Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring

Understanding human behavior and monitoring mental health are essential to maintaining the community and society's safety. As there has been an increase in mental health problems during the COVID-19 pandemic due to uncontrolled mental health, early detection of mental issues is crucial. Nowadays, the usage of Intelligent Virtual Personal Assistants (IVA) has increased worldwide. Individuals use their voices to control these devices to fulfill requests and acquire different services. This paper proposes a novel deep learning model based on the gated recurrent neural network and convolution neural network to understand human emotion from speech to improve their IVA services and monitor their mental health.Comment: 6 pages, 5 figures, 3 tables, accepted in the IEEE WFIoT202

In-Situ Thickness Measurement of Die Silicon Using Voltage Imaging for Hardware Assurance

Hardware assurance of electronics is a challenging task and is of great interest to the government and the electronics industry. Physical inspection-based methods such as reverse engineering (RE) and Trojan scanning (TS) play an important role in hardware assurance. Therefore, there is a growing demand for automation in RE and TS. Many state-of-the-art physical inspection methods incorporate an iterative imaging and delayering workflow. In practice, uniform delayering can be challenging if the thickness of the initial layer of material is non-uniform. Moreover, this non-uniformity can reoccur at any stage during delayering and must be corrected. Therefore, it is critical to evaluate the thickness of the layers to be removed in a real-time fashion. Our proposed method uses electron beam voltage imaging, image processing, and Monte Carlo simulation to measure the thickness of remaining silicon to guide a uniform delayering processComment: 5 pages, 10 figures, Government Microcircuit Applications & Critical Technology Conference (GOMACTech) 202

Framework for Automatic PCB Marking Detection and Recognition for Hardware Assurance

A Bill of Materials (BoM) is a list of all components on a printed circuit board (PCB). Since BoMs are useful for hardware assurance, automatic BoM extraction (AutoBoM) is of great interest to the government and electronics industry. To achieve a high-accuracy AutoBoM process, domain knowledge of PCB text and logos must be utilized. In this study, we discuss the challenges associated with automatic PCB marking extraction and propose 1) a plan for collecting salient PCB marking data, and 2) a framework for incorporating this data for automatic PCB assurance. Given the proposed dataset plan and framework, subsequent future work, implications, and open research possibilities are detailed.Comment: 5 pages, 3 figures, Government Microcircuit Applications & Critical Technology Conference (GOMACTech) 202

FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection

Over the years, computer vision and machine learn- ing disciplines have considerably advanced the field of automated visual inspection for Printed Circuit Board (PCB-AVI) assurance. However, in practice, the capabilities and limitations of these advancements remain unknown because there are few publicly accessible datasets for PCB visual inspection and even fewer that contain images that simulate realistic application scenarios. To address this need, we propose a publicly available dataset, “FICS-PCB”, to facilitate the development of robust methods for PCB-AVI. The proposed dataset includes challenging cases from three variable aspects: illumination, image scale, and image sensor. This dataset consists of 9,912 images of 31 PCB samples and contains 77,347 annotated components. This paper reviews the existing datasets and methodologies used for PCB- AVI, discusses challenges, describes the proposed dataset, and presents baseline performances using feature engineering and deep learning methods for PCB component classification

FICS PCB X-ray: A dataset for automated printed circuit board inter-layers inspection

Advancements in computer vision and machine learning breakthroughs over the years have paved the way for automated X-ray inspection (AXI) of printed circuit boards (PCBs). However, there is no standard dataset to verify the capabilities and limitations of such advancements in practice due to the lack of publicly available datasets for PCB X-ray inspection. Furthermore, there is a lack of diverse PCB X-ray datasets that encompass images from X-ray Computed Tomography (CT). To address the lack of data, we developed the first comprehensive publicly available dataset, FICS PCB X-ray, to aid in the development of robust PCB-AXI methodologies. The dataset consists of diverse images from the tomographic image domain, along with challenging cases of unaligned, raw X-ray data of PCBs. Further, the dataset contains projection data and the reconstructed volume which is converted into a Tiff stack. Annotated X-ray layer images are also available for image processing and machine learning tasks. This paper summarizes the existing databases and their limitations, and proposes a new dataset, FICS PCB X-ray\u27\u27

US Microelectronics Packaging Ecosystem: Challenges and Opportunities

The semiconductor industry is experiencing a significant shift from traditional methods of shrinking devices and reducing costs. Chip designers actively seek new technological solutions to enhance cost-effectiveness while incorporating more features into the silicon footprint. One promising approach is Heterogeneous Integration (HI), which involves advanced packaging techniques to integrate independently designed and manufactured components using the most suitable process technology. However, adopting HI introduces design and security challenges. To enable HI, research and development of advanced packaging is crucial. The existing research raises the possible security threats in the advanced packaging supply chain, as most of the Outsourced Semiconductor Assembly and Test (OSAT) facilities/vendors are offshore. To deal with the increasing demand for semiconductors and to ensure a secure semiconductor supply chain, there are sizable efforts from the United States (US) government to bring semiconductor fabrication facilities onshore. However, the US-based advanced packaging capabilities must also be ramped up to fully realize the vision of establishing a secure, efficient, resilient semiconductor supply chain. Our effort was motivated to identify the possible bottlenecks and weak links in the advanced packaging supply chain based in the US.Comment: 22 pages, 8 figure

ToSHI - Towards Secure Heterogeneous Integration: Security Risks, Threat Assessment, and Assurance

The semiconductor industry is entering a new age in which device scaling and cost reduction will no longer follow the decades-long pattern. Packing more transistors on a monolithic IC at each node becomes more difficult and expensive. Companies in the semiconductor industry are increasingly seeking technological solutions to close the gap and enhance cost-performance while providing more functionality through integration. Putting all of the operations on a single chip (known as a system on a chip, or SoC) presents several issues, including increased prices and greater design complexity. Heterogeneous integration (HI), which uses advanced packaging technology to merge components that might be designed and manufactured independently using the best process technology, is an attractive alternative. However, although the industry is motivated to move towards HI, many design and security challenges must be addressed. This paper presents a three-tier security approach for secure heterogeneous integration by investigating supply chain security risks, threats, and vulnerabilities at the chiplet, interposer, and system-in-package levels. Furthermore, various possible trust validation methods and attack mitigation were proposed for every level of heterogeneous integration. Finally, we shared our vision as a roadmap toward developing security solutions for a secure heterogeneous integration