Memristor technology has shown great promise for energy-efficient computing [1] , though it is still facing many challenges [1 , 2]. For instance, the required additional costly electroforming to establish conductive pathways is seen as a significant drawback as it contributes to power and area overheads, and limited device endurance. In this work, we propose a novel forming-free HfO₂ -based ReRAM device with low operating voltages , multi-level capability , and less sensitivity to device-to-device (D2D) and cycle-to-cycle (C2C) variations. The device is fabricated using CMOS-compatible processes, excluding the undesirable complex steps mandatory to manufacture the state-of-the-art forming-free devices [3, 4, 5]. This is accomplished by utilizing the desirable formation energy of Pd-O bonds [6, 7], which creates conducting paths at room temperature while maintaining the analog switching ability of the devices. The proposed ReRAM device holds a great value for dense memories and energy-efficient compute architectures.

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing the unique characteristics of single-wall carbon nanotubes (SWCNTs) to act as the switching medium of the device. Via the planar structure, the memristor device exhibits analog switching ability with high state stability. The device’s conductance and capacitance can be tuned simultaneously, increasing the device's potential and broadening its applications' horizons. The multi-state storage capability and long-term memory are the key factors that make the device a promising candidate for bio-inspired computing applications. As a demonstrator, the fabricated memristor is deployed in spiking neural networks (SNN) to exploit its analog switching feature for energy-efficient classification operation. Results reveal that the computation-in-memory implementation performs Vector Matrix Multiplication with 95% inference accuracy and few femtojoules per spike energy efficiency. The memristor device presented in this work opens new insights towards utilizing the outstanding features of SWCNTs for efficient analog computation in deep learning systems.

Environmentally friendly humidity sensors with high sensing performance are considered crucial components for various wearable electronic devices. We developed a rapid-response and durable Paper Cellulose Fiber/Graphene Oxide Matrix (PCFGOM) humidity sensor using an all-carbon functional material. The fabricated sensor demonstrated a high sensitivity to humidity through an electrical impedance measurement, with an increase in response to humidity ranging from 10% to 90% at 1 kHz and 10 kHz, respectively, along with a response time of 1.2 s and a recovery time of 0.8 s. The stability of the sensor was also examined, with consistent performance over a period of 24 h. This novel sensor was employed in several applications, including non-contact proximity sensing, environmental humidity detection, and human respiration detection, to showcase its potential. Moreover, this work represents a significant milestone in developing inexpensive and eco-friendly humidity sensors, given the abundance of paper and graphene in nature and their biocompatibility.

Due to their stability, single-walled carbon nanotubes (SWCNTs) have been used for multiple applications in the semiconductor industry. Herein, we report the humidity sensing capability of SWCNTs by comparing the different densities of SWCNTs dispersed on the sensing area of the planner sensor device. Three different humidity sensors have been fabricated by preparing three different densities of CNTs diluted in deionized water and drop cast on the channel of 100 μm width between 2mm x 1mm gold electrodes. It is observed that for very low density and high density of SWCNTs sensing layer, the sensing behavior either lacks in the detection range and response time, respectively. However, we found the optimized density of SWCNTs in deionized water for a highly sensitive, fast, and high-range humidity sensor for the optimized density of SWCNTs in deionized water.

In recent years, there has been a growing interest in investigating the potential of emerging memristor (MR) devices for gas sensing applications, particularly at room temperature. This article reports on a planar Au/reduced graphene oxide (rGO)/Au memristive hydrogen sensor, fabricated on a cost-effective cyclic olefin copolymer (COC) substrate, and utilizing the rGO green carbon material as its active sensing element. The sensor's performance is evaluated using two different testing modes: conventional chemiresistive testing under a constant voltage bias (CVB) and voltage pulse (VP) modes. The CVB mode demonstrates high repeatability, selectivity, response time, and recovery time, indicating the sensor's reliable gas sensing capabilities. In addition, the VP mode significantly enhances the sensor's relative percentage response, indicating its potential for improved gas sensing performance. To optimize the sensor's response, the impact of hydrogen exposure on the MR resistive switching is studied, revealing that the effect is contingent on the VP amplitude. Specifically, gas-enhanced resistive switching is achieved at lower voltage levels, whereas at higher voltage levels, gas exposure slows down the rate of resistive switching. Consequently, voltage-pulse testing is conducted at two voltage magnitudes, low (2.5 V) and high (4.5 V), and the sensor's response is enhanced from 0.5% under CVB mode to 786% under VP mode.

Recently, phase change chalcogenides, such as monochalcogenides, are reported as switching materials for conduction-bridge-based memristors. However, the switching mechanism focused on the formation and rupture of an Ag filament during the SET and RESET, neglecting the contributions of the phase change phenomenon and the distribution and re-distribution of germanium vacancies defects. The different thicknesses of germanium telluride (GeTe)-based Ag/GeTe/Pt devices are investigated and the effectiveness of phase loops and defect loops future application in neuromorphic computing are explored. GeTe-based devices with thicknesses of 70, 100, and 200 nm, are fabricated and their electrical characteristics are investigated. Highly reproducible phase change and defect-based characteristics for a 100 nm-thick GeTe device are obtained. However, 70 and 200 nm-thick devices are unfavorable for the reliable memory characteristics. Upon further analysis of the Ag/GeTe/Pt device with 100 nm of GeTe, it is discovered that a state-of-the-art dependency of phase loops and defect loops exists on the starting and stopping voltage sweeps applied on the top Ag electrode. These findings allow for a deeper understanding of the switching mechanism of monochalcogenide-based conduction-bridge memristors.