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.
The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions, compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR (residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-of-the-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and 6.4% total accuracy increase, respectively.
Integrating machine learning into the inverse design, fabrication, and characterization of photonic devices brings computational speed-ups throughout the entire device design process. In this presentation, we report on using machine learning for improving the inverse design of nanophotonic meta-structures and speeding up the characterization of single-photon emitters under sparse measurements. We find that compressing the design space of meta-structures using autoencoders can greatly reduce the time required to compute high-efficiency designs using global optimization methods. With the characterization of single-photon emitters, we find that convolutional neural networks can classify the g2 correlation function of an emitter above or below the 0.5 correlation threshold up to 100 times faster than full characterization with 95% accuracy. Using this fast characterization of single-photon emitters, we can speed up super-resolution imaging up to 12 times faster than conventional methods. Our work paves the way for quantum machine learning-assisted global optimization of nanostructures and super-resolution imaging.
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