一种静态随机存储器型现场可编程门阵列的单粒子软错误率的软件分析方法

静态随机存取存储器(SRAM)型现场可编程门阵列(FPGA)在当前空间电子设备中取得了广泛的应用，尽管它对空间辐射引起的单粒子翻转效应极其敏感。在FPGA的配置存储器中发生的单粒子翻转造成的失效机理不同于传统的存储器中的单粒子翻转。因此，如何评价这些单粒子翻转对系统造成的影响就成了一个值得研究的问题。传统的方法主要分为辐照实验和故障注入两种技术途径。本文中提出了一种新的方法，可以用来分析单粒子翻转对构建在FPGA上的系统造成的影响。这种方法基于对FPGA底层结构以及单粒子翻转带来的失效机理的深入理解，从布局布线之后的网表文件出发，寻找所有可能破坏电路结构的关键逻辑节点和路径。然后通过查询可配置资源与相应的配置数据之间关系来确定所有敏感的配置位。我们用加速器辐照实验和传统的故障注入方法验证了这种新方法的有效性。

A software solution to estimate the SEU-induced soft error rate for systems implemented on SRAM-based FPGAs

SRAM-based FPGAs are very susceptible to radiation-induced Single-Event Upsets (SEUs) in space applications. The failure mechanism in FPGA's configuration memory differs from those in traditional memory device. As a result, there is a growing demand for methodologies which could quantitatively evaluate the impact of this effect. Fault injection appears to meet such requirement. In this paper, we propose a new methodology to analyze the soft errors in SRAM-based FPGAs. This method is based on in depth understanding of the device architecture and failure mechanisms induced by configuration upsets. The developed programs read in the placed and routed netlist, search for critical logic nodes and paths that may destroy the circuit topological structure, and then query a database storing the decoded relationship of the configurable resources and corresponding control bit to get the sensitive bits. Accelerator irradiation test and fault injection experiments were carried out to validate this approach.