Reliability aspects in resistively switching valence change memory cells

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

HRS Instability in Oxide-Based Bipolar Resistive Switching Cells

One of the key challenges in the reliability of valence change [valence change-based memory (VCM)] resistive switching random access memories (ReRAMs) is the short-term instability of the programed state. Due to read noise, program verify or shaping algorithms are ineffective and read current (or resistance) distributions always revert to their intrinsic statistics. In this work, we analyze the instability of the high resistive state (HRS) measured on ZrO 2 -based devices via Factorial Hidden Markov Models. The extracted current jumps are explained by distinct ionic jumps via physics-based kinetic Monte Carlo (KMC) models. The simulation results reveal jumps of oxygen vacancies from the densely packed filament (plug) region to a sparsely packed gap (disc) region as origin of the most critical, large current jumps. These findings are used to extend our compact model (JART v1b) by a read noise module. We demonstrate simulated HRS instability in excellent agreement with our experimental data. Whereas the KMC approach provides a physical understanding of the processes underlying the HRS instability, the compact model enables the simulation of read noise up to industrially relevant array scales

Impact of the Ohmic Electrode on the Endurance of Oxide-Based Resistive Switching Memory

As one of the key aspects in the reliability of redox-based resistive switching memories (ReRAMs), maximizing their endurance is of high relevance for industrial applications. The major limitation regarding endurance is considered the excessive generation of oxygen vacancies during cycling, which eventually leads to irreversible RESET failures. Thus, the endurance could be increased by using combinations of switching oxide and ohmic electrode (OE) metal that provides a high barrier for the generation of oxygen vacancies [defect formation energy (DFE)]. In this work, we present a sophisticated programming algorithm that aims to maximize the endurance within reasonable measurement time. Using this algorithm, we compare ReRAM devices with four different OE metals and confirm the theoretically predicted trend. Thus, our work provides valuable information for device engineering toward higher endurance

Memristive Devices for Time Domain Compute-in-Memory

Analog compute schemes and compute-in-memory (CIM) have emerged in an effort to reduce the increasing power hunger of convolutional neural networks (CNNs), which exceeds the constraints of edge devices. Memristive device types are a relatively new offering with interesting opportunities for unexplored circuit concepts. In this work, the use of memristive devices in cascaded time-domain CIM (TDCIM) is introduced with the primary goal of reducing the size of fully unrolled architectures. The different effects influencing the determinism in memristive devices are outlined together with reliability concerns. Architectures for binary as well as multibit multiply and accumulate (MAC) cells are presented and evaluated. As more involved circuits offer more accurate compute result, a tradeoff between design effort and accuracy comes into the picture. To further evaluate this tradeoff, the impact of variations on overall compute accuracy is discussed. The presented cells reach an energy/OP of 0.23 fJ at a size of $1.2~{\mu{ }}\text{m}^{2}$ for binary and 6.04 fJ at $3.2~\mu \text{m}^{2}$ for $4\times 4$ bit MAC operations