  With the expansion of the semiconductor supply chain, the use of recycled
field-programmable gate arrays (FPGAs) has become a serious concern. Several
methods for detecting recycled FPGAs by analyzing the ring oscillator (RO)
frequencies have been proposed; however, most assume the known fresh FPGAs
(KFFs) as the training data in machine-learning-based classification. In this
study, we propose a novel recycled FPGA detection method based on an
unsupervised anomaly detection scheme when there are few or no KFFs available.
As the RO frequencies in the neighboring logic blocks on an FPGA are similar
because of systematic process variation, our method compares the RO frequencies
and does not require KFFs. The proposed method efficiently identifies recycled
FPGAs through outlier detection using direct density ratio estimation.
Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method
successfully distinguishes recycled FPGAs from 35 fresh FPGAs. In contrast, a
conventional recycled FPGA detection method results in certain
misclassification.
