Field programmable gate array (FPGA) is a potential hotbed for malicious and counterfeit hardware infiltration. Arbiter-based physical unclonable function (A-PUF) has been widely regarded as a suitable lightweight security primitive for FPGA bitstream encryption and device authentication. Unfortunately, the metastability of flip-flop gives rise to poor A-PUF reliability in FPGA implementation. Its linear additive path delays are also vulnerable to modeling attacks. Most reliability enhancement techniques tend to increase the response predictability and ease machine learning attacks. This paper presents a robust device authentication method based on the FPGA implementation of a reliability enhanced A-PUF with trinary digit (trit) quadruple responses. A two flip-flop arbiter is used to produce a trit for metastability detection. By considering the ordered responses to all four combinations of first and last challenge bits, each quadruple response can be compressed into a quadbit that represents one of the five classes of trit quadruple response with greater reproducibility. This challenge-response quadruple classification not only greatly reduces the burden of error correction at the device but also enables a precise A-PUF model to be built at the server without having to store the complete challenge-response pair (CRP) set for authentication. Besides, the real challenge to the A-PUF is generated internally by a lossy, nonlinear, and irreversible maximum length signature generator at both the server and device sides to prevent the naked CRP from being machine learned by the attacker. The A-PUF with short repetition code of length five has been tested to achieve a reliability of 1.0 over the full operating temperature range of the target FPGA board with lower hardware resource utilization than other modeling attack resilient strong PUFs. The proposed authentication protocol has also been experimentally evaluated to be practically secure against various machine learning attacks including evolutionary strategy covariance matrix adaptation.

Arbiter Physical unclonable function (A-PUF) with exponential number of challenges is an ideal candidate to realize lightweight and robust device authentication in Internet of Things applications. Unfortunately, it is particularly difficult to attain highly reliable responses and increase its modeling attack resistance simultaneously. This paper presents an approach to reduce the vulnerability of A-PUF to machine learning attacks without compromising its high reliability and uniqueness. It utilizes a multiple input signature register (MISR) to process the input challenges. Our experiment results show that the accuracy of predicting the responses of a MISR augmented 128-stage arbiter PUF in FPGA implementation by support vector machine and gradient boosting learning algorithms with a training set of 100,000 challenge-response pairs has reduced drastically from 98% to 50%. If design-for-testability is mandatory, the MISR can be reconfigured from an existing built-in logic block observer, making this approach virtually free. Otherwise, the MISR carries a negligible hardware overhead of only 0.4% of the total available resources in an Xilinx ZC706 FPGA chip.

One main problem encountered in the FPGA implementation of Arbiter based Physical Unclonable Function (A-PUF) is the response instability caused by the metastability of delay flip-flop. This paper presents a new multi-arbiter approach to extract more entropy to extend the number of response bits to a single challenge. New multi-arbiter schemes based on the insertion of either a four-flip-flop arbiter or SR latch arbiter after each pair of multiplexers in the configurable paths are proposed to detect the metastable state when two copies of test pulse arrive at the arbiter inputs almost simultaneously. The detected metastable states are distinguishable by the encoded multiple valued outputs of the arbiter. The codes corresponding to the metastable states collectively form a deterministic ternary state that can be recoded to one of the stable states to improve the uniqueness and reliability of the PUF. Our analysis shows that the proposed design can generate robust and reliable challenge-response pairs with a uniqueness of 0.4982 and a reliability of 0.9985 at the expense of a relatively small FPGA resource overhead.

This paper presents an integrated approach for both built-in online and off-line testing of embedded DRAMs. It is based on a new technique for output data compression which offers the same benefits as signature analysis during off-line test, but also supports efficient online consistency checking. The initial fault-free memory contents are compressed to a reference characteristic and compared to test characteristics periodically. The reference characteristic depends on the memory contents, but unlike similar characteristics based on signature analysis, it can be easily updated concurrently with WRITE operations. This way, changes in memory do not require a time consuming recomputation. The respective test characteristics can be efficiently computed during the periodic refresh operations of the dynamic RAM. Experiments show that the proposed technique significantly reduces the time between the occurrence of an error and its detection. Compared to error detecting codes (EDC) it also achieves a significantly higher error coverage at lower hardware costs. Therefore, it perfectly complements standard online checking approaches relying on EDC, where the concurrent detection of certain types of errors is guaranteed, but only during READ operations accessing the erroneous data.

This paper presents a new technique for on-line consistency checking of embedded DRAMs. The basic idea is to use the periodic refresh operation for concurrently computing a test characteristic of the memory contents and compare it to a precomputed reference characteristic. Experiments show that the proposed technique significantly reduces the time between the occurrence of an error and its detection (error detection latency). It also achieves a very high error coverage at low hardware costs. Therefore it perfectly complements standard on-line checking approaches relying on error detecting codes, where the detection of certain types of errors is guaranteed, but only during READ operations accessing the erroneous data.