Resistive random-access memory (RRAM) technology has been gaining importance due to scalability, low power, non-volatility, and the ability to perform in-memory computing. The RRAM sensing applications have also emerged to enable single RRAM technology platforms which include sensing, data storage, and computing. This paper reports on sol-gel drop coated low-power $\mu $ -thick Ag/TiO ₂ /Cu memristor, named MemSens, developed for radiation sensing. MemSens exhibits a bipolar memristive switching behavior within a small voltage window, ranging up to +0.7 V for the turn-ON, and down to −0.2 V for the turn-OFF. Under these operating conditions, MemSens has 67% less switching voltage, 20% drop in ON switching current, 75% reduced active area and > 3x improved device endurance, compared to the best characteristics reported in the literature for $\mu $ -thick memristors. The device is tested under direct exposure to ionizing Cs-137 662keV $\gamma $ -rays, during which a significant increase in the electrical conductivity of the device is observed. MemSens circuit is proposed to allow a relatively real time and cost-effective radiation detection. This provides a first insight to the advancement of reliable memristors that could potentially be deployed in future low-power radiation sensing technologies for medical, personal protection, and other field applications.

Random spray retinex (RSR) is an effective image enhancement algorithm owing to its effectiveness in improving the image quality. However, the computing complexity of the algorithm, the required hardware resources, and memory access hamper its deployment in many application scenarios, for instance, in IoT systems with limited hardware resources. With the rise of artificial intelligence (AI), the use of image enhancement has become essential for improving the performance of many emerging applications. In this paper, we propose the use of RSR as a preprocessing filter before the task of semantic segmentation of low-quality urban road scenes. Using the publicly available Cityscapes dataset, we compared the performance of a pre-trained deep semantic segmentation network on dark and noisy images with that of RSR preprocessed images. Our findings confirm the effectiveness of RSR in improving segmentation accuracy. In addition, to address the computational complexity and suitability of edge devices, we propose a novel and efficient implementation of RSR using resistive random access memory (RRAM) technology. This architecture provides highly parallel analog in-memory computing (IMC) capabilities. A detailed, efficient, and low-latency implementation of RSR using RRAM-CMOS technology is described. The design was verified using SPICE simulations with measured data from the fabricated RRAM and 65 nm CMOS technologies. The approach presented here represents an important step towards a low-complexity, real-time hardware-friendly architecture and the design of retinex algorithms for edge devices.

Adaptable communication systems are of great interest as they provide dynamic front end to accommodate the tunable spectrum management in advanced wireless systems. Memristor (acronym of memory resistor) is an emerging technology part of resistive RAM (RRAM) that has good potential for application in reconfigurable RF devices. The potentiality of using resistive switches for frequency tuning of high frequency RF filters is successfully explored in this article for the first time. Tunable RF filter is designed with detailed simulation using Ansys HFSS, and then correlated with measured results from experiment. As a proof of concept, a prototype of the tunable RF filter is fabricated by using a graphene oxide (GO) integrated with a conventional microstrip open stub notch filter. The resistor switching ability of the device is exploited for the frequency tuning. The resonating length of the notch filter is varied by changing the resistance of the active GO material between ‘HIGH’ (OFF) and ‘LOW’ (ON) resistance states. The measured results demonstrate the great potential of using RRAM in tunable RF devices. It also proves the possibility of tuning RF devices without any localized surface mount device (SMD) element or complex realization technique.

This work reports on the fabrication of a novel planar reduced graphene oxide (rGO) memristor (MR) device. For the first time in the literature, the MR tunable resistive switching behavior is controlled by the GO reduction time at a constant temperature. The device is fabricated using standard microfabrication techniques on a flexible cyclic olefin copolymer substrate (COC). Thermal reduction of the GO layer at low temperatures (100 °C) avoids the drawbacks of chemical reduction methods such as toxicity and electrode metal damage during fabrication, while allowing for fine-tuning of the MR’s switching behavior. The device has analog switching characteristics, with a range of different resistance states. By taking advantage of the slow nature of GO thermal annealing, the switching properties of the rGO memristors can be precisely controlled by adjusting the reduction period. At short annealing times (i.e., T < 20 h), the devices switch from high to low resistance states, while at longer annealing times the switching behavior is reversed, with the device switching from low to high resistance states (LRS to HRS). Resistive switching occurs as a result of the diffusion and removal of the oxygen functional groups in the GO film caused by Joule heating induced by the electric current. Complete electrical characterization tests are presented along with wettability and X-ray diffraction (XRD) tests. This work opens a new vision for realizing rGO-based MR devices with tunable switching properties, broadening the application horizon of the device.

Recently, Memristor (MR) device has received much attention in many applications, including memory, computing, neuromorphic, sensing, and security. Electrical characterization and testing of MR are among the main challenges facing MR technology. Available MR characterization tools are costly and bulky, limiting the research contributions in this field to big institutions. Furthermore, there is a need for a portable testing platform that provides the means to extract MR parameters, mainly when used in sensing or security applications. This work proposes a novel power-efficient, cost-effective, lightweight, and automated Arduino-based MR characterization platform named MemChar. A detailed circuit and system implementation of MemChar are presented. MemChar functionality results are compared to industry-standard device parameter analyzer tools, i.e., Keithley 4200A, with excellent correlation. MemChar is an easy-to-implement and user-friendly platform that provides a significant milestone toward utilizing MR devices in mobile, IoT, and sensing applications.