Corrigendum to: A Systematic Study of DDR4 DRAM Faults in the Field
Majed Valad Beigi, Yi Cao, Sudhanva Gurumurthi, Charles Recchia,, Andrew Walton, Vilas Sridharan

TL;DR
This corrigendum corrects decoding errors in a previous study of DDR4 DRAM faults, leading to revised fault classifications and updated statistical results, thereby refining the original analysis.
Contribution
It identifies and corrects a bug in fault decoding, providing more accurate fault classification and updated data analysis in DDR4 DRAM field fault study.
Findings
Revised fault classification with more accurate single-bit fault counts
Updated failure rate metrics (FIT) for DDR4 DRAM devices
Clarified the distribution of fault modes after correction
Abstract
This paper is a corrigendum to the paper by Beigi et al. published at HPCA 2023 https://doi.org/10.1109/HPCA56546.2023.10071066. The HPCA paper presented a detailed field data analysis of faults observed at scale in DDR4 DRAM from two different memory vendors. This analysis included a breakdown of fault patterns or modes. Upon further study of the data, we found a bug in how we decoded errors based on the logged row-bank-column address. Specifically, we found that some errors that occurred in one column were mis-interpreted as occurring in two non-adjacent columns. As a result of this, some single-bit faults were misclassified as partial-row faults (i.e., two-bit faults). Similarly, some single-column faults were misclassified as two-column faults. The result of these misclassification errors is that the proportion of single-bit faults is higher than reported in the paper, with a…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIntegrated Circuits and Semiconductor Failure Analysis · VLSI and Analog Circuit Testing · Industrial Vision Systems and Defect Detection
