This is a tiny (less than 200 LUTs) Physical Unclonable Function hardware module for FPGAs. It provides a unique 96-bit "fingerprint" that can be used for security-related applications like key-generation or authentication.
The fingerprint is defined by the FPGA's semiconductor characteristics. The same bitstream will lead to different fingerprints when programmed to different FPGAs of the same type.
The whole design is written in platform-independent VHDL. No device-specific macros, primitives or attributes are used at all so the design can be implemented on any FPGA (tested on Intel, Lattice and (in-progress) Xilinx).
(the "platform-agnostic" concept/technique was taken from the NEPORV32 TRNG -> https://github.com/stnolting/neorv32)
A few bits of the raw fingerprint from the module are quite noisy, so a software post-processing is required. I have implemented a simple "averaging" mechanism here. Error correction codes might be much better - but I am still fighting with the theory behind them ;)
I have tested the design on several FPGAs with promising results (see GitHub page). However, I still need to do more long-time testing to ensure stability of the fingerprint.
The fingerprint is defined by the FPGA's semiconductor characteristics. The same bitstream will lead to different fingerprints when programmed to different FPGAs of the same type.
The whole design is written in platform-independent VHDL. No device-specific macros, primitives or attributes are used at all so the design can be implemented on any FPGA (tested on Intel, Lattice and (in-progress) Xilinx). (the "platform-agnostic" concept/technique was taken from the NEPORV32 TRNG -> https://github.com/stnolting/neorv32)
A few bits of the raw fingerprint from the module are quite noisy, so a software post-processing is required. I have implemented a simple "averaging" mechanism here. Error correction codes might be much better - but I am still fighting with the theory behind them ;)
I have tested the design on several FPGAs with promising results (see GitHub page). However, I still need to do more long-time testing to ensure stability of the fingerprint.
Feedback is highly appreciated!