Resistive Random Access Memories (RRAMs) are based on resistive switching (RS) operation and exhibit a set of technological features that make them ideal candidates for applications related to non-volatile memories, neuromorphic computing and hardware cryptography. For the full industrial development of these devices different simulation tools and compact models are needed in order to allow computer-aided design, both at the device and circuit levels. Most of the different RRAM models presented so far in the literature deal with temperature effects since the physical mechanisms behind RS are thermally activated; therefore, an exhaustive description of these effects is essential. As far as we know, no revision papers on thermal models have been published yet; and that is why we deal with this issue here. Using the heat equation as the starting point, we describe the details of its numerical solution for a conventional RRAM structure and, later on, present models of different complexity to integrate thermal effects in complete compact models that account for the kinetics of the chemical reactions behind resistive switching and the current calculation. In particular, we have accounted for different conductive filament geometries, operation regimes, filament lateral heat losses, the use of several temperatures to characterize each conductive filament, among other issues. A 3D numerical solution of the heat equation within a complete RRAM simulator was also taken into account. A general memristor model is also formulated accounting for temperature as one of the state variables to describe electron device operation. In addition, to widen the view from different perspectives, we deal with a thermal model contextualized within the quantum point contact formalism. In this manner, the temperature can be accounted for the description of quantum effects in the RRAM charge transport mechanisms. Finally, the thermometry of conducting filaments and the corresponding models considering different dielectric materials are tackled in depth.

This paper presents a circuit-theoretic analysis of a NbO ₂ -Mott memristor fabricated at Hewlett-Packard Labs. It investigates mechanisms behind the origin of complexity based on local activity, which characterizes the behavior of this outstanding nanodevice. We propose an accurate, particularly simplified version of a recently introduced physical model suitable for large-scale circuit simulations. Following the concept of local activity, we then conduct a small-signal circuit-theoretic derivation of the impedance and associated small-signal equivalent circuit elements to analyze device stability and frequency response. Finally, our analysis reveals locally active operating regions, as well as regions where the device dynamics are positioned on the edge of chaos. The latter regions are crucial for designing bio-inspired computing systems.

We demonstrate the selective detection of hydrogen sulfide at breath concentration levels under humid airflow, using a self-validating 64-channel sensor array based on semiconducting single-walled carbon nanotubes (sc-SWCNTs). The reproducible sensor fabrication process is based on a multiplexed and controlled dielectrophoretic deposition of sc-SWCNTs. The sensing area is functionalized with gold nanoparticles to address the detection at room temperature by exploiting the affinity between gold and sulfur atoms of the gas. Sensing devices functionalized with an optimized distribution of nanoparticles show a sensitivity of 0.122%/part per billion (ppb) and a calculated limit of detection (LOD) of 3 ppb. Beyond the self-validation, our sensors show increased stability and higher response levels compared to some commercially available electrochemical sensors. The cross-sensitivity to breath gases NH₃ and NO is addressed demonstrating the high selectivity to H₂S. Finally, mathematical models of sensors’ electrical characteristics and sensing responses are developed to enhance the differentiation capabilities of the platform to be used in breath analysis applications.