Strontium titanate nickelate (STN) thin films on indium tin oxide (ITO)/glass substrate were synthesized using the sol-gel method for resistive random access memory (RRAM) applications. Aluminum (Al), titanium (Ti), tungsten (W), gold (Au) and platinum (Pt) were used as top electrodes in the STN-based RRAM to probe the switching behavior. The bipolar resistive switching behavior of the set and reset voltages is in opposite bias in the Al/STN/ITO and Pt/STN/ITO RRAMs, which can be partly ascribed to the different work functions of top electrodes in the ITO. Analyses of the fitting results and temperature-dependent performances showed that the Al/STN/ITO switching was mainly attributed to the absorption/release of oxygen-based functional groups, whereas the Pt/STN/ITO switching can be associated with the diffusion of metal electrode ions. The Al/STN/ITO RRAM demonstrated a high resistance ratio of >10⁶ between the high-resistance state (HRS) and the low-resistance state (LRS), as well as a retention ability of >10⁵ s. Furthermore, the Pt/STN/ITO RRAM displayed a HRS/LRS resistance ratio of >10³ and a retention ability of >10⁵ s.

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 study presents gallium-doped zinc oxide (ZnO:Ga, GZO) thin films. GZO thin films with both high transparency and low sheet resistance were prepared by RF sputtering and then post-annealed under nitrogen and hydrogen forming gas. With post-annealing at 450 °C, the proposed films with a film thickness of 100 nm showed high transparency (94%), while the sheet resistance of the films was reduced to 29 Ω/square, which was comparable with the performances of commercial indium tin oxide (ITO) samples. Post-annealing under nitrogen and hydrogen forming gas enhanced the films’ conductivity while altering the thin-film composition and crystallinity. Nitrogen gas played a role in improving the crystallinity while maintaining the oxygen vacancy of the proposed films, whereas hydrogen did not dope into the thin film, thus maintaining its transparency. Furthermore, hydrogen lowered the resistance of GZO thin films during the annealing process. Then, the detailed mechanisms were discussed. Hydrogen post-annealing helped in the removal of oxygen, therefore increasing the Ga³⁺ content, which provided extra electrons to lower the resistivity of the films. After the preferable nitrogen/hydrogen forming gas treatment, our proposed films maintained high transparency and low sheet resistance, thus being highly useful for further opto-electronic applications.

This study investigates the preparation and electrical properties of Al/cobalt-embedded gelatin (CoG)/ indium tin oxide (ITO) resistive switching memories. Co. elements can be uniformly distributed in gelatin without a conventional dispersion procedure, as confirmed through energy dispersive X-ray analyzer and X-ray photoelectron spectroscopy observations. With an appropriate Co. concentration, Co. ions can assist the formation of an interfacial AlO_x layer and improve the memory properties. High ON/OFF ratio, good retention capability, and good endurance switching cycles are demonstrated with 1 M Co. concentration, in contrast to 0.5 M and 2 M memory devices. This result can be attributed to the suitable thickness of the interfacial AlO_x layer, which acts as an oxygen reservoir and stores and releases oxygen during switching. The Co. element in a solution-processed gelatin matrix has high potential for bio-electronic applications.

A one-transistor and one-resistor (1T1R) architecture with a resistive random access memory (RRAM) cell connected to an organic thin-film transistor (OTFT) device is successfully demonstrated to avoid the cross-talk issues of only one RRAM cell. The OTFT device, which uses barium zirconate nickelate (BZN) as a dielectric layer, exhibits favorable electrical properties, such as a high field-effect mobility of 2.5 cm²/Vs, low threshold voltage of −2.8 V, and low leakage current of 10⁻¹² A, for a driver in the 1T1R operation scheme. The 1T1R architecture with a TiO₂-based RRAM cell connected with a BZN OTFT device indicates a low operation current (10 μA) and reliable data retention (over ten years). This favorable performance of the 1T1R device can be attributed to the additional barrier heights introduced by using Ni (II) acetylacetone as a substitute for acetylacetone, and the relatively low leakage current of a BZN dielectric layer. The proposed 1T1R device with low leakage current OTFT and excellent uniform resistance distribution of RRAM exhibits a good potential for use in practical low-power electronic applications.

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.