Counterfeit Integrated Circuits Research Articles

AutoDetect: Novel Autoencoding Architecture for Counterfeit IC Detection

AbstractThe global electronic supply chain industry is a complex and interconnected network of companies, organizations, and individuals that collaborate to produce and distribute electronic devices and components. It plays a critical role in the global economy, as electronic devices have become an essential part of modern life. Electronic components and devices are produced and distributed across the world, with different regions and countries specializing in different aspects of the supply chain. This global nature of the electronic supply chain industry also poses challenges, particularly in terms of quality control and supply chain transparency involving counterfeit components owing to its interconnected nature. Counterfeiting of integrated circuits (ICs) and semiconductor devices is a significant challenge that poses threats to the safety and reliability of electronic devices, the global economy, intellectual property rights, and the overall sustainability of the electronic supply chain industry. To address this challenge, we propose a novel autoencoding architecture for counterfeit IC detection using a labeled database. The proposed architecture achieves an overall accuracy of 83% which is $$\approx$$ ≈ 20% more than the existing transfer learning approaches.

An Implementation of Trust Chain Framework with Hierarchical Content Identifier Mechanism by Using Blockchain Technology.

Advances in information technology (IT) and operation technology (OT) accelerate the development of manufacturing systems (MS) consisting of integrated circuits (ICs), modules, and systems, toward Industry 4.0. However, the existing MS does not support comprehensive identity forensics for the whole system, limiting its ability to adapt to equipment authentication difficulties. Furthermore, the development of trust imposed during their crosswise collaborations with suppliers and other manufacturers in the supply chain is poorly maintained. In this paper, a trust chain framework with a comprehensive identification mechanism is implemented for the designed MS system, which is based and created on the private blockchain in conjunction with decentralized database systems to boost the flexibility, traceability, and identification of the IC-module-system. Practical implementations are developed using a functional prototype. First, the decentralized application (DApp) and the smart contracts are proposed for constructing the new trust chain under the proposed comprehensive identification mechanism by using blockchain technology. In addition, the blockchain addresses of IC, module, and system are automatically registered to InterPlanetary File System (IPFS), individually. In addition, their corresponding hierarchical CID (content identifier) values are organized by using Merkle DAG (Directed Acyclic Graph), which is employed via the hierarchical content identifier mechanism (HCIDM) proposed in this paper. Based on insights obtained from this analysis, the trust chain based on HCIDM can be applied to any MS system, for example, this trust chain could be used to prevent the counterfeit modules and ICs employed in the monitoring system of a semiconductor factory environment. The evaluation results show that the proposed scheme could work in practice under the much lower costs, compared to the public blockchain, with a total cost of 0.002094 Ether. Finally, this research is developed an innovation trust chain mechanism that could be provided the system-level security for any MS toward Industrial 4.0 in order to meet the requirements of both manufacturing innovation and product innovation in Sustainable Development Goals (SDGs).

A Novel Authentication Methodology to Detect Counterfeit PCB Using PCB Trace-Based Ring Oscillator

The existence of counterfeit products, e.g., integrated circuits (ICs) and printed circuit boards (PCBs), in the modern semiconductor supply chain has seriously jeopardized the security and reliability of electronic systems, and has also caused the loss of suppliers’ profit and reputation. Most of existing research papers prevent or detect counterfeit IC and PCB substrate separately, without testing the PCB as a whole, and often require the assistance of external equipment. In this article, a novel ring oscillator-based PCB authentication (ROPA) methodology to detect counterfeit PCB through supply chain is proposed, which utilizes PCB trace-based ring oscillators (PTRO) assisted by on-chip ring oscillators, and a novel PCB signature extraction methodology. By switching the PCB to different load modes, the signature can reflect the process variations in PCB traces and overall impedance. The ROPA can provide both IC and PCB authentication independently of external equipments, and allows remote authentication for the user. The ROPA structure has shown advantageous area (0.301% on average) and power (0.355% on average) overhead when implemented on a number of benchmarks. Then the ROPA is implemented on a set of authentic and counterfeit FPGA development boards to verify the effectiveness on counterfeit detection. The results show that the proposed method provides 96.7% confidence in detecting tampered PCBs and 100% confidence in detecting overproduced, recycled, and cloned PCBs.

Automated Defective Pin Detection for Recycled Microelectronics Identification

Counterfeit integrated circuits (ICs) constitute a major threat to system reliability, as well as security from personal to national scale. Most current techniques for counterfeit electronics detection are expensive, time consuming, and prone to human error. We have proposed a convolutional neural network (CNN) architecture–based supervised technique along with two unsupervised techniques based on depth map and texture of pins to identify bent and corroded pins respectively with high accuracy, thus helping to identify recycled ICs. The supervised technique requires one-time training, and is amenable to be integrated in a fast and automated counterfeit IC detection methodology. We have also compared the proposed CNN–based classification technique accuracy with support vector machine (SVM) and K-nearest neighbor (KNN)–based classification techniques. Using these methods, both corroded and bent pins are differentiated with high accuracy. The unsupervised bent pin detection technique uses depth map images of ICs to construct 3D images of ICs and the corroded pin detection methodology uses Laws’ texture energy method and K-means clustering to differentiate between defective and non-defective pins.

