Physical Layout Extraction via Ion Milling based IC Delayering for Reverse Engineering Applications

Reverse engineering using physical inspection has gained prominence in recent times due to its ability to detect Hardware Trojans hiding within the physical layout of integrated circuits (ICs). This requires sophisticated IC delayering, simultaneously imaging each of its layers, and finally matching the corresponding images with the original Graphic Design System (GDS) files. In this study, we aim to meticulously perform IC delayering using broad beam ion milling, focusing on refining process parameters to ensure a smooth, flat surface. Following this, we propose the use of a Swin transformer based Generative Adversarial Network (GAN) to effectively segment out metal lines, vias, and backgrounds within standard noisy electron microscopy images.

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Publication Details

Published
2024-11-12
DOI
https://doi.org/10.1109/paine62042.2024.10792871
Citations
1
Primary Topic
Integrated Circuits and Semiconductor Failure Analysis
Type
article
Field-Weighted Citation Impact
0.21

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Physical Layout Extraction via Ion Milling based IC Delayering for Reverse Engineering Applications

Abhishek Chakraborty, S. Maitra, Tishya Sarma Sarkar, Debdeep Mukhopadhyay
1 citations
Integrated Circuits and Semiconductor Failure Analysis
0.21
article

Physical Layout Extraction via Ion Milling based IC Delayering for Reverse Engineering Applications

Abhishek Chakraborty, S. Maitra, Tishya Sarma Sarkar, Debdeep Mukhopadhyay
article en
1 citations

Abstract

Reverse engineering using physical inspection has gained prominence in recent times due to its ability to detect Hardware Trojans hiding within the physical layout of integrated circuits (ICs). This requires sophisticated IC delayering, simultaneously imaging each of its layers, and finally matching the corresponding images with the original Graphic Design System (GDS) files. In this study, we aim to meticulously perform IC delayering using broad beam ion milling, focusing on refining process parameters to ensure a smooth, flat surface. Following this, we propose the use of a Swin transformer based Generative Adversarial Network (GAN) to effectively segment out metal lines, vias, and backgrounds within standard noisy electron microscopy images.

Indian Institute of Technology Kharagpur (IN)
Research and Development
Openalex Percentile: Top 45%
Integrated Circuits and Semiconductor Failure Analysis
0.21
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