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AI-powered optical detection to thwart counterfeit chips

AI-powered optical detection to thwart counterfeit chips

Technology News |
By Nick Flaherty



Researchers in the US have developed a robust optical anticounterfeit method for semiconductor devices using an AI framework.

Counterfeit chips are an increasing problem in the semiconductor supply chain, valued at over $75bn. Researchers at Purdue University have proposed an optical anti-counterfeit detection method that uses AI to detect the difference between aging and tampering.

The Residual, Attention-based Processing of Tampered Optical Responses (RAPTOR) technique identifies tampering by analyzing gold nanoparticle patterns embedded on chips.

Several techniques aimed at ensuring semiconductor authenticity have been used to detect counterfeit chips, often with physical security tags baked into the chip functionality or packaging. Central to many of these methods are physical unclonable functions (PUFs), which are unique physical systems that are difficult to replicate either because of economic constraints or inherent physical properties.

Optical PUFs use the distinct optical responses of random media and are easy to fabricate and quick to measure, making them ideal for proof-of-concept tampering identification experiments. Nano-scale metallic optical systems have especially been rising in popularity due to their strong scattering response at optical wavelengths, increasing robustness during post-tampering measurements.

However, achieving scalability and maintaining accurate discrimination between adversarial tampering and natural degradation, such as physical aging at higher temperatures, packaging abrasions, and humidity impact, pose significant challenges.

The optical PUF technique is robust under adversarial tampering features such as malicious package abrasions, compromised thermal treatment, and adversarial tearing.

The team first built a 10,000-image dataset of randomly distributed gold nanoparticles by augmenting original images from the dark-field microscope. Next, with nanoparticle pattern pixel regions clustered into local particle patterns, their centres of mass are extracted. Finally, the Distance matrix PUFs are generated by evaluating all pairwise distances between these nanoparticle patterns.

To test anticounterfeit capabilities, tampering behaviour in nanoparticle PUFs was simulated, considering both natural changes and malicious adversarial tampering. RAPTORprioritizes nanoparticle correlations across pre-tamper and post-tamper samples before feeding them into a convolutional classifier.

RAPTOR demonstrated the highest accuracy, correctly detecting tampering in 97.6 percent of distance matrices under worst-case tampering scenarios, outperforming previous methods.

Authentication through residual attention-based processing of tampered optical responses

www.purdue.edu

 

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