Recycled and Remarked Counterfeit Integrated Circuit Detection by Image-Processing-Based Package Texture and Indent Analysis

Wide proliferation of counterfeit integrated circuits (ICs) is a major global threat. Currently, the process of counterfeit IC detection is time-consuming and requires highly skilled subject matter experts. In this paper, we have developed an automated image-processing-based methodology for recycled and remarked counterfeit IC detection, using images acquired through an ordinary optical microscope or a digital camera. The methodology has two phases: first, identification of counterfeit IC is attempted by package texture comparison of a golden IC sample and the given sample. Then, for the ICs which have not been inferred to be counterfeit, an optional second phase where detection of position and size of indents (or cavities) on the IC package surface is performed. Compared to previously proposed techniques, the proposed technique is less computationally expensive and avoids expensive equipment such as a scanning electron microscope or X-ray tomograph. Experimental results demonstrate that the proposed methodology achieves high detection accuracy, and the results are supported by an unsupervised clustering approach.

Detection of Counterfeit ICs Using Public Identification Sequences and Side-Channel Leakage

In this paper, we present a new approach for counterfeit protection against remarked, overproduced, and out-of-spec integrated circuits (ICs). Our approach uses identification sequences that are regularly published by the original chip manufacturer and hidden in the electromagnetic (EM) leakage of authentic chips. A portion of the chip area is dedicated to a crypto engine running in stream cipher mode that is initialized with a unique key and an initialization vector stored in one-time-programmable antifuse memory. IC identification is carried out in the field by obtaining EM measurements of deployed ICs and by proving the presence of the genuine identification sequences in the measurements. We evaluate our approach using a proof-of-concept implementation on three FPGA boards by capturing EM leakage of the FPGAs under test at their decoupling capacitors. The results show that the user can carry out IC identification on-site, using standard lab equipment in short amount of time.

Detection of counterfeited ICs via on-chip sensor and post-fabrication authentication policy

Counterfeiting of integrated circuits (ICs) has become an increasingly vital concern for the security of commercial and mission-critical systems. Moreover, they pose an immense economic, security, and safety threat. We propose a comprehensive detection and prevention framework consisting of a multi-functional on-chip aging sensor, and post-fabrication authentication methodology. This framework targets several classes of counterfeit ICs, such as recycled, remarked, out-of-spec, cloned, and over-produced ICs. First, the new sensor consists of both antifuse memory and aging sensors. To reduce reference-circuit related area-overhead, the initial electronic properties of sensor circuits are stored in a global database, accessed by unique chip via challenge-response pairs. Second, this work consists of a two aging-sensor approach, based on IC wear-out effects, using a recently proposed electromigration (EM) aging sensor and a ring oscillator aging sensor. This method can be effective for chip usage estimation of both short and long time periods. Hence, it can serve as a more accurate timer for the chip to meter the long term usage, which can allow for timed services of some functionality of a chip, in addition to detection of the recycled/remark ICs. Third, on top of the new sensor, we propose a new post-fabrication authentication methodology to detect and prevent non-defective counterfeit ICs. All fabricated ICs will be registered in a global database and activated with a unique chip ID, which is written into the antifuse memory. Simulation results show that the combined aging sensors have a high degree of accuracy when compared to traditional on-chip sensors.

CDTA

The Internet of Things (IoT) is transforming the way we live and work by increasing the connectedness of people and things on a scale that was once unimaginable. However, the vulnerabilities in the IoT supply chain have raised serious concerns about the security and trustworthiness of IoT devices and components within them. Testing for device provenance, detection of counterfeit integrated circuits (ICs) and systems, and traceability of IoT devices are challenging issues to address. In this article, we develop a novel radio-frequency identification (RFID)-based system suitable for counterfeit detection, traceability, and authentication in the IoT supply chain called CDTA . CDTA is composed of different types of on-chip sensors and in-system structures that collect necessary information to detect multiple counterfeit IC types (recycled, cloned, etc.), track and trace IoT devices, and verify the overall system authenticity. Central to CDTA is an RFID tag employed as storage and a channel to read the information from different types of chips on the printed circuit board (PCB) in both power-on and power-off scenarios. CDTA sensor data can also be sent to the remote server for authentication via an encrypted Ethernet channel when the IoT device is deployed in the field. A novel board ID generator is implemented by combining outputs of physical unclonable functions (PUFs) embedded in the RFID tag and different chips on the PCB. A light-weight RFID protocol is proposed to enable mutual authentication between RFID readers and tags. We also implement a secure interchip communication on the PCB. Simulations and experimental results using Spartan 3E FPGAs demonstrate the effectiveness of this system. The efficiency of the radio-frequency (RF) communication has also been verified via a PCB prototype with a printed slot antenna.

