School of Engineering, Computing and Mathematics
Faculty of Technology, Design and Environment
Memristors are finding applications in memory, logic, neuromorphic systems, and data security. To this end, we leverage the non-linear behaviour of memristors to devise a low overhead physical unclonable function using a memristive chaos circuit in conjunction with a non-linear memristive encoder. We demonstrate the effectiveness of this architecture in Challenge-Response-Pair based authentication, and for its physical uncloneability. This architecture is highly versatile and can be implemented with a single encoder or a number of encoders running in parallel, each one with its own merit, for extending the sizes of CRPs. To demonstrate its effectiveness, we subject the architecture to machine learning based modelling attacks e.g. Logistic Regression, Support VectorMachines, Random Forest, as well as Artificial Neural Network classifiers. We found out that the proposed PUF architecture provides better resistance to such attacks, even for smaller bit sizes and at reduced overheads.
Sensors give factual and process information about the environment or other physical phenomena. Sensing using memristors has been recently introduced for its potential for high density integration and miniaturization. Complementary Resistive Switch (CRS) based sensor provides an extremely efficient crossbar array that reduces the sneak current. The objective of this paper is to introduce and evaluate a circuit model for sensing using memristive complementary resistive switch. We introduce a reliable SPICE implementation of memristor model that captures the sensing behaviour of memristor. Our simulation results also validate the SPICE model for CRS sensing architecture, whose parameters could be easily adapted to match experimental data. The results also investigate the sensitivity and device behaviour of memristor and CRS sensor device in the presence of oxidizing and reducing gases of different concentration.
This paper proposes a method to calculate the yield of a memristor based sensor array considered as theprobability that the chip provides acceptable sensing results when the array is affected by manufacturing defects. The modeling is based on a Markov Chain approach, in which each state represents an operating chip configuration and the state transitions take into account manufacturing defects. The proposed method is applicable to evaluate the yield with different fault models to achieve the comparative yield obtainedby several redundancy allocations.
Memristors are an attractive option for use in future architectures due to their non-volatility, high density and low power operation. Gas sensing is one of the proposed application of memristive devices. In spite of these advantages, memristors are susceptible to defect densities due to the nondeterministic nature of nano-scale fabrication. In this paper, a novel spice memristor model incorporating fault models that emulates the gas sensing behaviour with/without faults is developed for simulation and integration with design automation tools. Our simulation results show that the proposed non-linear model detects the presence of the oxidising/reducing gas and analyses the defects/faults affecting the functionality of the sensor.
Resistive memory, also known as memristor, is an emerging potential successor to traditional CMOS charge based memories. Memristors have also recently been proposed as a promising candidate for several additional applications such as logic design, sensing, non-volatile storage, neuromorphic computing, Physically Unclonable Functions (PUFs), Contentaddressable memory (CAM) and reconfigurable computing. In this paper, we explore three unique applications of memristor technology based implementations, specifically from the perspective of sensing, logic, in-memory computing and their solutions. We review solar cell health monitoring and diagnosis, describe the proposed solutions, and provide directions in memristive gas sensing and in-memory computing. For the gas sensor application, in order to determine the number of memristors to ensure a certain level of accuracy in sensitivity, a technique to optimize the sensor array based on an acceptable sensitivity variation and minimum sensitivity margin is presented. These "out-of-the-box" emerging ideas for applications of memristive devices in enhancing robustness and, at the same time, how the requirements of robust design are enabling unconventional use of the devices. To this end, the papers considers some examples of this mutual interaction.