Implementation for FPGAs
3.1. Design of Test Solution
3.2. Accelerated Aging Conditions
3.3. Correction Method for Measurement Errors
4. Test Results and Analysis
4.1. Experimental Setup
4.2. Influence of Stress Signals on FPGA Aging
4.2.1. The Influence of Frequency
4.2.2. The Influence of Duty Cycle
4.3. Evaluation of Correction Method
4.4. Results of Aging Prediction
4.5. Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Conflicts of Interest
References
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Factor | Relationship | Stress Condition | Acceleration |
---|---|---|---|
Core Voltage Supply | 1.1 V | ≈ | |
Temperature | 373 K | ≈ | |
Voltage and Temperature | ≈20× |
Coefficient | ||||||
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Value | ||||||
Coefficient | ||||||
Value |
Coefficient | 100 h | 200 h | 300 h | 400 h | 500 h |
---|---|---|---|---|---|
Coefficient | 600 h | 700 h | 800 h | 900 h | 1000 h |
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Li, Z.; Huang, Z.; Wang, Q.; Wang, J.; Luo, N. Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process. Sensors 2022, 22, 4439. https://doi.org/10.3390/s22124439
Li Z, Huang Z, Wang Q, Wang J, Luo N. Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process. Sensors. 2022; 22(12):4439. https://doi.org/10.3390/s22124439
Chicago/Turabian StyleLi, Zeyu, Zhao Huang, Quan Wang, Junjie Wang, and Nan Luo. 2022. "Implementation of Aging Mechanism Analysis and Prediction for XILINX 7-Series FPGAs with a 28-nm Process" Sensors 22, no. 12: 4439. https://doi.org/10.3390/s22124439