Reliable gas sensing with memristive crossbar array

Gas sensing is one of the proposed application field of memristive devices. We used a crossbar array of memristors as gas sensor using the HP labs fabricated TiO2 based memristor model in an attempt to improve sensing accuracy. We introduced the possibility of reliable multiple gases detection using multiple rows of memristors as separate sensor in a crossbar array. Our experimental results show that an array of memristors can minimise measurement errors as well as provide a good redundancy measure during gas sensing. Measurements taken from the sensors are also not affected by alternate current paths problem often experienced in crossbar architecture

Design and analysis of memristor-based reliable crossbar architectures

The conventional transistor-based computing landscape is already undergoing dramatic changes. While transistor-based devices’ scaling is approaching its physical limits in nanometer technologies, memristive technologies hold the potential to scale to much smaller geometries. Memristive devices are used majorly in memory design but they also have unignorable applications in logic design, neuromorphic computing, sensors among many others. The most critical research and development problems that must be resolved before memristive architectures become mainstream are related to their reliability. One of such reliability issue is the sneak-paths current which limits the maximum crossbar array size. This thesis presents various designs of the memristor based crossbar architecture and corresponding experimental analysis towards addressing its reliability issues. Novel contribution of this thesis starts with the formulation of robust analytic models for read and write schemes used in memristive crossbar arrays. These novel models are less restrictive and are suitable for accurate mathematical analysis of any mn crossbar array and the evaluation of their performance during these critical operations. In order to minimise the sneak-paths problem, we propose techniques and conditions for reliable read operations using simultaneous access of multiple bits in the crossbar array. Two new write techniques are also presented, one to minimise failure during single cell write and the other designed for multiple cells write operation. Experimental results prove that the single write technique minimises write voltage drop degradation compared to existing techniques. Test results from the multiple cells write technique show it consumes less power than other techniques depending on the chosen configuration. Lastly, a novel Verilog-A memristor model for simulation and analysis of memristor’s application in gas sensing is presented. This proposed model captures the gas sensing properties of titanium-dioxide using gas concentration to control the overall memristance of the device. This model is used to design and simulate a first-of-its-kind sneak-paths free memristor-based gas detection arrays. Experimental results from a 88 memristor sensor array show that there is a ten fold improvement in the accuracy of the sensor’s response when compared with a single memristor sensor

Electrical Characterization of Spherical Copper Oxide Memristive Array Sensors

A new System Protection (SP) technology is explored by using electrical and mechanical interference-sensing devices that are implemented with granular memristive material. The granular materials consist of oxide-coated copper spheres with radii of about 700 µm that are placed in contact to produce thin oxide junctions which exhibit memristive behavior. Processes for etching, which compared acetic acid and nitric acid etches, and thermal oxidation at 100°C are performed and compared to produce copper spheres with a copper oxide layer over the sphere surface. Oxidized copper spheres are tested as sensor arrays by loading into a capillary tube in an aligned arrangement. The spheres are held in contact to characterize current-voltage behavior for various oxide thicknesses with typical ROFF values in the megaohm range. Electrical characterization of the oxidized copper spheres reveal directly proportional changes to current-voltage hyseteresis in µW under compressive forces. The thinnest oxide exhibited changes of 8.3 to 21.2 µW over 9 mN while the thickest had a response from 0.4 to 2.5 µW over 22.3 mN

Ion beam effect on Ge-Se chalcogenide glass films: Non-volatile memory array formation, structural changes and device performance

The conductive bridge non-volatile memory technology is an emerging way to replace traditional charge based memory devices for future neural networks and configurable logic applications. An array of the memory devices that fulfills logic operations must be developed for implementing such architectures. A scheme to fabricate these arrays, using ion bombardment through a mask, has been suggested and advanced by us. Performance of the memory devices is studied, based on the formation of vias and damage accumulation due to the interactions of Ar+ ions with GexSe1-x (x=0.2, 0.3 and 0.4) chalcogenide glasses as a function of the ion energy and dose dependence. Blanket films and devices were created to study the structural changes, surface roughness, and device performance. Raman Spectroscopy, Atomic Force Microscopy (AFM), Energy Dispersive X-Ray Spectroscopy (EDS) and electrical measurements expound the Ar+ ions behavior on thin films of GexSe1-x system. Raman studies show that there is a decrease in area ratio between edge-shared to corner-shared structural units, revealing occurrence of structural reorganization within the system as a result of ion/film interaction. AFM results demonstrate a tendency in surface roughness improvement with increased Ge concentration, after ion bombardment. EDS results reveal a compositional change in the vias, with a clear tendency of greater interaction between ions and the Ge atoms, as evidenced by greater compositional changes in the Ge rich films

Efficient sensing approaches for high-density memristor sensor array

Recent research shows ever growing interest in the potential applications of memristive devices. Among the many proposed fields, sensing is one of the most interesting as it could lead to unprecedented sensor density and ubiquity in electronic systems. In this paper, a framework for efficient gas detection using memristor crossbar array is proposed and analysed. A novel Verilog-A based memristor model that emulates the gas sensing behaviour of doped metal oxides is developed for simulation and integration with design automation tools. Using this model, we propose and analyse three different gas detection structures based on array of memristor-based sensors. Gas presence together with some of its properties can be detected using resistance changes and spatial information from one or group of memristive sensors. Our simulation results show that depending on the organisation of the memristive elements and the sensing method, the response of the sensor varies providing a broader design space for future designers. For instance, with a 8 × 8 memristor sensor array, there is a ten times improvement in the accuracy of the sensor’s response when compared with a single memristor sensor but at the expense of extra area overhead

Über die Entwicklung von Memsensoren

Since the postulation of the experimental realization of memristive devices in 2008, a broad variety of concepts for the fabrication of memristive devices has been pursued and the underlying switching mechanisms have been studied in detail. The unique electronic properties of memristive devices inspire applications that go beyond conventional electronics, such as using memristive devices as programmable interconnects, to realize logics for in-array-computing or in neuromorphic engineering. A particularly interesting aspect of biological neural networks is the close connection between signal detection and processing at the neuron level, which is an essential contribution to their outstanding efficiency. This work evolves around the concept of memsensors, which unify the characteristic features of memristive devices and sensor devices and as such appear as promising candidates to realize a close connection between signal detection and processing on the device level. Memsensors are a highly interdisciplinary topic, bridging research in the fields of material science and electrical engineering and relating to insights from biology and medicine through neuromorphic engineering. The major objective of this thesis is to provide tools and building blocks and showcase pathways to incorporate memristive and sensitive properties into memsensor devices. For this purpose, motivated by an experimental point of view, a nanoparticle-based memristive device with diffusive memristive switching characteristics was developed and characterised in detail and sensors relying on semiconducting metal oxide thin films and nanostructures were thoroughly studied. In addition, in terms of modelling of memsensor circuits, emerging features such as amplitude adaptation are discussed, showcasing the particular eligibility of memsensors in the context of neuromorphic engineering