Counterfeit Electronics Detection Using Image Processing and Machine Learning

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.

P-Val: Antifuse-Based Package-Level Defense Against Counterfeit ICs

The rapidly growing incidences of counterfeit integrated circuits (ICs) pose a significant threat to the semiconductor industry. These ICs may suffer from functional, performance, or reliability issues and can affect chip manufacturers, system designers as well as end users. The standard chip/package level structural and functional tests are often inadequate in detecting various forms of counterfeit ICs. Moreover, existing design for security approaches are usually not attractive due to additional design modifications, hardware overhead, test cost, and inadequate robustness. In this paper, we propose a novel, low-overhead package-level IC integrity validation approach, referred to as P-Val, for unified protection against two primary forms of counterfeiting attacks: 1) recycling and 2) cloning. Protection against recycled/remarked chips is achieved through a unique active defense that inserts antifuses (AFs) (one-time programmable) to few select pins inside the package. It effectively disables the functionality or “locks” these pins, which need to be programmed before first-time use to make a chip functional in a system. To protect against cloned ICs, intrinsic random variations in programmed resistances of AFs connected to some of the remaining IC pins are exploited to create unique chip-specific signatures for authentication. P-Val requires no die-level design modifications and remains effective for legacy designs. Moreover, we show that it is effective for small chips with just few pins including analog ICs, where common authentication approaches fail to work. We discuss optimal choice of AF structure and program parameters; their integration in IC packages; and the signature generation/verification process. Through mathematical analysis and simulation results, we demonstrate that the proposed mechanism provides high level of protection against counterfeiting attacks at ultralow overhead (<0.05% package area).

SeMIA: Self-Similarity-Based IC Integrity Analysis

Counterfeit chips in the supply chain as well as hardware Trojan (HT) attacks pose serious threats to the semiconductor industry. If undetected before deployment, they can lead to serious consequences including system performance/reliability issues during field operation and potential revenue/reputation loss for a trusted manufacturer. Currently, no unified detection method is available that can simultaneously address these integrity violations in integrated circuits (ICs). In addition, most existing detection approaches require a set of golden chips as a reference, which significantly increases the test cost and complexity. Furthermore, in some scenarios, it may be extremely difficult to obtain golden chips. In this paper, we present a novel unified IC integrity analysis approach that can effectively detect both recycled counterfeit ICs (the most dominant form of counterfeiting) as well as Trojan attacks in ICs without the need of golden chips. The proposed approach, referred to as self-similarity-based microchip integrity analysis (SeMIA), exploits intrinsic structural self-similarity in a design (e.g., multiple cores, multiple functional units of the same type, different parts of an adder) to isolate recycled chips and HT attacks under large inter- and intra-die process variations. It compares dynamic current ( ${I} _{\text {DDT}}$ ) signatures between two adjacent similar circuit structures using an appropriate isolation metric to detect such attacks with high degree of confidence. SeMIA does not rely on any embedded structure for authentication, thus it comes at virtually zero hardware overhead and can be applied to chips already produced. Through extensive simulations, we show that for 15% inter- and 10% intra-die variations in threshold voltage for a 45 nm CMOS process, over 98% of recycled chips can be reliably identified. Finally, experimental measurements on field programmable gate array chips demonstrate effectiveness of SeMIA for protection against both attacks.

Detection of counterfeit electronic components through ambient mass spectrometry and chemometrics.

In the last several years, illicit electronic components have been discovered in the inventories of several distributors and even installed in commercial and military products. Illicit or counterfeit electronic components include a broad category of devices that can range from the correct unit with a more recent date code to lower-specification or non-working systems with altered names, manufacturers and date codes. Current methodologies for identification of counterfeit electronics rely on visual microscopy by expert users and, while effective, are very time-consuming. Here, a plasma-based ambient desorption/ionization source, the flowing atmospheric pressure afterglow (FAPA) is used to generate a mass-spectral fingerprint from the surface of a variety of discrete electronic integrated circuits (ICs). Chemometric methods, specifically principal component analysis (PCA) and the bootstrapped error-adjusted single-sample technique (BEAST), are used successfully to differentiate between genuine and counterfeit ICs. In addition, chemical and physical surface-removal techniques are explored and suggest which surface-altering techniques were utilized by counterfeiters.