Xilinx FPGA Counterfeit Issues and a Comprehensive Authentication Solution 

Prof. Alexandro Castellanos and Prof. Stephen Saddow

[Global ETS]

 

Abstract: 

Counterfeit electronics are an extremely serious and common issue in the global systems supply chain which increases the risk of critical system errors and failure which can be life-threatening. Systems affected range from modern mobile devices, computers and laptops, medical diagnostic and treatment systems, air traffic control, and GPS systems. Critical systems have a long-life cycle and often use obsolete ‘legacy’ devices which makes them a target for counterfeit parts. Reproducing legacy parts is both expensive and time-consuming. In addition, using obsolete parts often leads to quality conformance issues even if the part is legitimate since some of the electronics might have been sitting on the shelf for over 20 years.

Some manufacturers create an ID code in the device memory to prevent counterfeit electronics from being inserted into critical systems. This ID code is a serial binary code stored in an un-erasable or unchangeable register. Users must use technical ways such as JTAG, SPI or I2C to find this information. Such actions are usually performed by professional engineers and require extra setup and lead time.

Global’s Advanced Pin Correlation system is an easy and convenient tool for performing quality conformance and counterfeit IC (integrated circuit) detection based on our neural network-based AI/deep learning system. This authentication system first conducts a quick open/short circuit check, a leakage current check, and a supply current check to make sure all readings are within specification. Then it uses a matrix approach to scan from pin to pin to get physical characteristics (impedance-based) which are processed and fed into our deep learning system to train our model, which is capable of producing the corresponding golden chip library.

ICs usually have multiple pins that serve as electrical inputs/outputs and connect to the system through a printed circuit board. Then an automated test and diagnostic system rapidly scan between pins thus forming an ‘electronic signature’ of the device under test (DUT). The automatic test first transfers the scanned data to the diagnostic system and then speculates on the appropriate model to formulate the electronic signature, which is then compared to a known good device (KGD). Consequently, a fast assessment of the authenticity of the part is thus possible.

In addition, using a computer simulation based on gate delays the measured speed is compared to the corresponding simulated value. The outcome is the ability to accurately
determine the speed grade of the FPGA under test to not only determine if it is an OEM part but to ensure that its speed grade is correct. The last feature of the system is the ability to assess the temperature grade of the chip, which is another area of concern regarding counterfeit FPGAs. Thus full-spectrum assessment of FPGAs can be accomplished rapidly and accurately.

Although it is still not considered as functional testing, qualified personnel can perform quick screen testing using this system without a strong electronics background thus saving a lot of time and expense to set up and develop a testing method for microelectronics. Our approach offers two principal benefits:

• Rapid ‘signature-based’ identification of a part to determine its authenticity
• Reduced-skilled operators vs. highly skilled electronics experts both increase measurement costs and slow down a part assessment.

Recently counterfeiters have been replacing FPGA package lids to falsify chip speed. Using scanning acoustic microscopy (C-SAM) we are able to rapidly determine if the package lid has been removed and replaced with a fake lid. This paper discusses both our parametric (AI-based) electronic signature and C-SAM authentication procedures to aid end-use manufacturers in identifying counterfeit parts in the supply chain.

Biography:

Prof. Alexandro Castellanos [Global ETS]

Prof. Castellanos has more than 25 years of experience in higher education and industry with expertise in robotics and embedded systems. Currently consulting for Global ETS in the area of FPGA’s counterfeit detection. Educational background with a Doctor of Philosophy (Ph.D.) focused in MEMS from the University of South Florida and a Postdoc from the Barcelona Supercomputing Center. Strong Collaborations in USA/Latin America/Europe. Member of the Ibero-American Science and Technology Education Consortium. 

 

Prof. Stephen Saddow [Global ETS]

Experienced Professor with a demonstrated history of working in higher education. Skilled in Silicon Carbide Materials and Device Fabrication. Currently working at the nexus of Material Science and Biomedical Engineering to develop innovative solutions for the healthcare industry. Strong educational background with a Doctor of Philosophy (Ph.D.) focused in Electrical Engineering from the University of Maryland College Park and numerous visiting Scientist/Professor stages in Europe and Brasil. A senior member of the IEEE, National Academy of Inventors, and Fellow of the AIMBE.

 

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