Underwater images, often affected by light attenuation and particle scattering, pose a challenge for restoration, aggravated by the difficulty in obtaining a substantial amount of annotated data. Existing methods have tackled this issue through the development of semi-supervised frameworks; however, they commonly lack a suitable strategy or rely on additional models trained on extra data to ensure the quality of pseudo-labels. To address this, we propose a self-assessment training framework for semi-supervised underwater image restoration (SAT-UIR). SAT-UIR employs a dual-task network (DT-Net) incorporating an auxiliary assessment task to align the restored image with a target structure similarity index measure score. This enables accurate restoration completeness estimation at a feature level and effective pseudo-label filtering during self-training. Leveraging multi-scale features, the assessment task also encourages the model to learn advantageous features for image restoration. Moreover, we integrate a soft ranking loss to further refine the training process of the auxiliary assessment task. Comprehensive experiments on various underwater benchmarks demonstrate that SAT-UIR outperforms state-of-the-art methods quantitatively and qualitatively. The code is available at https://github.com/aroid721/SAT-UIR.

A Physical Unclonable Function (PUF) using capacitor mismatch in a standard successive approximation register analog-to-digital converter (SAR-ADC) as the entropy source is demonstrated in 65nm CMOS. SAR-ADCs are readily available in many system-on-chips, making the hardware overhead of the proposed PUF almost negligible. The inherent process variation of metal-oxide-metal (MOM) capacitors is harnessed through a charge redistribution operation which is sampled by the voltage comparator. To enhance the stability of the PUF output, soft response generation and dynamic thresholding techniques were adopted. Finally, we verify that performing the enrollment operation at a lower operating voltage can ensure that PUF responses are stable at the nominal supply voltage used during authentication.

The start-up value of an SRAM cell is unique, random, and unclonable as it is determined by the inherent process mismatch between transistors. These properties make SRAM an attractive circuit for generating encryption keys. The primary challenge for SRAM based key generation, however, is the poor stability when the circuit is subject to random noise, temperature and voltage changes, and device aging. Temporal majority voting (TMV) and bit masking were used in previous works to identify and store the location of unstable or marginally stable SRAM cells. However, TMV requires a long test time and significant hardware resources. In addition, the number of repetitive power-ups required to find the most stable cells is prohibitively high. To overcome the shortcomings of TMV, we propose a novel data remanence based technique to detect SRAM cells with the highest stability for reliable key generation. This approach requires only two remanence tests: writing `1' (or `0') to the entire array and momentarily shutting down the power until a few cells flip. We exploit the fact that the cells that are easily flipped are the most robust cells when written with the opposite data. The proposed method is more effective in finding the most stable cells in a large SRAM array than a TMV scheme with 1,000 power-up tests. Experimental studies show that the 256-bit key generated from a 512 kbit SRAM using the proposed data remanence method is 100% stable under different temperatures, power ramp up times, and device aging.

True random number generators (TRNGs) are crucial components for the security of cryptographic systems. In contrast to pseudo--random number generators (PRNGs), TRNGs provide higher security by extracting randomness from physical phenomena. To evaluate a TRNG, statistical properties of the circuit model and raw bitstream should be studied. In this article, a model for the beat frequency detector--based high-speed TRNG (BFD-TRNG) is proposed. The parameters of the model are extracted from the experimental data of a test chip. A statistical analysis of the proposed model is carried out to derive mean and variance of the counter values of the TRNG. Our statistical analysis results show that mean of the counter values is inversely proportional to the frequency difference of the two ring oscillators (ROSCs), whereas the dynamic range of the counter values increases linearly with standard deviation of environmental noise and decreases with increase of the frequency difference. Without the measurements from the test data, a model cannot be created; similarly, without a model, performance of a TRNG cannot be predicted. The key contribution of the proposed approach lies in fitting the model to measured data and the ability to use the model to predict performance of BFD-TRNGs that have not been fabricated. Several novel alternate BFD-TRNG architectures are also proposed; these include parallel BFD, cascade BFD, and parallel-cascade BFD. These TRNGs are analyzed using the proposed model, and it is shown that the parallel BFD structure requires less area per bit, whereas the cascade BFD structure has a larger dynamic range while maintaining the same mean of the counter values as the original BFD-TRNG. It is shown that 3.25 M and 4 M random bits can be obtained per counter value from parallel BFD and parallel-cascade BFD, respectively, where M counter values are computed in parallel. Furthermore, the statistical analysis results illustrate that BFD-TRNGs have better randomness and less cost per bit than other existing ROSC-TRNG designs. For example, it is shown that BFD-TRNGs accumulate 150% more jitter than the original two-oscillator TRNG and that parallel BFD-TRNGs require one-third power and one-half area for same number of random bits for a specified period.

The dependence of bias temperature instability (BTI) and hot carrier injection (HCI)-induced frequency degradation on interconnect length has been examined for the first time. Experimental data from 65-nm test chips show that frequency degradation due to BTI decreases monotonically for longer wires because of the shorter effective stress time, while the HCI-induced component has a nonmonotonic relationship with interconnect length due to the combined effect of increased effective stress time and decreased effective stress voltage. Simple aging models are proposed to capture the unique BTI and HCI behavior in global interconnect drivers. A closed-loop simulation methodology that takes into consideration the interplay between the frequency degradation and the stress parameters (such as stress duration and stress voltage) is used to determine the optimal repeater count and sizing in practical interconnect circuits.

Modern systems-on-a-chip depend heavily on hard intellectual properties, such as standard cell and datapath libraries. As the foundries accelerate their update of advanced processes with increasingly complex design rules, and the libraries grow in flexibility and size, the cost of library development becomes prohibitively high. Automated layout-migration techniques used today, which are based on layout compaction developed a decade ago, corrupt advanced design considerations by honoring only design rules, and cannot cope with some of the new challenges involved. In this paper, we present a new integer linear programming (ILP)-based layout-migration engine, called calligrapher, and make the following contributions. First, we extend the recently proposed minimum perturbation (MP) metric designed to retain original layout design intentions, while overcoming its shortcoming of biased treatment of layout objects. Second, we propose a new design-rule-constraint algorithm, and prove its linear complexity for the number of constraints generated. Compared with what has been achieved in the literature, the proposed algorithm can significantly reduce the ILP solver time by limiting the constraint size. Third, we propose an iterative migration framework based on the concept of soft constraint. With this framework, two-dimensional compaction quality can be achieved with a runtime comparable to one-dimensional compaction. We demonstrate the effectiveness of calligrapher by migrating the Berkeley low-power libraries, originally developed for the 1.2-/spl mu/m MOSIS process, into TSMC 0.25- and 0.18-/spl mu/m technologies. We show that even for a very compact layout, our metric and the MP metric can make a difference by as much as 20%-45%. We also show that our iterative algorithm can improve the area by 10% on average compared to the traditional technique using the MP metric, and inflates the area by merely 7.5% compared to the traditional technique using minimum-area metric.