Counterfeit integrated circuits (ICs) continue to persist in the supply chain causing early failure in electronics that unknowingly incorporate them. With counterfeiters becoming more adept at replicating ICs, the need for systems and processes to identify counterfeit ICs has been growing in recent years. In this paper, we benchmark the resonant cavity system (ResCav) by evaluating its ability to distinguish ICs with minor circuit variations. A baseline IC group along with 5 variant groups with changes made solely to their die were examined in this paper. Using a supervised machine learning algorithm, the system was able to distinguish every group of ICs amongst each other with an average weighted precision above 90% in every comparison scenario. The system’s ability to distinguish these subtle changes means that it would be suitable when used as a system for counterfeit detection, where the detection of minor deviations is pertinent. This could ultimately lead to the creation of a rapid, precise, and non-destructive system that can screen ICs for conformance.

Due to the continuous outsourcing of printed circuit board (PCB) fabrication, PCB counterfeits and Trojans have increased by a significant margin, and this has necessitated rapid and advanced hardware assurance techniques. PCB Image segmentation is the primary step in PCB assurance. Over the years, few PCB component segmentation methods have been proposed and none of those have provided a definite benchmark of performance. Besides those methods haven’t discussed how the performance is correlated with underlying data or annotation quality. In this work, we present a benchmark on PCB image segmentation along with a high-quality dataset. In addition, we explore how annotation quality affects component segmentation and present possible future research directions to work with coarse annotations to alleviate the human effort behind full data annotation tasks. We have analyzed the performance of the preferred Deep Neural Network (DNN) architecture with the data annotation quality and presented the direction to leverage the outcome with limited quality annotations. Finally, we present the qualitative as well as the quantitative results to demonstrate the performance of our techniques and provide observations and future research directions on the overall task.

This paper discusses the basic physics of scanning acoustic microscopy, the counterfeit features it can detect, and how it compares with other screening methods. Unlike traditional optical inspection and IR and X-ray techniques, SAM can identify recycled and remarked chips by exposing ghost markings, fill material differences, delaminations from excessive handling, and popcorn fractures caused by trapped moisture. The paper presents several examples along with detailed images of these telltale signs of semiconductor counterfeiting. It also discusses the potential of developing an automated solution for detecting counterfeits on a large scale.

PCB assurance currently relies on manual physical inspection, which is time consuming, expensive and prone to error. In this study, we propose a novel automated segmentation algorithm to detect and isolate PCB components called EC-Seg. Component segmentation and localization is a vital preprocessing step in the automation of component identification and authentication as well as the detection of logos and text markings. As test results indicate, EC-Seg is an efficient solution to automate quality assurance toolchains and also aid bill-of-material (BoM) extraction in PCBs. It also has the potential to be used as a region proposal algorithm for object detection networks and to facilitate sensor fusion involving artifact removal in PCB X-ray tomography.

Counterfeiting is an increasing concern for businesses and governments as greater numbers of counterfeit integrated circuits (IC) infiltrate the global market. There is an ongoing effort in experimental and national labs inside the United States to detect and prevent such counterfeits in the most efficient time period. However, there is still a missing piece to automatically detect and properly keep record of detected counterfeit ICs. Here, we introduce a web application database that allows users to share previous examples of counterfeits through an online database and to obtain statistics regarding the prevalence of known defects. We also investigate automated techniques based on image processing and machine learning to detect different physical defects and to determine whether or not an IC is counterfeit.

Bond pull testing, a well-known method in the failure analysis community, is used to evaluate the integrity of an electronic microchip as well as to detect counterfeit ICs. Existing bond pull tests require that the microchip be de-capsulated in order to obtain physical access to the bond wires in the IC package. Bond pull analysis based on simulation and finite element methods also exists but relies on the original model for a bond wire from a CAD design. In this work, we introduce X-ray tomography imaging with 700nm imaging resolution to acquire the 3D geometry details of bond wires non-destructively. Such information can be used to develop more accurate models for finite element analysis based on real size and structure. Therefore, one can test the bond wire strength as a proof of concept for virtual mechanical testing and counterfeit detection in microchips.

It is known by both the commercial and government suppliers, one of the best ways to guarantee the security and reliability of IC's is to image the IC directly using an x-ray microscope. These images can be inspected for many signs of counterfeit electronics. Unfortunately, previous generations of x-ray imaging systems have not kept up with the increasingly sophisticated counterfeiting techniques. Traditional 2D X-ray inspection techniques are becoming inadequate for imaging and verifying features due to the limited resolution of these systems for thick samples and because 2D images contain too many overlapping features to easily discern, making identification very difficult. This paper discusses the development of advanced sample preparation techniques for counterfeit IC detection. It presents information on the limitations of X-ray imaging and 3D tomographic reconstruction, and on the models for resolution configuration improvement.

Printed Circuit Boards (PCBs) are easy target for reverse engineering and counterfeiting attacks due to the distributed supply chain. The integrated circuits (ICs) authentication techniques such as Physically Unclonable Function (PUF) are not easily extendible to PCBs. In this paper, we analyze various sources of variations in PCB and qualitatively study the quality metrics that can be used to quantify the PCB PUFs. We propose several flavors of PCB PUFs by exploiting the manufacture process variations. We also propose a multi-stage arbiter PUF with exponential challenge response pairs. Our preliminary simulations revealed an average 50.4% inter-PCB hamming distance.

