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

Abstract

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.

Publication
IEEE Access
Olivia Dizon-Paradis
Olivia Dizon-Paradis
Graduate Research Assistant

My research interests include artificial intelligence, machine learning, computer vision, and reinforcement learning