AI Article Synopsis
- The study focuses on characterizing Physical Unclonable Functions (PUFs) to evaluate their quality for use in secure hardware systems.
- Characterization is resource-intensive, requiring numerous devices to be tested under various conditions, making the process costly and time-consuming.
- The authors present a dataset from 84 STM32 microcontrollers, featuring SRAM readouts and sensor data, collected using a custom platform that allows for reliability testing and experimentation.
Article Abstract
Large-scale parameter characterization of Physical Unclonable Functions (PUFs) is of paramount importance in order to assess the quality and thus the suitability of such PUFs which would then be developed as an industrial-grade solution for hardware root of trust. Carrying out a proper characterization requires a large number of devices that need to be repeatedly sampled at various conditions. These prerequisites make PUF characterization process a very time-consuming and expensive task. Our work presents a dataset for the study of SRAM-based PUFs on microcontrollers; it includes full SRAM readouts along with internal voltage and temperature sensors of 84 microcontrollers of STM32 type. Data has been gathered with a custom-made and open platform designed for the automatic acquisition of SRAM readouts of such devices. This platform also provides possibilities of experimenting aging and reliability properties.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224988 | PMC |
| http://dx.doi.org/10.1038/s41597-023-02225-9 | DOI Listing |
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