This paper presents a hardware-reuse strong physical unclonable function (PUF) based on the intrinsic transfer paths (TPs) of a conventional digital multiplier to achieve a strong modeling attack resilience. With the multiplier input employed as the PUF challenge and the path delay as the entropy source, all the possible valid propagation paths from distinct input/output pairs can serve as PUF primitives. We can quantize the path delay using a time-to-digital converter (TDC), and select the suitable TDC output bits as the PUF response. We further propose a lightweight dynamic obfuscation algorithm (DOA) and a secure mutual authentication protocol to counteract modeling attacks. The proposed strong PUF using a 32×32 multiplier as implemented in the Xilinx ZYNQ-7000 SoC features a total of 2048 intrinsic PUF primitives, while achieving a response stream (RS) with an average of 1024 responses per TDC output bit per challenge. WithBit(5) andBit(6) of the TDC output selected for PUF response generation, they demonstrate a measured reliability and uniqueness of up to 98.31% and 49.34%, respectively, with their excellent randomness performance as validated by the NIST SP800-22 tests. Under machine learning (ML)-based modeling attack with artificial neural network (ANN), the measured prediction accuracy of bothBit(5) andBit(6) can still be maintained at ~50% with a total of >200 million CRPs as the training set.

This paper presents an amplifier-chain-based XOR physical unclonable function (AC-XOR PUF), with the process- and/or bias-dependent voltage and amplification information of two identical amplifier chains serving as the entropy sources. The current-biased PUF cell using only 4 NMOS transistors achieves a small area with reduced temperature and supply sensitivity. Optimization on both the stage gain and stage number can reduce the input-referred noise (IRN) and improve the PUF reliability. We further employ an XOR gate to process the amplifier-chain outputs for the final response to improve the energy efficiency and uniqueness. The process- and bias-dependent stage amplification and the nonlinear amplifier-chain multiplication, which can significantly increase the number of modeling parameters and introduce a complex decision boundary respectively, can effectively resist machine learning (ML) modeling attacks. Fabricated in standard 65nm CMOS, the proposed AC-XOR PUF occupies an active area of$6845\mu \text{m}^{2}$. Without discarding any challenge-response pairs (CRPs), this work features a measured worst case bit error rate (BER) of 5.70% across$1.06\sim 1.55V$and$- 30\sim 125^{\circ }\text{C}$, while demonstrating a reliability (intra-die HD) and uniqueness (inter-die HD) of 0.58% and 49.92%, respectively. It also achieves a ML prediction accuracy of 50.72% using$80\times 80\times 80$artificial neural network (ANN) with 1M CPRs as training set.

This paper presents a digital N × N multiplier-based multi-bit strong physical unclonable function (PUF), which utilize the intrinsic path delay of the multiplier to achieve an approximated 1 : 2N2average challenge-to-response extraction to effectively increase the number of PUF responses. The PUF Extractor triggers the digital multiplier, and further processes the multiplier intrinsic path delay through a time-to-digital converter (TDC). Implemented with Xilinx Artix-7 FPGAs using the automatic place and route function, the proposed strong PUF demonstrates a 64-bit challenge with 32-bit multipliers with an extra level of unpredictability for counterfeiting model-based machine learning attack. With an average of 1:2048 responses per challenge, measurement results show that the uniqueness is 53.16%, and the stability of up to 95.54%, respectively.

In this paper, we present a novel multiple-stage amplifier chain based strong physical unclonable function (PUF) with low power and energy consumption. Based on the proposed two-dimensional subthreshold amplifier array, 12 different amplifiers can be selected through the analog multiplexer at each column. As a result, a 12-stage amplifier chain can be formed by applying different challenges to the aforesaid analog multiplexers with a linear feedback shift register (LFSR). Due to the inevitable process variation, the output voltage of the amplifier chain's last stage varies depending on the various combinations of the selected amplifiers, whose number features an exponential relationship with the size of the adopted amplifier array. By using 65nm standard CMOS process, the proposed strong PUF implementation is validated with high reliability and randomness. According to our extensive simulation results, the averaged bit error rate (BER) per 10°C and BER per 0.1V are calculated to be 3.15% and 3.85% for the operating temperature range of -20°C~120°C and supply voltage range of 0.9V~1.4V, respectively. Meanwhile, the proposed strong PUF's high randomness is also verified by passing both the NIST and auto-correlation function (ACF) test suites. Moreover, featuring an excellent uniqueness of 49.54%, the overall power consumption is simulated to be 0.128μW at the throughput of 0.02Mb/s, which corresponds to an energy consumption as low as 6.4pJ/bit.

In this paper, we present a novel smoothness-based interpolation algorithm for the division of focal plane Polarimeters (DoFP). By calculating the divided blocks' variance that represents their local smoothness, the proposed algorithm well-balances between the traditional bilinear and bicubic interpolation algorithms. In addition, compared with the previously reported gradient-based interpolation algorithm which only indicates the image's directional change along 0°, 90°, 45° and 135°, the presented smoothness-based algorithm covers the variations along all the possible directions, leading to more accurate selection between the bilinear and bicubic algorithms. According to our extensive simulation results, the proposed implementation exhibits the lowest mean square error (MSE) for the test images among all the previously reported algorithms, including bilinear, bicubic and gradient-based interpolation algorithms.