Detection of ESD-Induced Soft Failures by Analyzing Linux Kernel Function Calls
Abstract
Electrostatic discharge (ESD) into a functioning system can cause temporary upsets - soft failures. Subtle soft failures can reduce the reliability of system and cannot be detected by conventional methods based on external equipment or operation system (OS) log. This paper presents a proof of concept for a novel methodology for detection of ESD-induced soft failures through analyzing kernel function trace recordings of the operation system. The method is based on recording Linux kernel function calls during normal operation and after ESD stress injection. The recorded information is visualized in forms of graphical maps of function execution and system call distribution for each process to highlight ESD induced changes. The experimental data shows that soft failures manifest themselves as changes in the function maps and the call distribution within the observed processes. This novel method is capable of detecting subtle system upsets which are not observable for the user through standard I/O or attached equipment.
Recommended Citation
X. Liu et al., "Detection of ESD-Induced Soft Failures by Analyzing Linux Kernel Function Calls," IEEE Transactions on Device and Materials Reliability, vol. 20, no. 1, pp. 128 - 135, Institute of Electrical and Electronics Engineers (IEEE), Mar 2020.
The definitive version is available at https://doi.org/10.1109/TDMR.2020.2965205
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Call-Trace Pattern; Earth Mover's Distance (EMD); Electrostatic Discharge; GNU/Linux Operating System; Kernel Function; Power Law Distribution; Soft Failure
International Standard Serial Number (ISSN)
1530-4388
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Mar 2020
Comments
This work was supported in part by the U.S. National Science Foundation under Grant IIP-1440110.