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Deep Learning Secures Chips Against Counterfeits


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(PUFs), which are unique physi- cal systems that are difficult for adversaries to replicate either because of economic constraints or inherent physical properties.” Optical PUFs, which capi-


talize on the distinct optical re- sponses of random media, are es- pecially promising. “However, there are signifi-


cant challenges in achieving scalability and maintaining ac- curate discrimination between adversarial tampering and natu- ral degradation, such as physical aging at higher temperatures, packaging abrasions and humid- ity impact,” Kildishev says. Kildishev and his team drew


inspiration for RAPTOR from the capabilities of deep-learning models.


Creating RAPTOR “RAPTOR is a novel deep-


learning approach, a discrimina- tor that identifies tampering by analyzing gold nanoparticle pat- terns embedded on chips,” says Kildishev. “It is robust under ad- versarial tampering features such as malicious package abra- sions, compromised thermal treatment and adversarial tear- ing.”


Yuheng Chen, a doctoral


student in Kildishev’s group, said RAPTOR uses the distance matrix verification of gold nanoparticles. “The gold nanoparticles are


randomly and uniformly distrib- uted on the chip sample substrate, but their radii are normally dis- tributed. An original database of randomly positioned dark-field


images is created through dark- field microscopy characteriza- tion,” he says. “Gold nanoparticles can easily be measured using dark-field microscopy. This is a readily available technique that can integrate seamlessly into any stage of the semiconductor fabri- cation pipeline.” Blake Wilson, an alumnus of


Kildishev’s group, says, “RAP- TOR uses an attention mecha- nism for prioritizing nanoparti- cle correlations across pre-tamp- er and post-tamper samples be- fore passing them into a residual attention-based deep convolu- tional classifier. It takes nanoparticles in de-


scending order of radii to con- struct the distance matrices and radii from the pre-tamper and post-tamper samples.”


Validation The Purdue team tested


RAPTOR’s counterfeit detection capability by simulating the tampering behavior in nanopar- ticle systems. This included nat- ural changes, malicious adver-


sarial tampering, thermal fluctu- ations and varying degrees of random Gaussian translations of the nanoparticles. “We have proved that RAP-


TOR has the highest average ac- curacy, correctly detecting tam- pering in 97.6% of distance ma- trices under worst-case scenario tampering assumptions,” Wilson says. “This exceeds the perform- ance of the previous methods — Hausdorff, Procrustes and Aver- age Hausdorff distance — by 40.6%, 37.3%, and 6.4%, respec- tively.”


Kildishev says the team is


planning to collaborate with chip-packaging researchers to further innovate the nanoparti- cle embedding process and streamline the authentication steps.


“At the moment, RAPTOR is


a proof of concept that demon- strates AI’s great potential in the semiconductor industry,” he says. “Ultimately, we want to convert it into a mature industry solution.” Web: www.purdue.edu r


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Continued from page 1


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