For over 50 years, Moore‘s law functioned as road map for advancements in the semiconductor industry. Soon, the predicted exponential increase in the number of devices per microchip will reach physical limitations. In order to overcome these limitations, redox-based resistive switching random access memory (ReRAM) is discussed as promising candidate for future memory applications. Recently, also a potential application of ReRAM in neuro-inspired architectures is gaining a lot of attention. Among other approaches, valence change based memory (VCM) is studied intensively. Regardless of an application as classical memory or as neuronal network component, the reliability of ReRAM devices is the key attribute for industrial adaption. This dissertation addresses the three main components of the reliability VCM ReRAM devices, being variability, retention and endurance. Here, VCM ReRAM cells based on ZrO2 fabricated under laboratory conditions are characterized as well as industrial devices based on HfO2 as switching oxide. Throughout this work, a focus on large arrays instead of single cells is emphasized. The evaluation and interpretation is focused on the internal statistics rather than on the behavior of individual devices. The variability of VCM ReRAM remains one of the largest challenges for their large scale adaption in industrial applications. Whereas the stochastic nature of the switching process can be significantly reduced by appropriate programming algorithms, random fluctuations occur also between read operations. This read to read (R2R) variability is identified as key challenge in the short term stability of VCM ReRAM. It determines the intrinsic statistics of large memory arrays and effectively limits the read window between the low resistive (LRS) and high resistive state (HRS). The random R2R fluctuations are attributed to random jumps of the conduction supporting oxygen vacancies. In the HRS, these jumps lead to a log-normal read current distribution. Via an empirical model as well as kinetic Monte Carlo (KMC) methods, the most likely origin of these statistics is found to be tunneling across a normally distributed gap in HRS. Here, the exponential dependence of the read current on the tunneling gap results in the observed log-normal statistics. Investigating the long term stability or retention, the R2R variability remains a key characteristic of the investigated devices. The most critical aspect of the long term degradation of a programmed state is found to be a broadening of the whole distribution, i.e. increasing variability. The trend of the degradation is fitted by an empirical tunneling model which allows for extrapolation of data measured at higher temperatures towards the target retention time at lower operating temperature. Additionally, a statistical model based on the work of Abbaspour et al. is developed which explains the observed degradation by diffusion of oxygen vacancies from a confined filament region towards the active electrode. Finally, an algorithm is developed which increases the number of possible switching cycles, also referred to as endurance, of a device. It dynamically adjusts the programming parameters to ensure reliable switching. Since the frequency of applied adjustments determines the speed of the experiment, the algorithm dynamically adjusts this frequency to the tested cell. It therefore increases the measurement speed if a cell requires less adjustments. The algorithm is used to determine the maximum endurance for different material combinations. Thus, it is demonstrated that ohmic electrode metals with lower oxygen chemical potential ensure higher endurance which verifies the theoretical findings of Guo et al. All in all, this dissertation proposes to evaluate the reliability of VCM ReRAM for its intrinsic statistics rather than tracing single cells