One of the biggest problems in the integrated circuit industry as of late has been counterfeiting. Because the counterfeiting process is still quite crude in its nature, visual inspection has become a cornerstone process. This paper discusses the significance of the documentation post decapsulation during internal visual inspection. In order to properly inspect for counterfeit components, it is critical to understand that one inspection method alone will not provide enough information to detect all counterfeits. Proper counterfeit detection involves detailed inspection of multiple aspects of a component in order to gather as much information as possible, so that each component can be successfully labeled counterfeit or authentic. A collaboration of multiple inspection processes, comparison of data amongst sample testing batch, comparison to "known good/golden sample" when possible, and documentation of findings results in the highest confidence level of authenticity of counterfeit labeling of components.

Counterfeit components have been defined as a growing concern in recent years as demand increases for reducing costs. In fact the Department of Commerce has identified a 141% increase in the last three years alone. A counterfeit is any item that is not as it is represented with the intention to deceive its buyer or user. The misrepresentation is often driven by the known presence of defects or other inadequacies in regards to performance. Whether it is used for a commercial, medical or military application, a counterfeit component could cause catastrophic failure at a critical moment. The market for long life electronics, based on commercial off the shelf (COTS) parts, such as those used in medical, military, commercial depot repair, or long term use applications (e.g. street and traffic lights, photovoltaic systems), seems to create a perfect scenario for counterfeiters. With these products, components wear out and need to be replaced long before the overall product fails. The availability of these devices can be derived in many ways. For example, a typical manufacturer may render a component obsolete by changing the design, changing the functionality, or simply discontinuing manufacture. Also, the parts that are available after a design has been discontinued are often distributed by brokers who have very little control over the source or supply. Recycling of devices has also emerged as a means of creating counterfeit devices that are presented as new. And finally, as demand and price increase, the likelihood of counterfeits also increases. This paper will address the four unique sources of counterfeit components and insight into how they occur. Detection methodologies, such as visual inspection, mechanical robustness, X-Ray, XRF, C-SAM, Infrared Thermography, electrical characterization, decapsulation, and marking evaluations, will be compared and contrasted, as well as multiple examples of counterfeit parts identified by DfR.

Counterfeiting of electronic components continues to be an evolving issue that greatly impacts many companies around the globe. And with so many initiatives that have either been enacted or are in the works, there is a significant amount of confusion and even fear in our industry. The financial impact and potential loss of life related to counterfeit components has become so great that an enormous amount of attention is now being devoted to identifying and mitigating this risk by both private industry and governments agencies around the world. Many companies are reaching out to test labs for both the testing of their components and direction on how to address some of these issues. This paper addresses a few of the questions that our lab is getting from some of our customers regarding counterfeit IC’s and how we have answered those questions. The goal is to provide some additional insight to allow others to address this issue proactively and without fear.

The counterfeiting of semiconductor devices has become an important contributor as more components are used in the increasingly sophisticated audio and navigation systems while more suppliers are moving manufacturing plants off-shore. This paper presents a case study on how the authors were able to identify a counterfeit device and certify a replacement source. In this study, failed devices with Intersil CA3080 Operational Transductance Amplifier IC from factory testing and field returns in suspect lot codes were purchased from a second source, due to the unavailability of the obsolete device from the regular first source. The suspect lot codes that were not recognizable by the manufacturer were determined to be counterfeit devices. Many pieces of physical evidence suggested that the failed devices were not consistent with genuine devices directly purchased from the manufacturer.

This abstract provides an introduction to the utility of botanical DNA taggants to provide supply chain security for electronic components and to protect against counterfeiting and diversion. A detailed treatment of the science behind Applied DNA Sciences' botanical DNA technology, its applications to semiconductors and microchips, and an overview of DNA analysis by PCR and CE analysis is provided. In addition, information on the evolution of electronic product counterfeiting and inadequate anti-counterfeiting measures is also provided.

The electronics supply chain is being increasingly infiltrated by non-authentic, counterfeit electronic parts, whose use poses a great risk to the integrity and quality of critical hardware. There is a wide range of counterfeit parts such as leads and body molds. The failure analyst has many tools that can be used to investigate counterfeit parts. The key is to follow an investigative path that makes sense for each scenario. External visual inspection is called for whenever the source of supply is questionable. Other methods include use of solvents, 3D measurement, X-ray fluorescence, C-mode scanning acoustic microscopy, thermal cycle testing, burn-in technique, and electrical testing. Awareness, vigilance, and effective investigations are the best defense against the threat of counterfeit parts.

Counterfeit parts are marketed with the intent to deceive the customer. This intent to deceive defines a counterfeit part and separates it from faulty parts, which have defects that are unknown to the manufacturer or distributor. This paper presents three cases in which counterfeit electronic parts were assembled into hardware items and later found to be faulty in some manner. Laboratory techniques used to identify these parts as counterfeits are presented. Non-laboratory techniques that could have prevented the parts from entering service in these cases are also described. Techniques to combat counterfeit parts range from very simple observation of the parts and the paperwork to failure analysis carried out in a laboratory environment. In all of these cases, the potential existed to detect the counterfeit parts prior to assembly into hardware and prior to field deployment of the defective devices.