Metal oxides-based memristive devices: fabrication and characterizations / Nor Azira Akma Shaari

Shaari, Nor Azira Akma (2017) Metal oxides-based memristive devices: fabrication and characterizations / Nor Azira Akma Shaari. Masters thesis, Universiti Teknologi MARA.


This thesis presents the metal oxide-based memristive device intended for sensor application. Sol-gel spin coating technique was proposed as a simple method to fabricate the Ti02-based and ZnO-based memristive devices. These devices were compared for their performances by investigating their properties characterization. Lateral configuration memristive device was also proposed to investigate its capability of resistive switching. Both vertical and lateral configurations of memristive devices were discussed. Spin coating speed, annealing time and temperature were varied for vertical configuration memristive devices. Meanwhile, oxide width and electrodes width are varied for lateral configuration memristive devices. Samples produced were characterized for its resistive switching and supported by their physical properties characteristics. It was found that ZnO-based and Ti02-based memristive devices with spin coating speed of 3D00 rpm, annealed 350 °C for 1 hour are the optimized samples with resistance ratios of 1.96 and 3.233 respectively. Meanwhile, for the lateral configuration of Ti02-based memristive device with oxide width of 0.1cm and electrodes width of 1cm is the optimized sample. Sensing capability of these metaloxides memristive devices were also investigated and it was proven that TiC>2 is suitable for UV sensor application as opposed to ZnO. Electroforming process was carried out to determine the suitable voltage sweep for metal oxides in order to avoid irreversible damage to the samples. Measurement cycles were carried out to observe the memristive devices' reproducibility. The effect of polarity of voltage was also explored to observe the switching capability of the memristive device under different polarity bias application.


Item Type: Thesis (Masters)
Email / ID Num.
Shaari, Nor Azira Akma
Email / ID Num.
Thesis advisor
Herman, Sukreen Hana (Dr.)
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Apparatus and materials
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Master of Science (Electrical Engineering) - EE750
Keywords: memristive, metal, sensor
Date: October 2017
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