Recently, resistive random access memory (RRAM) has been an outstanding candidate among various emerging nonvolatile memories for high-density storage and in-memory computing applications. However, traditional RRAM, which accommodates two states depending on applied voltage, cannot meet the high density requirement in the era of big data. Many research groups have demonstrated that RRAM possesses the potential for multi-level cells, which would overcome demands related to mass storage. Among numerous semiconductor materials, gallium oxide (a fourth-generation semiconductor material) is applied in the fields of optoelectronics, high-power resistive switching devices, and so on, due to its excellent transparent material properties and wide bandgap. In this study, we successfully demonstrate that Al/graphene oxide (GO)/Ga₂O₃/ITO RRAM has the potential to achieve two-bit storage. Compared to its single-layer counterpart, the bilayer structure has excellent electrical properties and stable reliability. The endurance characteristics could be enhanced above 100 switching cycles with an ON/OFF ratio of over 10³. Moreover, the filament models are also described in this thesis to clarify the transport mechanisms.

This work reports 2-bits/cell hafnium oxide-based stacked resistive random access memory devices fabricated on flexible polyimide substrates for neuromorphic applications considering the high thermal budget. The ratio of low-resistance state current ( ${I}_{ \mathrm{\scriptscriptstyle ON}}$ ) to high-resistance state current ( ${I}_{ \mathrm{\scriptscriptstyle OFF}}$ ) or ${I}_{ \mathrm{\scriptscriptstyle ON}}/{I}_{ \mathrm{\scriptscriptstyle OFF}}$ for the fabricated devices was above $1.4\times10$ ³ with a low device-to-device variation at $100 \boldsymbol {\mu }\text{A}$ current compliance. The mechanical stability over 10 ⁴ bending cycles at a 5 mm bending radius and endurance over 10 ⁶ WRITE cycles makes these devices suitable for online neural network training. The data retention capability over 10 ⁴ s at 125°C also infuses these devices’ long-term inference capability. Furthermore, the performance of the devices has been verified for neuromorphic applications by system-level simulations with experimentally calibrated data. The system-level simulation reveals only a 2% loss in inference accuracy over ten years from the baseline.

Zr can be stabilized by the element selected, such as Mg-stabilized Zr (MSZ), thus providing MSZ thin films with potentially wide applications and outstanding properties. This work employed the element from alkaline earth metal stabilized Zr to investigate the electrical properties of sol–gel AZrO_x (A = alkaline earth metal; Mg, Sr, Ba) as dielectric layer in metal-insulator–metal resistive random-access memory devices. In addition, the Hume–Rothery rule was used to calculate the different atomic radii of elements. The results show that the hydrolyzed particles, surface roughness, and density of oxygen vacancy decreased with decreased difference in atomic radius between Zr and alkaline earth metal. The MgZrOx (MZO) thin film has fewer particles, smoother surface, and less density of oxygen vacancy than the SrZrO_x (SZO) and BaZrO_x (BZO) thin films, leading to the lower high resistance state (HRS) current and higher ON/OFF ratio. Thus, a suitable element selection for the sol–gel AZrO_x memory devices is helpful for reducing the HRS current and improving the ON/OFF ratio. These results were obtained possibly because Mg has a similar atomic radius as Zr and the MgO_x-stabilized ZrO_x.

This study demonstrates a pentacene-based nonvolatile memory using a ferroelectric poly (vinylidene fluoride-trifluoroethylene) $/$ Barium Zirconate Titanate (P(VDF-TrFE) $/$ BZT) as a hybrid gate dielectric for thin film transistors (TFTs). The experiment results show that the solution-processed BZT is amorphous and has no obvious ferroelectricity. However, stacking the BZT and the P(VDF-TrFE) can effectively reduce the leakage current due to the improvement of the thin film interface quality. To further understand the memory properties, endurance and retention reliability tests were conducted. The devices exhibit good memory characteristics at a low operating voltage of ±10 V and a large storage window of 7 V.

We enhanced the device uniformity for reliable memory performances by increasing the device surface roughness by exposing the HfO₂ thin film surface to argon (Ar) plasma. The results showed significant improvements in electrical and synaptic properties, including memory window, linearity, pattern recognition accuracy, and synaptic weight modulations. Furthermore, we proposed a non-identical pulse waveform for further improvement in linearity accuracy. From the simulation results, the Ar plasma processing device using the designed waveform as the input signals significantly improved the off-chip training and inference accuracy, achieving 96.3% training accuracy and 97.1% inference accuracy in only 10 training cycles.

Magnesium zirconate titanate (MZT) thin films, used as a sensing layer on Al interdigitated electrodes prepared using a sol–gel spin-coating method, are demonstrated in this study. The p-type MZT/Al/SiO₂/Si structure for sensing NO₂ is also discussed. The results indicated that the best sensitivity of the gas sensor occurred when it was operating at a temperature ranging from 100 to 150 °C. The detection limit of the sensor was as low as 250 ppb. The sensitivity of the MZT thin film was 8.64% and 34.22% for 0.25 ppm and 5 ppm of NO₂ gas molecules at a working temperature of 150 °C, respectively. The gas sensor also exhibited high repeatability and selectivity for NO₂. The response values to 250, 500, 1000, 1500, 2000, 2500, and 5000 ppb NO2 at 150 °C were 8.64, 9.52, 12, 16.63, 20.3, 23, and 34.22%, respectively. Additionally, we observed a high sensing linearity in NO₂ gas molecules. These results indicate that MZT-based materials have potential applications for use as gas sensors.