Shayan Taheri

Md Mahfuz Al Hasan, Mohammad Tahsin Mostafiz, Thomas An Le et al.

Due to the ever-growing demands for electronic chips in different sectors the semiconductor companies have been mandated to offshore their manufacturing processes. This unwanted matter has made security and trustworthiness of their fabricated chips concerning and caused creation of hardware attacks. In this condition, different entities in the semiconductor supply chain can act maliciously and execute an attack on the design computing layers, from devices to systems. Our attack is a hardware Trojan that is inserted during mask generation/fabrication in an untrusted foundry. The Trojan leaves a footprint in the fabricated through addition, deletion, or change of design cells. In order to tackle this problem, we propose Explainable Vision System for Hardware Testing and Assurance (EVHA) in this work that can detect the smallest possible change to a design in a low-cost, accurate, and fast manner. The inputs to this system are Scanning Electron Microscopy (SEM) images acquired from the Integrated Circuits (ICs) under examination. The system output is determination of IC status in terms of having any defect and/or hardware Trojan through addition, deletion, or change in the design cells at the cell-level. This article provides an overview on the design, development, implementation, and analysis of our defense system.

Wenwei Zhao, Suprith Gurudu, Shayan Taheri et al.

Printed Circuit Board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods, however they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduce the number of trainable ML parameters and thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection using semantic data. Results of this study indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities.

Shayan Taheri, Jiann-Shiun Yuan

Security and energy are considered as the most important parameters for designing and building a computing system nowadays. Today's attacks target different layers of the computing system (i.e. software and hardware). Introduction of new transistor technologies to the integrated circuits design is beneficial, especially for low energy requirements. The new devices have unique features and properties that provide security advantages. However, these properties may come to the aid of an adversary to design stronger attacks. Therefore, the advantages as well as the disadvantages of these technologies need to be well studied. This paper demonstrates the area and power efficiency of the tunnel field-effect transistor (TFET) technology along with analyzing its security aspects.