Flash-based security primitives: Evolution, challenges and future directions

Over the last two decades, hardware security has gained increasing attention in academia and industry. Flash memory has been given a spotlight in recent years, with the question of whether or not it can prove useful in a security role. Because of inherent process variation in the characteristics of flash memory modules, they can provide a unique fingerprint for a device and have thus been proposed as locations for hardware security primitives. These primitives include physical unclonable functions (PUFs), true random number generators (TRNGs), and integrated circuit (IC) counterfeit detection. In this paper, we evaluate the efficacy of flash memory-based security primitives and categorize them based on the process variations they exploit, as well as other features. We also compare and evaluate flash-based security primitives in order to identify drawbacks and essential design considerations. Finally, we describe new directions, challenges of research, and possible security vulnerabilities for flash-based security primitives that we believe would benefit from further exploration

Smartphone-based molecular sensing for advanced characterization of asphalt concrete materials

Pavement systems deteriorate with time due to the aging of materials, excessive use, overloading, climatic conditions, inadequate maintenance, and deficiencies in inspection methods. Proper evaluation of pavement conditions provides important decision-support to implement preventative rehabilitation. This study presents an innovative smartphone-based monitoring method for advanced characterization of asphalt concrete materials. The proposed method is based on deploying a pocket-sized near-infrared (NIR) molecular sensor that is fully integrated with smartphones. The NIR spectrometer illuminates a sample with a broad-spectrum of near-infrared light, which can be absorbed, transmitted, reflected, or scattered by the sample. The light intensity is measured as a function of wavelength before and after interacting with the sample. Thereafter, the diffuse reflectance, a combination of absorbance and scattering, caused by the sample is calculated. This portable smartphone-based NIR method is used to detect asphalt binders with various performance grading (PG) and aging levels. To this end, a number of binder samples are tested in a wavelength range of 740 to 1070 nm. The results indicate that asphalt binders with different grades and aging levels yield significantly different spectrums. These distinctive spectrums can be attributed to the variations of binder components such as saturate, asphaltenic, resin, and aromatic. Furthermore, the molecular sensor is successfully deployed to detect and classify asphalt mixtures fabricated with a various binder and recycled material types such as styrene-butadiene-styrene (SBS), ground tire rubber (SBS), engineered crumbed rubber (ECR), reclaimed asphalt pavement (RAP), and recycled asphalt shingles (RAS). The proposed monitoring technology is not only a viable tool for asphalt material characterization but also a cost-effective platform capable of transforming the current physical and chemical methods for civil engineering material characterization.Includes bibliographical reference

Leveraging CMOS Aging for Efficient Microelectronics Design

Aging is known to impact electronic systems affecting performance and reliability. However, it has been shown that it also brings benefits for power saving and area optimization. This paper presents highlights of those benefits and further shows how aging effects can be leveraged by novel methods to contribute towards improving hardware oriented security and reliability of electronic circuits. We have demonstrated static power reduction in complex circuits from IWLS05 benchmark suite, reaching a noticeable 7S% of reduction in ten years of operation. In hardware oriented security, a novel aging sensor has been proposed for detection of recycled ICs, measuring discharge time Tdv of the virtual power (VV dd ) network in power-gated designs. This sensor utilizes discharge time of VV dd network through leakage current that is much more sensitive to aging than path delay, exhibiting up to 15.7X increment in 10 years. Furthermore, we show how frequency degradation caused by aging is used for online prediction of remaining useful lifetime (RUL) of electronic circuits. Results show an average RUL prediction deviation of less than 0.1 years. This methodology provides node calculations rather than a mean time to failure (MTTF) of the population. The set of techniques that are presented in this paper takes advantage of aging effects, having a positive impact in various aspects of microelectronic systems

A Support Vector Regression based Machine Learning method for on-chip Aging Estimation

Semiconductor supply chain industry is spread worldwide to reduce cost and to meet the electronic systems high demand for ICs, and with the era of internet of things (IoT), the estimated numbers of electronic devices will rise over trillions. This drift in the semiconductor supply chain produces high volume of e-waste, which affects integrated circuits (ICs) security and reliability through counterfeiting, i.e., recycled and remarked ICs. Utilising recycled IC as a new one or a remarked IC to upgrade its level into critical infrastructure such as defence or medical electronics may cause systems failure, compromising human lives and financial loss. This paper harvests aging degradation induced by BTI and HCI, observing frequency and discharge time affected by changes in drain current and sub-threshold leakage current over time, respectively. Such task is undertaken by Cadence simulations, implementing a 51-stage ring oscillator (51-RO) using 22nm CMOS technology library and aging model provided by GlobalFoundries (GF). Machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, discharge time, frequency, and aging time. The data sampling is performed over an emulated 12 years period with four representative temperatures of 20° C, 40° C, 60° C, and 80° C with additional testing data from temperatures of 25° C and 50° C. The results demonstrate a high accuracy on aging estimation by SVR, reported as a normal distribution with the mean (µ) equal to 0.01 years (3.6 days) and a standard deviation (σ) of ±0.1 years (±36 days)