ABSTRACT
A methodology to estimate the device temperature in resistive random access memories (RRAMs) is presented. Unipolar devices, which are known to be highly influenced by thermal effects in their resistive switching operation, are employed to develop the technique. A 3D RRAM simulator is used to fit experimental data and obtain the maximum and average temperatures of the conductive filaments (CFs) that are responsible for the switching behavior. It is found that the experimental CFs temperature corresponds to the maximum simulated temperatures obtained at the narrowest sections of the CFs. These temperature values can be used to improve compact models for circuit simulation purposes.
ABSTRACT
Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to variability and reliability issues, which are usually evaluated through switching endurance tests. However, we note that most studies that claimed high endurances >106 cycles were based on resistance versus cycle plots that contain very few data points (in many cases even <20), and which are collected in only one device. We recommend not to use such a characterization method because it is highly inaccurate and unreliable (i.e., it cannot reliably demonstrate that the device effectively switches in every cycle and it ignores cycle-to-cycle and device-to-device variability). This has created a blurry vision of the real performance of RS devices and in many cases has exaggerated their potential. This article proposes and describes a method for the correct characterization of switching endurance in RS devices; this method aims to construct endurance plots showing one data point per cycle and resistive state and combine data from multiple devices. Adopting this recommended method should result in more reliable literature in the field of RS technologies, which should accelerate their integration in commercial products.
ABSTRACT
Immunosensors based on electrolyte-oxide-semiconductors (EOS) have been extensively researched over the last few decades. By electrochemical impedance spectroscopy (EIS) the specific molecular biorecognition of the antibody-antigen (Ab-Ag) can be detected providing an alternative quantitative system to immunoassay techniques. The electrochemical variations from a fabricated immunosensor can provide quantitative values for the analyte of interest at reduced costs and analysis time. In this context, a novel EOS substrate based on aluminum oxide (Al2O3) grown by atomic layer deposition on silicon was applied. The interaction between recombinant human (rh) interleukin-10 (IL-10) with the corresponding monoclonal antibody (mAb) for early cytokine detection of an anti-inflammatory response due to left ventricular assisted device implantation was studied. For this purpose, a 3D biosensor was composed of multi-walled carbon nanotubes with carboxylic acid functionalities (multi-walled carbon nanotubes-COOH) to increase the surface area for the range of human IL-10 detection. These were activated with N-hydroxysuccinimide and N-(3-dimethylaminopropyl)-N'-ethyl-carbodiimide hydrochloride for the immobilization of the anti-human IL-10 mAb. First, the interaction between the Ab and Ag was observed by fluorescence patterning to ensure that the biorecognition event was achievable. Then, EIS is explained for the quantification of commercial human IL-10 on this capacitance-based EOS macroimmuno-FET sensor.
