SECURE: A Segmentation Quality Evaluation Metric on SEM Images for Reverse Engineering on Integrated Circuits

Comprehensive hardware assurance and failure analysis methods require exhaustive validation of the design layout through post-silicon imaging. Overlooked segmentation errors in such images cause significant inflation of resource requirements, both in terms of human resources and process time frame,...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.137798-137809
Main Authors: Wilson, Ronald, Dizon-Paradis, Olivia P., Forte, Domenic, Woodard, Damon L.
Format: Article
Language:English
Subjects:
Assurance
Errors
Failure analysis
Hardware
Hardware assurance
Image analysis
Image quality
Image segmentation
Integrated circuits
Layout
Layouts
Measurement
metric
Quality assessment
Quality assurance
Reverse engineering
segmentation
SEM
Shape
unsupervised
Unsupervised learning
Workflow
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Comprehensive hardware assurance and failure analysis methods require exhaustive validation of the design layout through post-silicon imaging. Overlooked segmentation errors in such images cause significant inflation of resource requirements, both in terms of human resources and process time frame, for fault isolation and debugging in the hardware assurance process. Further, due to their lack of contextual awareness, the existing segmentation measures lack the ability to detect and suppress segmentation errors. In this paper, we introduce the first reference-less context-aware segmentation quality evaluation metric for scanning electron microscopy images, called Structural Equivalency and Connection Uniformity for Reverse Engineering (SECURE), to alleviate this issue by capturing an implicit understanding of design layout from weakly labeled unpaired images. The proposed metric can be seamlessly integrated into the design layout validation workflow for in-line rejection or correction of corrupted segmented images preventing the corrupted data from compromising the reliability of the hardware assurance process while simultaneously supporting process automation. Exhaustive qualitative and quantitative validation is provided to support the efficacy of the metric.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3340618