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.

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.