Single event effects sensitivity of low energy proton in Xilinx Zynq-7010 system-on chip

Du, Xuecheng; Liu, Shuhuan; Luo, Dongyang; Zhang, Yao; Du, Xiaoyong; He, Chaohui; Ren, Xiaotang; Wu, Yang; Yuan, Yuan

doi:10.1016/j.microrel.2017.02.014

Cited by 20 publications

(5 citation statements)

References 12 publications

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“…The variable n is a Poisson variable with parameter  , where  represents the average probability of SEU occurring. By summing over all possible values of n , we can obtain (2), where Gm represents the n -fold convolution of () Gm , denoting the distribution function of the sum of n independently and identically distributed random variables.…”

Section: Probability Modelmentioning

confidence: 99%

“…As integrated circuit processes are refined and electronics become more integrated, the frequency of SEE increases. These effects have been statistically shown to be a major source of failures in today's spacecraft [1] [2], among which single-event upsets (SEUs) are the most common type of error, making it necessary to quantitatively assess the impact of SEU on neural network chips.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

Yan,

Gao

2024

J. Phys.: Conf. Ser.

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The Convolutional neural networks have gradually penetrated into various fields of social life, in addition to aerospace devices, neural network chips are also indispensable for target recognition and detection, etc. Due to the complex environment in space, neural network chips are extremely easy to be damaged by cosmic rays, so it is urgent to evaluate and enhance the robustness of neural network chips. However, most of the existing researches are monolithic error injection of neural networks and do not build relevant models for quantitative analysis. The goal of this paper is to analyse the effects of single-event upsets (SEUs) induced by the penetration of SRAM storage areas in a quantized neural network chip by energetic particles from space. Firstly, a SEU probability model as well as an uncertainty assessment model are established, and the effect of SEU on the reliability of CNNs is evaluated by fault injection with the upset rate of four chip types. The results of the error injection experiments show that the Virtex-5 type of rollover rate decreases the average accuracy of VGG-16 by 20%, while the other three types of error injection have an impact of about 11% on the accuracy of the neural network. Quantitative experimental results confirm the hypothesis that SEU increases the uncertainty of neural networks. The quantification of these effects contributes to the study of reinforcement of neural network chips.

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Section: Probability Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

Yan,

Gao

2024

J. Phys.: Conf. Ser.

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“…究对象, 杜雪成、杨卫涛课题组系统研究了SoC 辐射效应. 开展了α单粒子效应实验和低能质子单粒子效应实验, 测得系统级芯片的α单粒子效应敏感模块、单粒子效应截面及不同模块质子单粒子效应截面随能量变化的关系曲线, 评估了Xilinx Zynq-7000 SoC 中7个模块的α粒子 [87] 和低能质子 [88] 的单粒子效应敏感性; 开展了故障注入和概率风险分析研究 [85,96] , 获得了系统级芯片多个功能单元的敏感单元以及故障表现类型, 并且通过建立系统芯片软错误故障树, 定量计算了系统芯片及其各功能单元的故障率和不可用度, 确定了系统和子系统中的敏感模块. Yang等人 [94] 应用重离子微束辐照研究Xilinx 28 nm SoC的单粒子敏感部位. Liu等人 [89] 应用激光辐照研究了Xilinx 28 nm SoC 的单粒子效应敏感性.…”

Section: Risc-v处理器核、fpga、存储芯片、接口电路等;unclassified

Radiation effects and radiation hardening technology of new microsystems

HE,

CHEN,

HAN

et al. 2024

Sci. Sin.-Phys. Mech. Astron.

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“…Therefore, single event effects are becoming to be increasingly serious impacts on aerospace integrated circuits [7]. Studies have shown that single event effects are the main cause of failures in spacecraft today [8,9]. Hence, the evaluation of the radiation-induced vulnerability of integrated circuits is of great importance for promoting the design optimization of aerospace integrated circuits, improving experimental methods for single event effects, and enhancing the comprehensiveness of aerospace integrated circuit evaluations.…”

Section: Introductionmentioning

confidence: 99%

A Method for Automatically Predicting the Radiation-Induced Vulnerability of Unit Integrated Circuits

Dong,

Lu,

Yang

et al. 2024

Micromachines

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With the rapid development of semiconductor technology, the reduction in device operating voltage and threshold voltage has made integrated circuits more susceptible to the effects of particle radiation. Moreover, as process sizes decrease, the impact of charge sharing effects becomes increasingly severe, with soft errors caused by single event effects becoming one of the main causes of circuit failures. Therefore, the study of sensitivity evaluation methods for integrated circuits is of great significance for promoting the optimization of integrated circuit design, improving single event effect experimental methods, and enhancing the irradiation reliability of integrated circuits. In this paper, we first established a device model for the charge sharing effect and simulated it under reasonable conditions. Based on the simulation results, we then built a neural network model to predict the charge amounts in primary and secondary devices. We also propose a comprehensive automated method for calculating soft errors in unit circuits and validated it through TCAD simulations, achieving an error margin of 2.8–4.3%. This demonstrated the accuracy and effectiveness of the method we propose.

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Single event effects sensitivity of low energy proton in Xilinx Zynq-7010 system-on chip

Cited by 20 publications

References 12 publications

Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

Radiation effects and radiation hardening technology of new microsystems

A Method for Automatically Predicting the Radiation-Induced Vulnerability of Unit Integrated Circuits

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