Efficient sensing approaches for high-density memristor sensor array

Abstract

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 analyzed. A novel Verilog-A-based memristor model that emulates the gas sensing behavior of doped metal oxides is developed for simulation and integration with design automation tools. Using this model, we propose and analyze 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 organization 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 $$8\times 8$$ 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.