Machine-learning-assisted optical authentication of chip tampering

The chip industry grapples with a chip shortage and a $75 billion influx of counterfeit chips, risking malfunctions and surveillance. Several techniques to authenticate semiconductors have been introduced to detect counterfeit chips, including using physical security tags integrated into chip functionality or packaging. Among them, physical unclonable functions (PUFs), are known as unique and challenging to replicate. However, some PUFs face verification robustness challenges. We introduce a statistical and fabrication method for semiconductor device packaging that is resilient to adversarial tampering. Our innovative deep-learning approach uses a residual, attention-based discriminator to identify tampering in an optical anti-counterfeit PUF, with a random array of gold nanoparticles embedded in the package. Authentication is swiftly achieved in 80ms with 97.6% accuracy, even in challenging adversarial tampering conditions, and our approach demonstrates substantial improvements in total accuracy compared to state-of-the-art metrics. We also propose to map our concept onto a photonic neuromorphic preprocessor. Such a transition offers significant speedup and additional security.