Memristors, traditionally considered as non-volatile resistive memories for high-density applications, also exhibit excellent sensitivity to chemicals, making them suitable for chemical sensing with intrinsic memory capabilities. This paper introduces an innovative technique for directly measuring and digitising sensor readings, such as gas concentration, using the switching state of the device, which is influenced by the applied bias voltage or current in the presence of chemicals. When a sensor itself detects and measures a chemical property, its state changes, enabling the direct digitisation of the sensed information. The proposed memristive sensor employs a TiO 2 based memristor as both the sensing and digitising element, and is evaluated using SPICE simulations with hydrogen gas (H 2 ) at different concentrations. We present a calibration curve that establishes a reliable correlation between pulse counts and chemical concentration, highlighting the consistent relationship between switching behaviour and concentration levels. This method significantly reduces the reliance on separate analogue to digital converters (ADC), simplifying the sensor architecture in terms of power consumption and circuit complexity. Additionally, the inherent nonlinearity of the fabricated devices renders this digitisation method significantly nonlinear, which can provide an added layer of security to the measured information. This approach paves the way for compact, low-power chemical sensing nodes, making them suitable for future integrated environmental monitoring systems.

This paper evaluates the yield of a memristor-based crossbar array of artificial neural networks in the presence of stuck-at-faults (SAFs). A technique based on Markov chains is used to estimate the yield in the presence of stuck-at-faults. This method provides a high degree of accuracy. Another method that is used for analysis and comparison is the Poisson distribution, which uses the sum of all repairable fault patterns. A fault repair mechanism is also considered when evaluating the yield of the memristor crossbar array. The results demonstrate that the yield could be improved with redundancies and a higher repairable stuck-at-fault ratio.

We propose novel memristive gas sensor architectures that can significantly reduce process and parametric variations in a predictable manner, while improving accuracy and overall power consumption. The proposed architecture can also be configured to realize multifunction logic operations as well as Complementary Resistive Switch with low hardware overhead in the absence of gasses. Our results show that the proposed architecture is significantly immune to process and parametric effects compared to a single sensor and almost unaffected by wire resistance, while offering much higher accuracy and much lower power consumption compared to existing techniques.

This paper proposes a method to calculate the yield of a memristor based sensor array considered as the probability 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 obtained by 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), Content-addressable 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.