Enhancing Supply Chain Security: ML Assisted Manufacturer Identification of SRAM PUFs

The widespread manufacturing of memory chips at untrustworthy facilities has led to the entry of fake memory chips into the supply chain. Identification of these counterfeit chips is crucial because it poses substantial concerns about the security of electronic systems. Current approaches generally...

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Bibliographic Details
Main Authors: Singh, Harshdeep, Amsaad, Fathi
Format: Conference Proceeding
Language:English
Subjects:
Classification Models
Computational modeling
Machine Learning
Manufacturer Classification
Random Forest
Semiconductor device manufacture
SRAM chips
SRAM PUF
Supply Chain Security
Supply chains
Support vector machines
SVM
Systems operation
Very large scale integration
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Summary:The widespread manufacturing of memory chips at untrustworthy facilities has led to the entry of fake memory chips into the supply chain. Identification of these counterfeit chips is crucial because it poses substantial concerns about the security of electronic systems. Current approaches generally focus on identifying specific kinds of counterfeiting, perhaps neglecting underlying flaws that develop throughout system operation. This paper uses process variations of the manufacturer, allowing machine learning models to identify differences in manufactur-ers without complex procedures. The technique's usefulness is demonstrated through validation utilizing 345 SRAM chips from five reputable manufacturers. It results in F1 ratings of 0.98 for accurately identifying the device manufacturer, respectively.
ISSN:2159-3477
DOI:10.1109/ISVLSI61997.2024.00161